Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis

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Its diagnosis remains largely dependent on clinical expertise, leading to variability and limited diagnostic accuracy, particularly among general practitioners. This study aimed to develop and evaluate a multimodal artificial intelligence (AI) model that integrates lesion image analysis and structured anamnesis to improve AD diagnosis. Methods This diagnostic study was conducted in two phases: Phase 1 used retrospective data from 2021–2024, and Phase 2 involved prospective external validation from multiple hospitals in 2025. Patients with AD or related skin conditions were included, with diagnoses based on AAD 2014 criteria. Multimodal fusion combined ResNet50-extracted image features and MPNet-based anamnesis text features using a late fusion model. This approach mimics clinical reasoning by integrating visual and contextual clinical information to classify cases as AD or non-AD. Results and Discussion The multimodal AI model integrating ResNet50 (image) and MPNet (anamnesis) achieved 98.28% accuracy in classifying AD vs non-AD, outperforming image-or text-only models. It offers clinical advantages by mimicking physician reasoning, improving diagnostic consistency, reducing subjectivity, and enabling mass triage. However, real-world generalizability remains a challenge due to limited training diversity, potential language constraints (Bahasa Indonesia), and narrow differential diagnoses. External validation and explainable AI (XAI) are critical for broader application. Despite limitations, the model aligns with emerging literature, showing multimodal AI can approach or surpass expert-level performance in dermatological diagnosis when rigorously validated. Conclusions The multimodal ResNet50-MPNet model shows near-perfect accuracy in diagnosing AD by mimicking clinician reasoning. It offers consistent, holistic assessment but requires external validation and improved interpretability for clinical adoption. Continued AI-clinician collaboration is vital to translating this promising technology into real-world dermatological care. " } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/14-952/v1", "name": "Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis" } } ] } Home Browse Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Widiawaty A, Indriatmi W, Jatmiko W et al. Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.12688/f1000research.169102.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] Alida Widiawaty https://orcid.org/0009-0002-6325-0843 1,2 , Wresti Indriatmi 2,3 , Wisnu Jatmiko 4 , [...] Endi Novianto 3 , Aria Kekalih 5 , Hendra Gunawan 6 , Pramudita Satria Palar 7 , Muhammad Febrian Rachmadi 4 , Sherly Dermawan https://orcid.org/0009-0004-8530-662X 1 , Tengku Laras Malahayati 4 , Alif Wicaksana Ramadhan 4 Alida Widiawaty https://orcid.org/0009-0002-6325-0843 1,2 , Wresti Indriatmi 2,3 , [...] Wisnu Jatmiko 4 , Endi Novianto 3 , Aria Kekalih 5 , Hendra Gunawan 6 , Pramudita Satria Palar 7 , Muhammad Febrian Rachmadi 4 , Sherly Dermawan https://orcid.org/0009-0004-8530-662X 1 , Tengku Laras Malahayati 4 , Alif Wicaksana Ramadhan 4 PUBLISHED 19 Sep 2025 Author details Author details 1 Faculty of Medicine, Universitas Riau, Pekanbaru, Riau, Indonesia 2 Medical Science, Doctoral Study Program, Universitas Indonesia, Jakarta Pusat, Jakarta, Indonesia 3 Department of Dermatology and Venereology, Universitas Indonesia, Jakarta Pusat, Jakarta, Indonesia 4 Faculty of Computer Science, Universitas Indonesia, Depok, West Java, Indonesia 5 Occupational Medicine, Universitas Indonesia, Jakarta Pusat, Jakarta, Indonesia 6 Department of Dermatology and Venereology, Universitas Padjadjaran, Bandung, West Java, Indonesia 7 Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Bandung, West Java, Indonesia Alida Widiawaty Roles: Conceptualization, Methodology, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing Wresti Indriatmi Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Wisnu Jatmiko Roles: Writing – Original Draft Preparation, Writing – Review & Editing Endi Novianto Roles: Resources, Writing – Original Draft Preparation, Writing – Review & Editing Aria Kekalih Roles: Methodology, Writing – Review & Editing Hendra Gunawan Roles: Writing – Review & Editing Pramudita Satria Palar Roles: Writing – Review & Editing Muhammad Febrian Rachmadi Roles: Software Sherly Dermawan Roles: Writing – Review & Editing Tengku Laras Malahayati Roles: Software Alif Wicaksana Ramadhan Roles: Software OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Artificial Intelligence and Machine Learning gateway. Abstract Background Atopic dermatitis (AD) is a prevalent, chronic inflammatory skin disease with diverse clinical presentations, often overlapping with other dermatoses. Its diagnosis remains largely dependent on clinical expertise, leading to variability and limited diagnostic accuracy, particularly among general practitioners. This study aimed to develop and evaluate a multimodal artificial intelligence (AI) model that integrates lesion image analysis and structured anamnesis to improve AD diagnosis. Methods This diagnostic study was conducted in two phases: Phase 1 used retrospective data from 2021–2024, and Phase 2 involved prospective external validation from multiple hospitals in 2025. Patients with AD or related skin conditions were included, with diagnoses based on AAD 2014 criteria. Multimodal fusion combined ResNet50-extracted image features and MPNet-based anamnesis text features using a late fusion model. This approach mimics clinical reasoning by integrating visual and contextual clinical information to classify cases as AD or non-AD. Results and Discussion The multimodal AI model integrating ResNet50 (image) and MPNet (anamnesis) achieved 98.28% accuracy in classifying AD vs non-AD, outperforming image-or text-only models. It offers clinical advantages by mimicking physician reasoning, improving diagnostic consistency, reducing subjectivity, and enabling mass triage. However, real-world generalizability remains a challenge due to limited training diversity, potential language constraints (Bahasa Indonesia), and narrow differential diagnoses. External validation and explainable AI (XAI) are critical for broader application. Despite limitations, the model aligns with emerging literature, showing multimodal AI can approach or surpass expert-level performance in dermatological diagnosis when rigorously validated. Conclusions The multimodal ResNet50-MPNet model shows near-perfect accuracy in diagnosing AD by mimicking clinician reasoning. It offers consistent, holistic assessment but requires external validation and improved interpretability for clinical adoption. Continued AI-clinician collaboration is vital to translating this promising technology into real-world dermatological care. READ ALL READ LESS Keywords Atopic dermatitis, Multimodal Artificial Intelligence (AI), ResNet50, MPNet, Dermatology diagnosis, Clinical decision support, Machine learning, Explainable AI (XAI) Corresponding Author(s) Alida Widiawaty ( [email protected] ) Close Corresponding author: Alida Widiawaty Competing interests: No competing interests were disclosed. Grant information: This study was supported by the Indonesian Education Scholarship, the Center for Higher Education Funding and Assessment, and the Indonesian Endowment Fund for Education. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Widiawaty A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Widiawaty A, Indriatmi W, Jatmiko W et al. Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.12688/f1000research.169102.1 ) First published: 19 Sep 2025, 14 :952 ( https://doi.org/10.12688/f1000research.169102.1 ) Latest published: 09 Feb 2026, 14 :952 ( https://doi.org/10.12688/f1000research.169102.2 )  There is a newer version of this article available. Suppress this message for one day. Introduction Atopic dermatitis (AD) is a chronic and relapsing inflammatory skin disease frequently encountered in both children and adults. Its prevalence is estimated to be 15–20% in children and 10% in adults. 1 – 3 The onset typically occurs before the age of five, underscoring the importance of timely and accurate diagnosis to prevent complications and improve quality of life. 4 – 6 Clinically, AD is characterized by severe pruritus and xerosis, and is often associated with allergic comorbidities such as asthma and allergic rhinitis. 5 , 7 The complexity of its pathogenesis—encompassing genetic, immunologic, and environmental factors—along with diverse clinical presentations can hinder accurate diagnosis. 8 – 10 Morphologically, AD may mimic other dermatoses (e.g., psoriasis vulgaris, contact dermatitis, nummular dermatitis), leading to potential misdiagnosis if not thoroughly evaluated. 11 – 13 Another diagnostic challenge is the high inter-clinician variability. Diagnosis of AD currently relies on the clinician’s expertise through careful anamnesis and physical examination. This conventional approach is subjective, resulting in variable diagnostic accuracy, especially among general practitioners. Previous studies report diagnostic accuracy for skin diseases by general practitioners to range from 24% to 70%, markedly lower than that of dermatologists. 14 This variation in expertise and experience leads to diagnostic inconsistencies and may result in inappropriate or prolonged treatment. Even standardized severity scoring tools (e.g., SCORAD or EASI) show interobserver disparities due to the subjectivity in assessing certain clinical elements. This condition underscores the need for a more reliable and consistent diagnostic method for AD. 13 , 15 To address these diagnostic challenges, artificial intelligence (AI) presents a promising solution. Advances in AI, particularly deep learning (DL), offer new opportunities in dermatology to enhance diagnostic accuracy and consistency. Research on AI-based tools for diagnosing inflammatory skin diseases has demonstrated significant potential. 16 – 18 Various Convolutional Neural Network (CNN)-based algorithms have successfully recognized and classified skin lesions with high accuracy, comparable to that of dermatologists. 13 Wu et al. (2020) developed a DL model using EfficientNet-b4 to classify psoriasis and AD from lesion images, achieving accuracy, sensitivity, and specificity rates above 90%. Maron et al. (2019) showed that CNNs systematically outperformed 112 dermatologists in multi-class skin lesion classification, highlighting the potential of DL in dermatological diagnostics. 19 Dautovic et al. applied an artificial neural network (ANN) using nine clinical parameters to diagnose AD in both healthy individuals and AD patients. Yang et al. also utilized DL to recognize dermoscopic images of psoriasis and inflammatory diseases such as dermatitis, achieving a sensitivity of 73%. 20 Despite these promising results, several limitations persist—particularly the lack of clinical context. Most current models are trained on images alone, neglecting additional clinical information that could enhance diagnostic decision-making. 21 Such single-modality approaches risk overlooking important patient context that clinicians routinely consider in clinical practice. In fact, anamnesis and clinical information—such as patient age, chief complaints, history of atopy, lesion distribution, and family medical history—contain valuable insights that can aid in distinguishing AD from its differential diagnoses and provide a more comprehensive assessment of the patient’s condition. Fundamentally, physicians establish a diagnosis based on a combination of history taking and physical examination. The fact that only a few multimodal AI models are currently available in dermatology—especially for inflammatory skin diseases such as AD—reveals a significant research gap. Integrating clinical data into AI models has the potential to bridge this disparity, in line with the current medical trend of leveraging diverse patient data sources to enhance diagnostic accuracy. Multimodal AI approaches represent a highly promising avenue for achieving more comprehensive and accurate diagnoses of complex conditions such as AD (Yu et al., 2025). 13 , 22 , 23 This study aims to develop and evaluate an AI model with a multimodal approach, combining DL-based clinical image analysis with structured textual anamnesis using a transformer-based language model. Specifically, the architecture employs a 50-layer Residual Network (ResNet50) for image feature extraction and a Masked and Permuted Pre-training for Language Understanding (MPNet) for textual feature extraction. ResNet50 is a widely used CNN model with proven success in both medical and general image classification. 24 Its architecture introduces skip connections that help the network learn deep image features without losing critical information. In dermatology, ResNet50 has demonstrated high accuracy—for example, achieving 90% on the ISIC 2018 dataset and 95.8% on the PH2 dataset for skin disease detection. 25 MPNet is a transformer-based NLP model designed to produce rich contextual sentence representations. Combining the strengths of BERT and XLNet, MPNet effectively captures complex language patterns and generates high-dimensional text embeddings. In this study, MPNet processes structured anamnesis data (e.g., chief complaints, subjective symptoms, history of allergies, family medical history, and others) into numerical text features. The integration of MPNet ensures that relevant non-visual clinical information is appropriately incorporated into the model. Methods This diagnostic study was conducted in two phases. Phase 1 focused on the development of the AI model. This phase comprised several stages. The first stage involved the collection of medical data through retrospective review of hospital medical records from 2021 to 2024. Data included clinical information and skin lesion images of patients diagnosed with AD, psoriasis vulgaris (PV), chronic lichen simplex (CLS), nummular dermatitis (ND), and contact dermatitis (CD), as diagnosed by board-certified dermatologists. In the second stage, images were pre-labeled with relevant clinical information. The third stage involved training the machine learning (ML) model to identify discriminative features and characteristics that differentiate AD from non-AD ( Figure 4 ). Figure 1. ResNet50 training and validation accuracy image. Figure 2. F1 score image. Figure 3. Multimodal model algorithm flowchart. Figure 4. Research flow – Phase 1. Phase 2 was a multicenter validation study designed to evaluate the generalizability of the ML model trained in Phase 1. Clinical data and lesion images were collected from patients diagnosed with AD or non-AD skin conditions who visited board-certified dermatologists at various hospitals between January and May 2025. In this phase, dermatologists performed complete clinical interviews, and all clinical data were recorded using a structured form ( Figure 5 ). Figure 5. Research flow – Phase 2. In both Phases 1 and 2, skin lesion images were captured using mobile phones owned by the examining physicians. All participants formed a consecutive and provided written informed consent and agreed to the use and publication of their anonymized data, including medical history and lesion images. For minor participants, written consent was obtained from their legal guardians. Ethical approval was obtained from the Ethics Committee of the Faculty of Medicine, Universitas Indonesia–Cipto Mangunkusumo Hospital. The diagnosis of AD was established based on the criteria outlined in the American Academy of Dermatology (AAD) guidelines for the diagnosis and assessment of AD 2014. Two board-certified dermatologists independently annotated all images, achieving 100% agreement. The learning outcome was binary: AD or non-AD. All clinical images and patient data were archived systematically. The sample size was calculated using the formula for diagnostic studies, resulting in a minimum requirement of 346 participants for the AD group and 138 for the non-AD group. Multimodal fusion was implemented by integrating visual features from ResNet50 and textual features from MPNet prior to final prediction. A late fusion approach was used, in which feature vectors from both modalities were concatenated and passed through a classification layer to determine the diagnosis. This method leverages the strengths of both modalities: images provide morphological and distributional characteristics of lesions, while anamnesis text offers clinical context such as chronic pruritus, personal or family history of atopy, and comorbid asthma. The combined approach is expected to reach above 90% accuracy and emulate the way dermatologists diagnose—by integrating visual inspection with the patient’s clinical information. Results The characteristics of subjects with and without AD are presented in Table 1 . In Phase 1, a total of 926 AD samples and 697 non-AD samples were collected, while Phase 2 yielded 525 AD samples and 663 non-AD samples. The findings indicate that AD was most frequently observed in infants and children, accounting for 45.45% of cases in Phase 1 and 40% in Phase 2. Its prevalence declined with increasing age, with only 0.21% of cases occurring in individuals over 65 years old. These results are consistent with the known epidemiology of AD, which typically begins in childhood or adolescence. AD most commonly manifests in infancy and early childhood, with approximately 60% of cases developing before the age of one, and nearly all cases occurring before the age of five. 26 Table 1. Demographic and clinical characteristics of phase 1 and phase 2 study subjects. Characteristics Phase 1 Phase 2 AD Non-AD AD Non-AD n % n % n % n % Age Group Toddler: 0–5 years 189 20.41 0 0 69 13.14 3 0.45 Child: 5–11 years 125 13.50 4 0.57 98 18.67 32 4.83 Early adolescent: 12–16 years 107 11.56 20 2.87 43 8.19 11 1.66 Late adolescent: 17–25 years 199 21.49 60 8.61 89 16.95 86 12.97 Young adult: 26–35 years 107 11.56 192 27.54 77 14.67 128 19.31 Middle-aged adult: 36–45 years 109 11.77 115 16.50 67 12.76 84 12.67 Early elderly: 46–55 years 73 7.89 232 33.29 41 7.81 142 21.42 Late elderly: 56–65 years 15 1.61 74 10.62 12 2.29 109 16.44 Senior: > 65 years 2 0.21 0 0 29 5.52 68 10.26 Sex Male 460 49.68 256 36.73 242 46.10 313 47.21 Female 466 50.32 441 63.27 283 53.90 350 52.79 Duration of illness 1 week 31 3.35 16 2.30 18 3.43 58 8.75 2 weeks 15 1.62 0 0 38 7.24 50 7.54 3 weeks 0 0 2 0.29 0 0 0 0 4 weeks 34 3.67 53 7.60 23 4.38 14 2.11 > 4 weeks 846 91.36 626 89.81 446 84.95 541 81.6 Image The dataset used in this study consists of images of skin lesions from patients diagnosed with AD as well as patients suffering from other skin conditions whose lesions visually resemble those found in AD cases. To perform a supervised classification task, we categorized the images into two distinct classes: AD (representing AD lesions) and Non-AD (representing non-AD lesions). In order to enhance model generalization and robustness, we applied extensive data augmentation prior to training. Each original image was augmented five times using the Albumentations library to produce image sets with various transformations. These included horizontal and vertical flipping, random adjustments to brightness and contrast, rotations of up to 30 degrees, mild Gaussian blurring, and the addition of Gaussian noise. After augmentation, all images were resized to a standardized dimension of 224 × 224 pixels. Furthermore, pixel values were normalized using the ImageNet mean [0.485, 0.456, 0.406] and standard deviation [0.229, 0.224, 0.225] to match the input expectations of the pretrained models. For the classification task, we evaluated two different DL architectures: ResNet50 and Vision Transformer (ViT). Each model’s final classification layer was adapted to produce outputs corresponding to the two target classes, AD and Non-AD. Training was conducted using the PyTorch DL framework, which is widely used in the field of computer vision research due to its flexibility and high performance. Since the dataset used in this study is relatively small in scale, which is a common situation in the medical field where obtaining labeled images can be challenging, it becomes crucial to adopt an evaluation strategy that maximizes the use of available data while still providing a reliable estimate of the model performance. To address this, we employed the use of K-fold Cross Validation. Rather than splitting the dataset into a single training and testing group, the cross-validation approach divides the dataset into ten equal parts, or “folds.” In each round of training, the model is trained using nine of these folds and evaluated on the remaining one. This process is repeated ten times, with each fold taking a turn as the validation set. At the end of this procedure, the results from all ten rounds are averaged to provide a comprehensive assessment of the model’s performance. This technique reduces the risk of the evaluation being biased by the specific choice of training or testing data and is particularly important when dealing with medical datasets, where the number of samples can be limited. It offers a favorable balance between model accuracy and computational efficiency. During training, we also implemented an optimization algorithm called Adam, which automatically adjusts the learning rate during training to improve the model’s ability to learn from data. To further refine the training process, we applied a strategy called learning rate scheduling, where the learning rate was reduced gradually over time to allow the model to converge more smoothly. In addition, we implemented early stopping, a technique where the training process is halted if the model’s performance on validation data does not improve after a few consecutive training rounds. This prevents the model from overfitting, or memorizing the training data too much, and helps ensure that it learns patterns that are generalizable to unseen data. Throughout the training and evaluation process, we monitored several key metrics to assess model performance. These included accuracy, which measures how often the model’s predictions matched the correct labels; precision, which indicates how many of the predicted DA cases were actually correct; recall, which measures how many actual DA cases the model successfully identified; and F1-score, which balances precision and recall into a single number to give an overall sense of the model’s effectiveness. By considering these multiple aspects, we aimed to evaluate not just whether the model made correct predictions overall, but also whether it was good at correctly identifying cases of AD without missing too many or falsely predicting AD where there was none. After completing the full training and evaluation pipeline, ResNet50 emerged as the best-performing model among the three architectures tested, achieving an accuracy score of 0.8750, compared to 0.6013 for the ViT. ResNet50 consistently demonstrated superior performance across all key metrics, including higher accuracy, precision, recall, and F1-score when compared to the other model ( Table 2 ). In particular, ResNet50 exhibited strong stability during training and converged more rapidly. These results suggest that convolutional architectures with residual connections, such as ResNet, are particularly well-suited for medical image classification tasks such as skin lesion analysis, where fine-grained texture analysis is the highlight. Table 2. Key performance metrics of Resnet50 vs ViT. Model Accuracy Precision Recall F1-Score Resnet50 0.8750 0.8809 0.8750 0.8728 ViT 0.6013 0.5809 0.6013 0.5487 Text The dataset used in this study consists of anamnesis text of patients whose diagnosed with AD as well as patients suffering from other skin conditions whose lesions visually resemble those found in AD cases. Clinical text inputs were derived from dermatologist notes within medical records during Phase 1, and from patient anamnesis recorded in structured forms during Phase 2. The text in the dataset are written in Bahasa Indonesia. To perform a supervised classification task, we categorized the anamnesis into two classes, the AD and Non-AD. The anamnesis contains several parameters such as patient age, gender, symptoms, contact history, etc. Those parameters were then configured to follows a template as follows: Pasien dengan jenis kelamin: usia: klasifikasi usia: Keluhan utama dan onset: Riwayat kontak dengan bahan alergen atau iritan: Sumber infeksi: Faktor pencetus penyakit saat ini: Lama sakit: Lokasi lesi: Kriteria mayor: Kriteria minor: Riwayat penyakit dahulu: Riwayat penyakit keluarga: By generating text following this template, we obtained a string as the model input to be classified by the proposed method. After we obtain the full text as model input, we need to do pre-processing to the text before feeding it to the model. In this text processing task, the preprocessing part was done by using a tokenizer. A tokenizer is a tool or process used in natural language processing (NLP) to break down a text into smaller units, called tokens. These tokens can be words, subwords, or characters, depending on the tokenization method used. The purpose of tokenization is to transform raw text into a more manageable and structured form that can be analyzed or fed into ML models. In this study we used a pre-trained model, MPNet which is a state-of-the-art transformer model that was introduced as a variant of BERT (Bidirectional Encoder Representations from Transformers) and other transformer-based models like RoBERTa and ALBERT. MPNet improves upon BERT’s pretraining strategy by using a more sophisticated approach for handling masked tokens and learning better dependencies between tokens in a sequence. The tokenizer used in MPNet is based on the WordPiece tokenization method, which is also used in BERT and similar transformer models. The output of this MPNet pre-trained model is 768 sized vectors. To do classification task, we added some dense networks downstream the MPNet architecture and formed a single output classification. The model was created using PyTorch DL framework, which is widely used in the field of computer vision research due to its flexibility and high performance. For the training process, we splitted the dataset into training and validation parts with a ratio of 8:2. After the training process, the fine-tuned MPNet model obtained an accuracy of 100% in both validation and training data. Multimodal The multimodal approach in this study integrates two powerful models: ResNet50 for visual data and MPNet for textual data, to enhance classification performance by leveraging both image and text information. ResNet50, a deep CNN, is employed to process the images of skin lesions. These images are first preprocessed through augmentation, resizing, and normalization, ensuring compatibility with the input requirements of ResNet50. The model then generates a feature vector from the image, capturing the essential visual characteristics of the skin lesion, which represents the image data for subsequent processing. In parallel, MPNet, a transformer-based model, is applied to the textual data consisting of patients’ anamnesis information. MPNet tokenizes the text and generates a fixed-size vector (768-dimensional), which encodes the relationships and context between the words. This representation is essential for understanding the nuanced medical history of the patients, such as symptoms, age, and other relevant factors. MPNet’s ability to model contextual dependencies and generate more accurate token representations provides an edge over traditional models like BERT, especially when dealing with complex and diverse medical texts. Once both ResNet50 and MPNet have processed their respective modalities and produced their vectorized outputs, the next step is to combine these two vectors into a single unified feature representation. This fusion of image and text data ensures that the model is utilizing all available information, combining the fine-grained visual analysis from the images with the rich, contextual information from the text. The feature vectors from both modalities are concatenated, creating a comprehensive representation that encapsulates the full scope of the data. Finally, to refine the combined feature vector and prepare it for classification, several dense layers are applied downstream. These dense layers help the model learn the most relevant patterns from the multimodal data. The final output from the dense layers is a classification label that distinguishes between the two target classes: AD and Non-AD. This multimodal approach effectively leverages both image and text data, improving the model’s robustness and accuracy by considering multiple facets of the data simultaneously, which is crucial in medical classification tasks. Clinical implications, strengths, and limitations of the multimodal AI model The image-only model achieved an accuracy of 87.50% on the training set and 90.13% on the test set ( Figure 1 ). Key performance metrics, including accuracy, precision, recall, and F1 score are presented in Figure 2 and Table 2 . The text-only model achieved 100% accuracy on both the training and validation sets. Following the fusion of both modalities, the overall accuracy increased significantly to 98.28% ( Figure 3 ). The multimodal AI approach employed in this study offers several advantages. First, the AI model can consistently analyze hundreds of microscopic visual features and textual patterns without fatigue, enabling the potential for high accuracy and sensitivity across diverse cases. In fact, numerous studies have demonstrated that AI performance in dermatological diagnosis can match or even surpass that of dermatology specialists for certain specific tasks. 27 For instance, a recent meta-analysis by Salinas et al. (2024) on skin cancer diagnosis reported AI algorithms achieving a sensitivity of 87% and specificity of 77%, comparable to expert dermatologists (sensitivity 84%, specificity 74%). 28 In the context of AD, the ResNet50 model, when specifically trained, has outperformed conventional models and approached expert-level performance in assessing AD severity. 29 By integrating anamnesis data, AI can also account for factors typically gathered through patient interviews, thereby allowing for more clinically informed decision-making. AI systems can also process multiple cases simultaneously, supporting mass screening or triage workflows, thereby allowing physicians to prioritize complex cases. Moreover, multimodal AI reduces subjectivity: decisions are derived from patterns learned across hundreds of examples, rather than individual intuition, which can vary among physicians. With a reported accuracy of 98.28%, this system has strong potential as a reliable diagnostic aid, offering second opinions to clinicians and enhancing general practitioners’ confidence in correctly identifying AD. 30 Nevertheless, several important considerations must be addressed. First, the model’s outstanding performance in a controlled setting may not necessarily reflect its effectiveness in real-world clinical practice. Generalizability remains a common challenge in AI models, as they may learn patterns specific to a particular hospital’s dataset, potentially resulting in reduced accuracy when applied to different patient populations or images captured with other camera devices. While 10-fold cross-validation provides a comprehensive internal evaluation, external validation using data from independent clinics is essential to ensure the model is not overfitting. The high accuracy of 98.28% raises a concern that the model may have been overtrained on a limited dataset. In this study, external validation was conducted using datasets from multiple hospitals to ensure that the model maintains reliable and consistent performance. In future work, explainable AI (XAI) techniques could be employed to enhance interpretability, allowing the model’s decision-making process to be visually explained and verified against clinical reasoning. 31 , 32 Another key issue is interpretability. Although AI can achieve high levels of accuracy, it remains limited in its ability to explain the rationale behind a given prediction. Physicians, by contrast, can justify a diagnosis based on observable clinical features (e.g., “there is excoriation due to intense itching, a typical distribution pattern on the infant’s cheeks, and a positive family history of atopy, consistent with AD”). AI models require XAI techniques to offer similar justifications. Without such interpretability, trust in AI-generated recommendations—both among physicians and patients—may be diminished. Moreover, broader clinical context is often necessary for final decision-making. Physicians may rely on direct physical examination (e.g., palpating the skin texture), additional diagnostic tests, or further clinical history. AI models, on the other hand, can only process the limited input they are provided (i.e., images and structured textual data), making their reasoning less flexible than that of physicians. Clinicians can pose follow-up questions, revise diagnostic hypotheses in real time, and manage atypical or complex cases not represented in the training data—for example, a patient presenting with two concurrent skin conditions. Finally, physicians possess critical strengths in empathy and clinical ethics—for example, delivering a diagnosis and treatment plan in a psychologically appropriate and compassionate manner. This human element lies beyond the scope of AI models. Therefore, AI should be viewed as a complementary tool that supports physicians, rather than as a full replacement. AI is intended to serve as a decision support system, not a substitute for clinical judgment. Collaborative use of AI and physicians has been shown to improve diagnostic accuracy compared to either working alone. 33 In practice, AI can provide a rapid second opinion, while the final decision and any necessary contextual adjustments remain the responsibility of the physician. Discussion Accuracy of 98.28% and its relevance in current literature The final accuracy of 98.28% achieved by the multimodal ResNet50 and MPNet model in distinguishing between AD and non-AD cases represents an exceptionally high performance. To assess its relevance, this result must be compared with recent studies in the field of computer-aided dermatological diagnosis. In general, AI models for skin lesion classification typically achieve accuracies above 80–90%. For example, Wu et al., (2020) developed and validated an image-based DL model, AIDDA, using the EfficientNet-b4 convolutional neural network architecture to automate the diagnosis of common inflammatory skin diseases, including psoriasis, eczema, and AD. Trained on a dataset of 4,740 expert-labeled clinical images, the model achieved high diagnostic performance, with an overall accuracy of 95.8%, sensitivity of 94.4%, and specificity of 97.2%. Therefore, the 98.28% accuracy achieved in this study warrants attention as a highly promising result. It highlights the potential advantage of a multimodal approach. By incorporating additional clinical data, the model may resolve ambiguities that remain when relying on image analysis alone, allowing for near-perfect classification across samples. Other multimodal studies have also reported strong performance. Adebiyi et al. (2024), for instance, integrated lesion images with patient metadata and achieved an area under the curve (AUC) of 0.94 (94%) on the HAM10000 dataset—an improvement over image-only models. 34 In addition, recent developments in transformer-based fusion approaches, such as TFormer, have been specifically designed to deeply integrate multimodal information and have demonstrated enhanced diagnostic accuracy across various types of skin lesions. 17 Nevertheless, the scientific community emphasizes the necessity of validating AI models in real-world clinical scenarios. Burlando et al. (2024), in a recent meta-analysis, stressed the importance of external validation across diverse populations and collaborative decision-making between AI and clinicians. 28 Near-perfect accuracy may indicate model excellence, but also raises the concern of potential data leakage or bias. For instance, if structured anamnesis texts contain inadvertently predictive features (e.g., specific keywords unique to AD cases), the model might overfit to non-clinical artifacts. Thus, transparent methodological reporting—such as details on 10-fold cross-validation and feature importance analyses—is essential for interpreting these results responsibly. The 98.28% accuracy is consistent with previous studies reporting that advanced DL models, particularly those utilizing multimodal inputs, can achieve diagnostic performance comparable to or exceeding that of experienced clinicians in specific tasks. 19 This parallels recent innovations in digital dermatology, such as the PanDerm foundation model, trained on millions of multimodal images, which outperformed dermatologists in early melanoma detection. 35 Similarly, generative models like SkinGPT-4 (2024) now integrate ViT with large language models to generate diagnostic reports that approximate physician reasoning. 36 In this context, our findings contribute compelling evidence that multimodal AI has the potential to reshape dermatological diagnostics, provided that it is demonstrated to be consistent in larger-scale studies. Several limitations warrant consideration. First, the model was trained on anamnesis written in Bahasa Indonesia using a language-specific version of MPNet. Although MPNet has multilingual variants, the one applied here may not generalize effectively to English or other languages. Moreover, differences in documentation style (e.g., brief vs. narrative notes) could also impact model performance. Future implementations should consider language adaptation or employ inherently multilingual models for broader applicability. 37 Second, the differential diagnosis in the present study was restricted to four conditions: lichen simplex chronicus, psoriasis vulgaris, nummular dermatitis, and contact dermatitis. In clinical reality, AD mimickers may also include conditions such as scabies, fungal infections, or seborrheic dermatitis, which were not represented in the training data. This raises concerns about generalizability when the model is applied to unfamiliar cases. Expanding the dataset to encompass a broader spectrum of dermatoses, or transitioning to a multiclass classification model, could improve both utility and clinical relevance. Third, real-world deployment of this system would require regulatory approval, robust documentation of safety and reliability, and integration with electronic health records to ensure seamless clinical workflow. This would necessitate interdisciplinary collaboration among clinicians, engineers, and policy makers. However, to successfully implement AI, key barriers need to be addressed. Efforts should focus on ensuring algorithm transparency and adequate regulation of algorithms. Simultaneously, improving knowledge about AI could reduce the fear of replacement. Conclusion The multimodal approach combining ResNet50 and MPNet represents a breakthrough in the automated diagnosis of AD. By integrating image and textual data, the model is able to emulate a holistic diagnostic approach similar to that of clinicians, as demonstrated by its near-perfect internal accuracy. Compared to baseline physician assessments, this model offers the potential for greater consistency in diagnosis. However, its adoption in clinical practice must be supported by robust external validation and improved interpretability to gain trust within the medical community. The reported 98.28% accuracy reflects the promise of multimodal AI in dermatology, echoing broader trends in the field toward increasingly sophisticated diagnostic systems. Nonetheless, it is essential to remain critical of the limitations and challenges involved in real-world implementation. With continued advancement in AI research, collaboration between AI and clinicians holds great promise for improving diagnostic accuracy and the overall quality of care, particularly for patients with AD and other dermatologic conditions. 38 , 39 Ethical considerations Ethical approval for the study was obtained from the Health Research Ethics Committee, Faculty of Medicine, Universitas Indonesia–Cipto Mangunkusumo Hospital (certificate number KET-1477/UN2.F1/ETIK/PPM.00.02/2024). All participants provided written informed consent prior to data collection. For minor participants, written consent was obtained from their legal guardians. Ethical approval was obtained from the Ethics Committee of the Faculty of Medicine, Universitas Indonesia–Cipto Mangunkusumo Hospital. Patient data and images were anonymized before use, and no identifiable information was included in the analysis. This diagnostic study did not involve any interventional procedures. Reporting guidelines Figshare: STARD checklist for “ Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis ” https://doi.org/10.6084/m9.figshare.29925533 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability Source code available from: • https://github.com/AlidaWidiawaty/multimodal-skin-lesion-classification 41 • https://github.com/AlidaWidiawaty/multimodal-dermatitis-classification-anamnesys 42 Archived software availabile from: • https://doi.org/10.5281/zenodo.16983448 43 • https://doi.org/10.5281/zenodo.16983460 44 License: MIT License Data availability Underlying data Figshare: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis . 40 https://doi.org/10.6084/m9.figshare.29925533 The project contains the following underlying data: • Clinical data of AD and non-AD research samples from phases 1 and 2.xlsx. Extended data Figshare: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis . 40 https://doi.org/10.6084/m9.figshare.29925533 The project contains the following underlying data: • Questionnaires in the form of a Google Form containing clinical data of AD and non-AD patients • Translated and signed informed consent forms obtained from participants prior to lesion photography • Tables and Figures Acknowledgements The authors would like to thank the board-certified dermatologists, participating hospitals, and patients for their contributions. References 1. Langan SM, Mulick AR, Rutter CE, et al. : Trends in eczema prevalence in children and adolescents: A Global Asthma Network Phase I Study. Clin Exp Allergy. 2023 Mar; 53 (3): 337–352. Publisher Full Text 2. Hadi HA, Tarmizi AI, Khalid KA, et al. : The Epidemiology and Global Burden of Atopic Dermatitis: A Narrative Review. Life (Basel). 2021 Sept 9; 11 (9): 936. Publisher Full Text 3. 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Reference Source Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 19 Sep 2025 ADD YOUR COMMENT Comment Author details Author details 1 Faculty of Medicine, Universitas Riau, Pekanbaru, Riau, Indonesia 2 Medical Science, Doctoral Study Program, Universitas Indonesia, Jakarta Pusat, Jakarta, Indonesia 3 Department of Dermatology and Venereology, Universitas Indonesia, Jakarta Pusat, Jakarta, Indonesia 4 Faculty of Computer Science, Universitas Indonesia, Depok, West Java, Indonesia 5 Occupational Medicine, Universitas Indonesia, Jakarta Pusat, Jakarta, Indonesia 6 Department of Dermatology and Venereology, Universitas Padjadjaran, Bandung, West Java, Indonesia 7 Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Bandung, West Java, Indonesia Alida Widiawaty Roles: Conceptualization, Methodology, Resources, Software, Writing – Original Draft Preparation, Writing – Review & Editing Wresti Indriatmi Roles: Conceptualization, Writing – Original Draft Preparation, Writing – Review & Editing Wisnu Jatmiko Roles: Writing – Original Draft Preparation, Writing – Review & Editing Endi Novianto Roles: Resources, Writing – Original Draft Preparation, Writing – Review & Editing Aria Kekalih Roles: Methodology, Writing – Review & Editing Hendra Gunawan Roles: Writing – Review & Editing Pramudita Satria Palar Roles: Writing – Review & Editing Muhammad Febrian Rachmadi Roles: Software Sherly Dermawan Roles: Writing – Review & Editing Tengku Laras Malahayati Roles: Software Alif Wicaksana Ramadhan Roles: Software Competing interests No competing interests were disclosed. Grant information This study was supported by the Indonesian Education Scholarship, the Center for Higher Education Funding and Assessment, and the Indonesian Endowment Fund for Education. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 09 Feb 2026, 14:952 https://doi.org/10.12688/f1000research.169102.2 version 1 Published: 19 Sep 2025, 14:952 https://doi.org/10.12688/f1000research.169102.1 Copyright © 2025 Widiawaty A et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Widiawaty A, Indriatmi W, Jatmiko W et al. Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.12688/f1000research.169102.1 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 1 VERSION 1 PUBLISHED 19 Sep 2025 Views 0 Cite How to cite this report: Krzywicki T. Reviewer Report For: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.5256/f1000research.186388.r446710 ) The direct URL for this report is: https://f1000research.com/articles/14-952/v1#referee-response-446710 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Jan 2026 Tomasz Krzywicki , University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.186388.r446710 The article concerns the proposal of a solution in the form of a multimodal neural network model supporting skin diagnostics by aggregating clinical information from photos and texts. The idea of aggregation of many modalities in modelling a clinical ... Continue reading READ ALL The article concerns the proposal of a solution in the form of a multimodal neural network model supporting skin diagnostics by aggregating clinical information from photos and texts. The idea of aggregation of many modalities in modelling a clinical decision is already well known in the scientific literature, and the authors used in this case the common generic families of neural network architectures: ResNet and BERT. While the article itself looks interesting, I have noticed a few flaws that the authors should refer to. It is worth specifying at the beginning what kind of clinical information was included in the text data. Was there information that was unobservable from the point of view of the visual skin analysis, which meant that it was not correlated with the information contained in the photos? It is worth clarifying what experience the specialists have had in describing the data. Is it worth clarifying how the ground truth of the photo descriptions was selected? What would be the procedure if the specialists (with a completely independent evaluation) assigned different ratings to the photos? Sections regarding descriptions of dataset characteristics, details of training models and initial data preparation should be moved from "Results" to "Methods" or create a separate chapter, e.g. "Materials". The details of the training do not apply to the results. This chapter should contain the final results. Table 2 should be included in the paragraph with the first reference to. In this table, the authors present the result of the comparison of the accuracy of the classification obtained on the ViT and ResNet architectures. It is worth mentioning that these results, with the current data set, should not be summarised with each other. One of the important requirements of the Transformer architecture is a sufficiently large set of training data, where most often the minimum requirements apply to several hundred thousand images, and reasonably, at least a million. With such a small dataset, the ViT architecture is doomed to failure. With such a small dataset, other CNN-based architectures should be compared, e.g. ResNet, Inception and Xception. The article lacks a coherent scheme presenting the final multimodal architecture and information about the training. Yes, Figure 3 presents a complete flow from data collection stage to inference. However, there is no information about the final model itself. Were the models that process the image and text learned together with the final classifier, jointly correcting errors? Or is the final classifier a separate model that classifies independent image and text inputs? Figures 1 and 2 only provide information about how the quality of the classification indicators changed during the training iterations. In my opinion, they do not contribute anything significant to the results. In addition, Figure 1 shows the features of overfitting model based on the ResNet architecture, which raises serious doubts about the reliability of the results. The dataset used in the experiment was unbalanced in terms of the number of photos in decision classes. The authors do not specify how they solved this problem in the context of training and evaluating model results. According to the names of the quality measures, the authors refer to their global versions intended for balanced datasets. Figures 4 and 5 are trivial and contribute nothing but to the repetition of the content of the previous paragraphs. The final results of the accuracy of clinical models are standard given in confidence intervals at least using typical statistical indicators: sensitivity, specificity, PPV and NPV. For better reception, it is worth listing them in the table. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Competing Interests: No competing interests were disclosed. Reviewer Expertise: computer vision, deep learning, reinforcement learning, biomedical engineering, parallel processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Krzywicki T. Reviewer Report For: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.5256/f1000research.186388.r446710 ) The direct URL for this report is: https://f1000research.com/articles/14-952/v1#referee-response-446710 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 09 Feb 2026 Alida Widiawaty , Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia 09 Feb 2026 Author Response Dear Tomasz Krzywicki University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland We thank the reviewer for the thorough evaluation of our manuscript and for the constructive ... Continue reading Dear Tomasz Krzywicki University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of clinical information Reviewer comment: It is worth specifying at the beginning what kind of clinical information was included in the text data. Was there information that was unobservable from the point of view of the visual skin analysis, which meant that it was not correlated with the information contained in the photos? Response: Thank you for this valuable suggestion. We have clarified the clinical variables included in the textual modality at the beginning of the Methods section. Specifically, clinical information incorporated into the text data for non–atopic dermatitis (non-AD) cases comprised sex, age, chief complaint with onset, history of exposure to allergens or irritants, source of infection, current triggering factors, duration of illness, lesion location, past medical history, and family medical history. For atopic dermatitis (AD) cases, additional inputs included major and minor diagnostic criteria. Importantly, several of these variables—such as symptom onset, exposure history, triggering factors, and family history—are not directly observable from visual skin analysis and therefore provide complementary, non-visual clinical context beyond the information contained in the images. 2. Clarification of the specialists' experience Reviewer comment: It is worth clarifying what experience the specialists have had in describing the data. Response: Thank you for this suggestion. We have clarified the expertise of the annotators in the Methods section. Specifically, all images were independently annotated by two board-certified dermatologists, each with over 20 years of clinical experience in inflammatory dermatoses, achieving 100% inter-rater agreement. 3. Clarification of how the photo descriptions was selected Reviewer comment: Is it worth clarifying how the ground truth of the photo descriptions was selected? What would be the procedure if the specialists (with a completely independent evaluation) assigned different ratings to the photos? Response: Thank you for this comment. Ground truth photo descriptions were established based on independent annotations performed by two board-certified dermatologists. Complete inter-rater agreement (100%) was achieved across all images; therefore, all annotated photo descriptions were retained for analysis. We have added clarification that, in the event of discrepant ratings, images with discordant annotations and their associated clinical data would have been excluded from the dataset to avoid introducing ambiguity in the ground truth labels. 4. Revision of the results section Reviewer comment: Sections regarding descriptions of dataset characteristics, details of training models and initial data preparation should be moved from "Results" to "Methods" or create a separate chapter, e.g. "Materials". The details of the training do not apply to the results. This chapter should contain the final results. Response: Thank you for this comment. We have revised the manuscript structure by moving the descriptions of dataset characteristics, model training details, and initial data preparation from the Results section to a separate chapter entitled Materials, as suggested. The Results section has been streamlined to present only the final performance outcomes of the models. 5. Clarification of the comparison between ResNet and Vision Transformer (ViT) Reviewer comment: Table 2 should be included in the paragraph with the first reference to. In this table, the authors present the result of the comparison of the accuracy of the classification obtained on the ViT and ResNet architectures. It is worth mentioning that these results, with the current data set, should not be summarised with each other. One of the important requirements of the Transformer architecture is a sufficiently large set of training data, where most often the minimum requirements apply to several hundred thousand images, and reasonably, at least a million. With such a small dataset, the ViT architecture is doomed to failure. With such a small dataset, other CNN-based architectures should be compared, e.g. ResNet, Inception and Xception. Response: Thank you for this valuable comment. Table 2 has been relocated to appear immediately after its first mention in the text. In response to the reviewer’s concern, the direct performance comparison with the Vision Transformer (ViT) architecture has been removed from Table 2. We have also revised the Methods section to clarify that the ViT model was included for exploratory purposes only. Given the limited dataset size, ViT was not expected to achieve optimal performance, as transformer-based architectures generally require substantially larger training datasets. Accordingly, ViT results were not used as a basis for direct performance comparison with CNN-based models. In addition, the Discussion section has been expanded to explicitly state that any comparison between CNN-based architectures and ViT should be interpreted with caution. The relatively small dataset in this study likely constrained the performance of the ViT model, consistent with the known data requirements of transformer-based approaches. 6. Clarification of the final multimodal architecture and information about the training Reviewer comment: The article lacks a coherent scheme presenting the final multimodal architecture and information about the training. Yes, Figure 3 presents a complete flow from data collection stage to inference. However, there is no information about the final model itself. Were the models that process the image and text learned together with the final classifier, jointly correcting errors? Or is the final classifier a separate model that classifies independent image and text inputs? Response: Thank you for this comment. We have clarified the final multimodal architecture and training strategy in the Methods section. Once ResNet50 and MPNet processed their respective modalities and generated modality-specific feature embeddings, the resulting vectors were concatenated to form a unified multimodal representation. This late-fusion strategy integrates fine-grained visual information from images with contextual information derived from clinical text. The image and text encoders were trained independently, and their parameters were kept fixed during multimodal training. The concatenated feature vector was subsequently passed through several fully connected layers, which were trained as a separate classifier to distinguish between atopic dermatitis (AD) and non–atopic dermatitis (non-AD) cases. No end-to-end joint optimization across modalities was performed. 7. Revision of results section and removal of redundant figures Reviewer comment: Figures 1 and 2 only provide information about how the quality of the classification indicators changed during the training iterations. In my opinion, they do not contribute anything significant to the results. In addition, Figure 1 shows the features of overfitting model based on the ResNet architecture, which raises serious doubts about the reliability of the results. Response: Thank you for this insightful comment. We agree that Figures 1 and 2 primarily illustrate training dynamics rather than final model performance and do not add substantial value to the presentation of the results. Moreover, as the learning curves may raise concerns regarding overfitting, we have removed Figures 1 and 2 from the revised manuscript. The Results section has been refocused on the final evaluation metrics obtained on the test set, which more accurately reflect the clinical performance of the proposed models. 8. Handling Class Imbalance and Evaluation Metrics Reviewer comment: The dataset used in the experiment was unbalanced in terms of the number of photos in decision classes. The authors do not specify how they solved this problem in the context of training and evaluating model results. According to the names of the quality measures, the authors refer to their global versions intended for balanced datasets. Response: Thank you for this comment. The dataset was imbalanced with respect to the number of samples per class. To address this issue during model development, we have added information indicating that stratified splitting was applied to preserve class proportions across the training, validation, and test sets. For performance evaluation, class-imbalance–aware metrics were used, including weighted precision, weighted recall, and weighted F1-score, in addition to overall accuracy. These weighted metrics compute class-wise scores and aggregate them according to class prevalence, thereby providing a more reliable assessment under imbalanced data conditions. 9. Inclusion of research flow diagrams Reviewer comment: Figures 4 and 5 are trivial and contribute nothing but to the repetition of the content of the previous paragraphs. Response: Thank you for this comment. Figures 2 and 3 (previously Figures 4 and 5) were included to comply with the STARD 2015 reporting guidelines, specifically the recommended flow of participants diagram. These figures provide a transparent visual summary of the study flow, including participant inclusion and data processing steps, which may not be fully conveyed through narrative text alone. 10. Addition of confidence intervals and diagnostic performance table Reviewer comment: The final results of the accuracy of clinical models are standard given in confidence intervals at least using typical statistical indicators: sensitivity, specificity, PPV and NPV. For better reception, it is worth listing them in the table. Thank you for this important comment. We have revised the manuscript to report the final diagnostic performance of the multimodal ResNet50–MPNet model using standard clinical metrics, including accuracy, sensitivity, specificity, PPV, and NPV, each accompanied by 95% confidence intervals. The final accuracy of 98.28% (95% confidence interval = 96.93 - 99.65), sensitivity of 99.71% (95% confidence interval = 99.14 - 100), specificity of 97.21% (95% confidence interval = 95.01 - 99.41), Negative Predictive Value (NPV) 99.5% and Positive Predictive Value (PPV) 98.3%. These results are now summarized in a dedicated table entitled “Diagnostic Performance of the Multimodal ResNet50–MPNet Model” to improve clarity and clinical interpretability. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Dear Tomasz Krzywicki University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of clinical information Reviewer comment: It is worth specifying at the beginning what kind of clinical information was included in the text data. Was there information that was unobservable from the point of view of the visual skin analysis, which meant that it was not correlated with the information contained in the photos? Response: Thank you for this valuable suggestion. We have clarified the clinical variables included in the textual modality at the beginning of the Methods section. Specifically, clinical information incorporated into the text data for non–atopic dermatitis (non-AD) cases comprised sex, age, chief complaint with onset, history of exposure to allergens or irritants, source of infection, current triggering factors, duration of illness, lesion location, past medical history, and family medical history. For atopic dermatitis (AD) cases, additional inputs included major and minor diagnostic criteria. Importantly, several of these variables—such as symptom onset, exposure history, triggering factors, and family history—are not directly observable from visual skin analysis and therefore provide complementary, non-visual clinical context beyond the information contained in the images. 2. Clarification of the specialists' experience Reviewer comment: It is worth clarifying what experience the specialists have had in describing the data. Response: Thank you for this suggestion. We have clarified the expertise of the annotators in the Methods section. Specifically, all images were independently annotated by two board-certified dermatologists, each with over 20 years of clinical experience in inflammatory dermatoses, achieving 100% inter-rater agreement. 3. Clarification of how the photo descriptions was selected Reviewer comment: Is it worth clarifying how the ground truth of the photo descriptions was selected? What would be the procedure if the specialists (with a completely independent evaluation) assigned different ratings to the photos? Response: Thank you for this comment. Ground truth photo descriptions were established based on independent annotations performed by two board-certified dermatologists. Complete inter-rater agreement (100%) was achieved across all images; therefore, all annotated photo descriptions were retained for analysis. We have added clarification that, in the event of discrepant ratings, images with discordant annotations and their associated clinical data would have been excluded from the dataset to avoid introducing ambiguity in the ground truth labels. 4. Revision of the results section Reviewer comment: Sections regarding descriptions of dataset characteristics, details of training models and initial data preparation should be moved from "Results" to "Methods" or create a separate chapter, e.g. "Materials". The details of the training do not apply to the results. This chapter should contain the final results. Response: Thank you for this comment. We have revised the manuscript structure by moving the descriptions of dataset characteristics, model training details, and initial data preparation from the Results section to a separate chapter entitled Materials, as suggested. The Results section has been streamlined to present only the final performance outcomes of the models. 5. Clarification of the comparison between ResNet and Vision Transformer (ViT) Reviewer comment: Table 2 should be included in the paragraph with the first reference to. In this table, the authors present the result of the comparison of the accuracy of the classification obtained on the ViT and ResNet architectures. It is worth mentioning that these results, with the current data set, should not be summarised with each other. One of the important requirements of the Transformer architecture is a sufficiently large set of training data, where most often the minimum requirements apply to several hundred thousand images, and reasonably, at least a million. With such a small dataset, the ViT architecture is doomed to failure. With such a small dataset, other CNN-based architectures should be compared, e.g. ResNet, Inception and Xception. Response: Thank you for this valuable comment. Table 2 has been relocated to appear immediately after its first mention in the text. In response to the reviewer’s concern, the direct performance comparison with the Vision Transformer (ViT) architecture has been removed from Table 2. We have also revised the Methods section to clarify that the ViT model was included for exploratory purposes only. Given the limited dataset size, ViT was not expected to achieve optimal performance, as transformer-based architectures generally require substantially larger training datasets. Accordingly, ViT results were not used as a basis for direct performance comparison with CNN-based models. In addition, the Discussion section has been expanded to explicitly state that any comparison between CNN-based architectures and ViT should be interpreted with caution. The relatively small dataset in this study likely constrained the performance of the ViT model, consistent with the known data requirements of transformer-based approaches. 6. Clarification of the final multimodal architecture and information about the training Reviewer comment: The article lacks a coherent scheme presenting the final multimodal architecture and information about the training. Yes, Figure 3 presents a complete flow from data collection stage to inference. However, there is no information about the final model itself. Were the models that process the image and text learned together with the final classifier, jointly correcting errors? Or is the final classifier a separate model that classifies independent image and text inputs? Response: Thank you for this comment. We have clarified the final multimodal architecture and training strategy in the Methods section. Once ResNet50 and MPNet processed their respective modalities and generated modality-specific feature embeddings, the resulting vectors were concatenated to form a unified multimodal representation. This late-fusion strategy integrates fine-grained visual information from images with contextual information derived from clinical text. The image and text encoders were trained independently, and their parameters were kept fixed during multimodal training. The concatenated feature vector was subsequently passed through several fully connected layers, which were trained as a separate classifier to distinguish between atopic dermatitis (AD) and non–atopic dermatitis (non-AD) cases. No end-to-end joint optimization across modalities was performed. 7. Revision of results section and removal of redundant figures Reviewer comment: Figures 1 and 2 only provide information about how the quality of the classification indicators changed during the training iterations. In my opinion, they do not contribute anything significant to the results. In addition, Figure 1 shows the features of overfitting model based on the ResNet architecture, which raises serious doubts about the reliability of the results. Response: Thank you for this insightful comment. We agree that Figures 1 and 2 primarily illustrate training dynamics rather than final model performance and do not add substantial value to the presentation of the results. Moreover, as the learning curves may raise concerns regarding overfitting, we have removed Figures 1 and 2 from the revised manuscript. The Results section has been refocused on the final evaluation metrics obtained on the test set, which more accurately reflect the clinical performance of the proposed models. 8. Handling Class Imbalance and Evaluation Metrics Reviewer comment: The dataset used in the experiment was unbalanced in terms of the number of photos in decision classes. The authors do not specify how they solved this problem in the context of training and evaluating model results. According to the names of the quality measures, the authors refer to their global versions intended for balanced datasets. Response: Thank you for this comment. The dataset was imbalanced with respect to the number of samples per class. To address this issue during model development, we have added information indicating that stratified splitting was applied to preserve class proportions across the training, validation, and test sets. For performance evaluation, class-imbalance–aware metrics were used, including weighted precision, weighted recall, and weighted F1-score, in addition to overall accuracy. These weighted metrics compute class-wise scores and aggregate them according to class prevalence, thereby providing a more reliable assessment under imbalanced data conditions. 9. Inclusion of research flow diagrams Reviewer comment: Figures 4 and 5 are trivial and contribute nothing but to the repetition of the content of the previous paragraphs. Response: Thank you for this comment. Figures 2 and 3 (previously Figures 4 and 5) were included to comply with the STARD 2015 reporting guidelines, specifically the recommended flow of participants diagram. These figures provide a transparent visual summary of the study flow, including participant inclusion and data processing steps, which may not be fully conveyed through narrative text alone. 10. Addition of confidence intervals and diagnostic performance table Reviewer comment: The final results of the accuracy of clinical models are standard given in confidence intervals at least using typical statistical indicators: sensitivity, specificity, PPV and NPV. For better reception, it is worth listing them in the table. Thank you for this important comment. We have revised the manuscript to report the final diagnostic performance of the multimodal ResNet50–MPNet model using standard clinical metrics, including accuracy, sensitivity, specificity, PPV, and NPV, each accompanied by 95% confidence intervals. The final accuracy of 98.28% (95% confidence interval = 96.93 - 99.65), sensitivity of 99.71% (95% confidence interval = 99.14 - 100), specificity of 97.21% (95% confidence interval = 95.01 - 99.41), Negative Predictive Value (NPV) 99.5% and Positive Predictive Value (PPV) 98.3%. These results are now summarized in a dedicated table entitled “Diagnostic Performance of the Multimodal ResNet50–MPNet Model” to improve clarity and clinical interpretability. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 09 Feb 2026 Alida Widiawaty , Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia 09 Feb 2026 Author Response Dear Tomasz Krzywicki University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland We thank the reviewer for the thorough evaluation of our manuscript and for the constructive ... Continue reading Dear Tomasz Krzywicki University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of clinical information Reviewer comment: It is worth specifying at the beginning what kind of clinical information was included in the text data. Was there information that was unobservable from the point of view of the visual skin analysis, which meant that it was not correlated with the information contained in the photos? Response: Thank you for this valuable suggestion. We have clarified the clinical variables included in the textual modality at the beginning of the Methods section. Specifically, clinical information incorporated into the text data for non–atopic dermatitis (non-AD) cases comprised sex, age, chief complaint with onset, history of exposure to allergens or irritants, source of infection, current triggering factors, duration of illness, lesion location, past medical history, and family medical history. For atopic dermatitis (AD) cases, additional inputs included major and minor diagnostic criteria. Importantly, several of these variables—such as symptom onset, exposure history, triggering factors, and family history—are not directly observable from visual skin analysis and therefore provide complementary, non-visual clinical context beyond the information contained in the images. 2. Clarification of the specialists' experience Reviewer comment: It is worth clarifying what experience the specialists have had in describing the data. Response: Thank you for this suggestion. We have clarified the expertise of the annotators in the Methods section. Specifically, all images were independently annotated by two board-certified dermatologists, each with over 20 years of clinical experience in inflammatory dermatoses, achieving 100% inter-rater agreement. 3. Clarification of how the photo descriptions was selected Reviewer comment: Is it worth clarifying how the ground truth of the photo descriptions was selected? What would be the procedure if the specialists (with a completely independent evaluation) assigned different ratings to the photos? Response: Thank you for this comment. Ground truth photo descriptions were established based on independent annotations performed by two board-certified dermatologists. Complete inter-rater agreement (100%) was achieved across all images; therefore, all annotated photo descriptions were retained for analysis. We have added clarification that, in the event of discrepant ratings, images with discordant annotations and their associated clinical data would have been excluded from the dataset to avoid introducing ambiguity in the ground truth labels. 4. Revision of the results section Reviewer comment: Sections regarding descriptions of dataset characteristics, details of training models and initial data preparation should be moved from "Results" to "Methods" or create a separate chapter, e.g. "Materials". The details of the training do not apply to the results. This chapter should contain the final results. Response: Thank you for this comment. We have revised the manuscript structure by moving the descriptions of dataset characteristics, model training details, and initial data preparation from the Results section to a separate chapter entitled Materials, as suggested. The Results section has been streamlined to present only the final performance outcomes of the models. 5. Clarification of the comparison between ResNet and Vision Transformer (ViT) Reviewer comment: Table 2 should be included in the paragraph with the first reference to. In this table, the authors present the result of the comparison of the accuracy of the classification obtained on the ViT and ResNet architectures. It is worth mentioning that these results, with the current data set, should not be summarised with each other. One of the important requirements of the Transformer architecture is a sufficiently large set of training data, where most often the minimum requirements apply to several hundred thousand images, and reasonably, at least a million. With such a small dataset, the ViT architecture is doomed to failure. With such a small dataset, other CNN-based architectures should be compared, e.g. ResNet, Inception and Xception. Response: Thank you for this valuable comment. Table 2 has been relocated to appear immediately after its first mention in the text. In response to the reviewer’s concern, the direct performance comparison with the Vision Transformer (ViT) architecture has been removed from Table 2. We have also revised the Methods section to clarify that the ViT model was included for exploratory purposes only. Given the limited dataset size, ViT was not expected to achieve optimal performance, as transformer-based architectures generally require substantially larger training datasets. Accordingly, ViT results were not used as a basis for direct performance comparison with CNN-based models. In addition, the Discussion section has been expanded to explicitly state that any comparison between CNN-based architectures and ViT should be interpreted with caution. The relatively small dataset in this study likely constrained the performance of the ViT model, consistent with the known data requirements of transformer-based approaches. 6. Clarification of the final multimodal architecture and information about the training Reviewer comment: The article lacks a coherent scheme presenting the final multimodal architecture and information about the training. Yes, Figure 3 presents a complete flow from data collection stage to inference. However, there is no information about the final model itself. Were the models that process the image and text learned together with the final classifier, jointly correcting errors? Or is the final classifier a separate model that classifies independent image and text inputs? Response: Thank you for this comment. We have clarified the final multimodal architecture and training strategy in the Methods section. Once ResNet50 and MPNet processed their respective modalities and generated modality-specific feature embeddings, the resulting vectors were concatenated to form a unified multimodal representation. This late-fusion strategy integrates fine-grained visual information from images with contextual information derived from clinical text. The image and text encoders were trained independently, and their parameters were kept fixed during multimodal training. The concatenated feature vector was subsequently passed through several fully connected layers, which were trained as a separate classifier to distinguish between atopic dermatitis (AD) and non–atopic dermatitis (non-AD) cases. No end-to-end joint optimization across modalities was performed. 7. Revision of results section and removal of redundant figures Reviewer comment: Figures 1 and 2 only provide information about how the quality of the classification indicators changed during the training iterations. In my opinion, they do not contribute anything significant to the results. In addition, Figure 1 shows the features of overfitting model based on the ResNet architecture, which raises serious doubts about the reliability of the results. Response: Thank you for this insightful comment. We agree that Figures 1 and 2 primarily illustrate training dynamics rather than final model performance and do not add substantial value to the presentation of the results. Moreover, as the learning curves may raise concerns regarding overfitting, we have removed Figures 1 and 2 from the revised manuscript. The Results section has been refocused on the final evaluation metrics obtained on the test set, which more accurately reflect the clinical performance of the proposed models. 8. Handling Class Imbalance and Evaluation Metrics Reviewer comment: The dataset used in the experiment was unbalanced in terms of the number of photos in decision classes. The authors do not specify how they solved this problem in the context of training and evaluating model results. According to the names of the quality measures, the authors refer to their global versions intended for balanced datasets. Response: Thank you for this comment. The dataset was imbalanced with respect to the number of samples per class. To address this issue during model development, we have added information indicating that stratified splitting was applied to preserve class proportions across the training, validation, and test sets. For performance evaluation, class-imbalance–aware metrics were used, including weighted precision, weighted recall, and weighted F1-score, in addition to overall accuracy. These weighted metrics compute class-wise scores and aggregate them according to class prevalence, thereby providing a more reliable assessment under imbalanced data conditions. 9. Inclusion of research flow diagrams Reviewer comment: Figures 4 and 5 are trivial and contribute nothing but to the repetition of the content of the previous paragraphs. Response: Thank you for this comment. Figures 2 and 3 (previously Figures 4 and 5) were included to comply with the STARD 2015 reporting guidelines, specifically the recommended flow of participants diagram. These figures provide a transparent visual summary of the study flow, including participant inclusion and data processing steps, which may not be fully conveyed through narrative text alone. 10. Addition of confidence intervals and diagnostic performance table Reviewer comment: The final results of the accuracy of clinical models are standard given in confidence intervals at least using typical statistical indicators: sensitivity, specificity, PPV and NPV. For better reception, it is worth listing them in the table. Thank you for this important comment. We have revised the manuscript to report the final diagnostic performance of the multimodal ResNet50–MPNet model using standard clinical metrics, including accuracy, sensitivity, specificity, PPV, and NPV, each accompanied by 95% confidence intervals. The final accuracy of 98.28% (95% confidence interval = 96.93 - 99.65), sensitivity of 99.71% (95% confidence interval = 99.14 - 100), specificity of 97.21% (95% confidence interval = 95.01 - 99.41), Negative Predictive Value (NPV) 99.5% and Positive Predictive Value (PPV) 98.3%. These results are now summarized in a dedicated table entitled “Diagnostic Performance of the Multimodal ResNet50–MPNet Model” to improve clarity and clinical interpretability. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Dear Tomasz Krzywicki University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of clinical information Reviewer comment: It is worth specifying at the beginning what kind of clinical information was included in the text data. Was there information that was unobservable from the point of view of the visual skin analysis, which meant that it was not correlated with the information contained in the photos? Response: Thank you for this valuable suggestion. We have clarified the clinical variables included in the textual modality at the beginning of the Methods section. Specifically, clinical information incorporated into the text data for non–atopic dermatitis (non-AD) cases comprised sex, age, chief complaint with onset, history of exposure to allergens or irritants, source of infection, current triggering factors, duration of illness, lesion location, past medical history, and family medical history. For atopic dermatitis (AD) cases, additional inputs included major and minor diagnostic criteria. Importantly, several of these variables—such as symptom onset, exposure history, triggering factors, and family history—are not directly observable from visual skin analysis and therefore provide complementary, non-visual clinical context beyond the information contained in the images. 2. Clarification of the specialists' experience Reviewer comment: It is worth clarifying what experience the specialists have had in describing the data. Response: Thank you for this suggestion. We have clarified the expertise of the annotators in the Methods section. Specifically, all images were independently annotated by two board-certified dermatologists, each with over 20 years of clinical experience in inflammatory dermatoses, achieving 100% inter-rater agreement. 3. Clarification of how the photo descriptions was selected Reviewer comment: Is it worth clarifying how the ground truth of the photo descriptions was selected? What would be the procedure if the specialists (with a completely independent evaluation) assigned different ratings to the photos? Response: Thank you for this comment. Ground truth photo descriptions were established based on independent annotations performed by two board-certified dermatologists. Complete inter-rater agreement (100%) was achieved across all images; therefore, all annotated photo descriptions were retained for analysis. We have added clarification that, in the event of discrepant ratings, images with discordant annotations and their associated clinical data would have been excluded from the dataset to avoid introducing ambiguity in the ground truth labels. 4. Revision of the results section Reviewer comment: Sections regarding descriptions of dataset characteristics, details of training models and initial data preparation should be moved from "Results" to "Methods" or create a separate chapter, e.g. "Materials". The details of the training do not apply to the results. This chapter should contain the final results. Response: Thank you for this comment. We have revised the manuscript structure by moving the descriptions of dataset characteristics, model training details, and initial data preparation from the Results section to a separate chapter entitled Materials, as suggested. The Results section has been streamlined to present only the final performance outcomes of the models. 5. Clarification of the comparison between ResNet and Vision Transformer (ViT) Reviewer comment: Table 2 should be included in the paragraph with the first reference to. In this table, the authors present the result of the comparison of the accuracy of the classification obtained on the ViT and ResNet architectures. It is worth mentioning that these results, with the current data set, should not be summarised with each other. One of the important requirements of the Transformer architecture is a sufficiently large set of training data, where most often the minimum requirements apply to several hundred thousand images, and reasonably, at least a million. With such a small dataset, the ViT architecture is doomed to failure. With such a small dataset, other CNN-based architectures should be compared, e.g. ResNet, Inception and Xception. Response: Thank you for this valuable comment. Table 2 has been relocated to appear immediately after its first mention in the text. In response to the reviewer’s concern, the direct performance comparison with the Vision Transformer (ViT) architecture has been removed from Table 2. We have also revised the Methods section to clarify that the ViT model was included for exploratory purposes only. Given the limited dataset size, ViT was not expected to achieve optimal performance, as transformer-based architectures generally require substantially larger training datasets. Accordingly, ViT results were not used as a basis for direct performance comparison with CNN-based models. In addition, the Discussion section has been expanded to explicitly state that any comparison between CNN-based architectures and ViT should be interpreted with caution. The relatively small dataset in this study likely constrained the performance of the ViT model, consistent with the known data requirements of transformer-based approaches. 6. Clarification of the final multimodal architecture and information about the training Reviewer comment: The article lacks a coherent scheme presenting the final multimodal architecture and information about the training. Yes, Figure 3 presents a complete flow from data collection stage to inference. However, there is no information about the final model itself. Were the models that process the image and text learned together with the final classifier, jointly correcting errors? Or is the final classifier a separate model that classifies independent image and text inputs? Response: Thank you for this comment. We have clarified the final multimodal architecture and training strategy in the Methods section. Once ResNet50 and MPNet processed their respective modalities and generated modality-specific feature embeddings, the resulting vectors were concatenated to form a unified multimodal representation. This late-fusion strategy integrates fine-grained visual information from images with contextual information derived from clinical text. The image and text encoders were trained independently, and their parameters were kept fixed during multimodal training. The concatenated feature vector was subsequently passed through several fully connected layers, which were trained as a separate classifier to distinguish between atopic dermatitis (AD) and non–atopic dermatitis (non-AD) cases. No end-to-end joint optimization across modalities was performed. 7. Revision of results section and removal of redundant figures Reviewer comment: Figures 1 and 2 only provide information about how the quality of the classification indicators changed during the training iterations. In my opinion, they do not contribute anything significant to the results. In addition, Figure 1 shows the features of overfitting model based on the ResNet architecture, which raises serious doubts about the reliability of the results. Response: Thank you for this insightful comment. We agree that Figures 1 and 2 primarily illustrate training dynamics rather than final model performance and do not add substantial value to the presentation of the results. Moreover, as the learning curves may raise concerns regarding overfitting, we have removed Figures 1 and 2 from the revised manuscript. The Results section has been refocused on the final evaluation metrics obtained on the test set, which more accurately reflect the clinical performance of the proposed models. 8. Handling Class Imbalance and Evaluation Metrics Reviewer comment: The dataset used in the experiment was unbalanced in terms of the number of photos in decision classes. The authors do not specify how they solved this problem in the context of training and evaluating model results. According to the names of the quality measures, the authors refer to their global versions intended for balanced datasets. Response: Thank you for this comment. The dataset was imbalanced with respect to the number of samples per class. To address this issue during model development, we have added information indicating that stratified splitting was applied to preserve class proportions across the training, validation, and test sets. For performance evaluation, class-imbalance–aware metrics were used, including weighted precision, weighted recall, and weighted F1-score, in addition to overall accuracy. These weighted metrics compute class-wise scores and aggregate them according to class prevalence, thereby providing a more reliable assessment under imbalanced data conditions. 9. Inclusion of research flow diagrams Reviewer comment: Figures 4 and 5 are trivial and contribute nothing but to the repetition of the content of the previous paragraphs. Response: Thank you for this comment. Figures 2 and 3 (previously Figures 4 and 5) were included to comply with the STARD 2015 reporting guidelines, specifically the recommended flow of participants diagram. These figures provide a transparent visual summary of the study flow, including participant inclusion and data processing steps, which may not be fully conveyed through narrative text alone. 10. Addition of confidence intervals and diagnostic performance table Reviewer comment: The final results of the accuracy of clinical models are standard given in confidence intervals at least using typical statistical indicators: sensitivity, specificity, PPV and NPV. For better reception, it is worth listing them in the table. Thank you for this important comment. We have revised the manuscript to report the final diagnostic performance of the multimodal ResNet50–MPNet model using standard clinical metrics, including accuracy, sensitivity, specificity, PPV, and NPV, each accompanied by 95% confidence intervals. The final accuracy of 98.28% (95% confidence interval = 96.93 - 99.65), sensitivity of 99.71% (95% confidence interval = 99.14 - 100), specificity of 97.21% (95% confidence interval = 95.01 - 99.41), Negative Predictive Value (NPV) 99.5% and Positive Predictive Value (PPV) 98.3%. These results are now summarized in a dedicated table entitled “Diagnostic Performance of the Multimodal ResNet50–MPNet Model” to improve clarity and clinical interpretability. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Sitaru S. Reviewer Report For: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.5256/f1000research.186388.r420623 ) The direct URL for this report is: https://f1000research.com/articles/14-952/v1#referee-response-420623 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 29 Oct 2025 Sebastian Sitaru , Technical University of Munich, Munich, Munich, Germany Approved VIEWS 0 https://doi.org/10.5256/f1000research.186388.r420623 In their manuscript, the authors describe the creation and validation of a multimodal image-text AI algorithm for the diagnosis of atopic dermatitis. The topic is highly relevant and the technical approach seems sound. The manuscript is overall ... Continue reading READ ALL In their manuscript, the authors describe the creation and validation of a multimodal image-text AI algorithm for the diagnosis of atopic dermatitis. The topic is highly relevant and the technical approach seems sound. The manuscript is overall well written and concise. It is commendable that the authors decided to share their source code. I have a couple of minor points which could be improved before Indexing: - In the methods it says, "Two board-certified dermatologists independently annotated all images". Do you mean all cases, so image+text? Or did they just annotate the images alone? - Was the skin tone of the patients recorded? Even if not, I think this point deserves a mention in the discussion Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Data analysis and AI applications in dermatology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Sitaru S. Reviewer Report For: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.5256/f1000research.186388.r420623 ) The direct URL for this report is: https://f1000research.com/articles/14-952/v1#referee-response-420623 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 09 Feb 2026 Alida Widiawaty , Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia 09 Feb 2026 Author Response Dear Sebastian Sitaru Technical University of Munich, Munich, Germany We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved ... Continue reading Dear Sebastian Sitaru Technical University of Munich, Munich, Germany We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of annotation scope Reviewer comment: In the methods it says, "Two board-certified dermatologists independently annotated all images". Do you mean all cases, so image + text? Or did they just annotate the images alone? Response: Thank you for this insightful comment. We have clarified this point in the manuscript to state that two board-certified dermatologists independently annotated both the clinical images and the corresponding text data for all cases. 2. Clarification of patients' skin tone Reviewer comment: Was the skin tone of the patients recorded? Even if not, I think this point deserves a mention in the discussion Response: Thank you for this comment. Skin tone was not explicitly recorded as a standalone variable in this study. However, the study population predominantly consisted of Indonesian patients, who generally fall within Fitzpatrick skin phototypes III–IV. Within this relatively homogeneous skin tone range, no systematic variation in clinical presentation or image-based model performance attributable to skin tone was observed. Therefore, skin tone was not considered a confounding factor influencing the study outcomes. Nonetheless, we acknowledge in the Discussion that external validation in cohorts with a broader range of Fitzpatrick skin types would be valuable to confirm the model’s robustness across more diverse populations. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Dear Sebastian Sitaru Technical University of Munich, Munich, Germany We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of annotation scope Reviewer comment: In the methods it says, "Two board-certified dermatologists independently annotated all images". Do you mean all cases, so image + text? Or did they just annotate the images alone? Response: Thank you for this insightful comment. We have clarified this point in the manuscript to state that two board-certified dermatologists independently annotated both the clinical images and the corresponding text data for all cases. 2. Clarification of patients' skin tone Reviewer comment: Was the skin tone of the patients recorded? Even if not, I think this point deserves a mention in the discussion Response: Thank you for this comment. Skin tone was not explicitly recorded as a standalone variable in this study. However, the study population predominantly consisted of Indonesian patients, who generally fall within Fitzpatrick skin phototypes III–IV. Within this relatively homogeneous skin tone range, no systematic variation in clinical presentation or image-based model performance attributable to skin tone was observed. Therefore, skin tone was not considered a confounding factor influencing the study outcomes. Nonetheless, we acknowledge in the Discussion that external validation in cohorts with a broader range of Fitzpatrick skin types would be valuable to confirm the model’s robustness across more diverse populations. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 09 Feb 2026 Alida Widiawaty , Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia 09 Feb 2026 Author Response Dear Sebastian Sitaru Technical University of Munich, Munich, Germany We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved ... Continue reading Dear Sebastian Sitaru Technical University of Munich, Munich, Germany We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of annotation scope Reviewer comment: In the methods it says, "Two board-certified dermatologists independently annotated all images". Do you mean all cases, so image + text? Or did they just annotate the images alone? Response: Thank you for this insightful comment. We have clarified this point in the manuscript to state that two board-certified dermatologists independently annotated both the clinical images and the corresponding text data for all cases. 2. Clarification of patients' skin tone Reviewer comment: Was the skin tone of the patients recorded? Even if not, I think this point deserves a mention in the discussion Response: Thank you for this comment. Skin tone was not explicitly recorded as a standalone variable in this study. However, the study population predominantly consisted of Indonesian patients, who generally fall within Fitzpatrick skin phototypes III–IV. Within this relatively homogeneous skin tone range, no systematic variation in clinical presentation or image-based model performance attributable to skin tone was observed. Therefore, skin tone was not considered a confounding factor influencing the study outcomes. Nonetheless, we acknowledge in the Discussion that external validation in cohorts with a broader range of Fitzpatrick skin types would be valuable to confirm the model’s robustness across more diverse populations. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Dear Sebastian Sitaru Technical University of Munich, Munich, Germany We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of annotation scope Reviewer comment: In the methods it says, "Two board-certified dermatologists independently annotated all images". Do you mean all cases, so image + text? Or did they just annotate the images alone? Response: Thank you for this insightful comment. We have clarified this point in the manuscript to state that two board-certified dermatologists independently annotated both the clinical images and the corresponding text data for all cases. 2. Clarification of patients' skin tone Reviewer comment: Was the skin tone of the patients recorded? Even if not, I think this point deserves a mention in the discussion Response: Thank you for this comment. Skin tone was not explicitly recorded as a standalone variable in this study. However, the study population predominantly consisted of Indonesian patients, who generally fall within Fitzpatrick skin phototypes III–IV. Within this relatively homogeneous skin tone range, no systematic variation in clinical presentation or image-based model performance attributable to skin tone was observed. Therefore, skin tone was not considered a confounding factor influencing the study outcomes. Nonetheless, we acknowledge in the Discussion that external validation in cohorts with a broader range of Fitzpatrick skin types would be valuable to confirm the model’s robustness across more diverse populations. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 19 Sep 2025 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 09 Feb 26 read Version 1 19 Sep 25 read read Sebastian Sitaru , Technical University of Munich, Munich, Germany Tomasz Krzywicki , University of Warmia and Mazury in Olsztyn, Olsztyn, Poland Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Krzywicki T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 17 Feb 2026 | for Version 2 Tomasz Krzywicki , University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland 0 Views copyright © 2026 Krzywicki T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I thank the authors for addressing my comments. The article now looks much better. I have no further comments. Competing Interests No competing interests were disclosed. Reviewer Expertise computer vision, deep learning, reinforcement learning, biomedical engineering, parallel processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Krzywicki T. Peer Review Report For: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.5256/f1000research.195755.r456887) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-952/v2#referee-response-456887 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2026 Krzywicki T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Jan 2026 | for Version 1 Tomasz Krzywicki , University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland 0 Views copyright © 2026 Krzywicki T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The article concerns the proposal of a solution in the form of a multimodal neural network model supporting skin diagnostics by aggregating clinical information from photos and texts. The idea of aggregation of many modalities in modelling a clinical decision is already well known in the scientific literature, and the authors used in this case the common generic families of neural network architectures: ResNet and BERT. While the article itself looks interesting, I have noticed a few flaws that the authors should refer to. It is worth specifying at the beginning what kind of clinical information was included in the text data. Was there information that was unobservable from the point of view of the visual skin analysis, which meant that it was not correlated with the information contained in the photos? It is worth clarifying what experience the specialists have had in describing the data. Is it worth clarifying how the ground truth of the photo descriptions was selected? What would be the procedure if the specialists (with a completely independent evaluation) assigned different ratings to the photos? Sections regarding descriptions of dataset characteristics, details of training models and initial data preparation should be moved from "Results" to "Methods" or create a separate chapter, e.g. "Materials". The details of the training do not apply to the results. This chapter should contain the final results. Table 2 should be included in the paragraph with the first reference to. In this table, the authors present the result of the comparison of the accuracy of the classification obtained on the ViT and ResNet architectures. It is worth mentioning that these results, with the current data set, should not be summarised with each other. One of the important requirements of the Transformer architecture is a sufficiently large set of training data, where most often the minimum requirements apply to several hundred thousand images, and reasonably, at least a million. With such a small dataset, the ViT architecture is doomed to failure. With such a small dataset, other CNN-based architectures should be compared, e.g. ResNet, Inception and Xception. The article lacks a coherent scheme presenting the final multimodal architecture and information about the training. Yes, Figure 3 presents a complete flow from data collection stage to inference. However, there is no information about the final model itself. Were the models that process the image and text learned together with the final classifier, jointly correcting errors? Or is the final classifier a separate model that classifies independent image and text inputs? Figures 1 and 2 only provide information about how the quality of the classification indicators changed during the training iterations. In my opinion, they do not contribute anything significant to the results. In addition, Figure 1 shows the features of overfitting model based on the ResNet architecture, which raises serious doubts about the reliability of the results. The dataset used in the experiment was unbalanced in terms of the number of photos in decision classes. The authors do not specify how they solved this problem in the context of training and evaluating model results. According to the names of the quality measures, the authors refer to their global versions intended for balanced datasets. Figures 4 and 5 are trivial and contribute nothing but to the repetition of the content of the previous paragraphs. The final results of the accuracy of clinical models are standard given in confidence intervals at least using typical statistical indicators: sensitivity, specificity, PPV and NPV. For better reception, it is worth listing them in the table. Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? No If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? No Are the conclusions drawn adequately supported by the results? Partly Competing Interests No competing interests were disclosed. Reviewer Expertise computer vision, deep learning, reinforcement learning, biomedical engineering, parallel processing I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 09 Feb 2026 Alida Widiawaty, Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia Dear Tomasz Krzywicki University of Warmia and Mazury in Olsztyn, Olsztyn, Warmian-Masurian Voivodeship, Poland We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of clinical information Reviewer comment: It is worth specifying at the beginning what kind of clinical information was included in the text data. Was there information that was unobservable from the point of view of the visual skin analysis, which meant that it was not correlated with the information contained in the photos? Response: Thank you for this valuable suggestion. We have clarified the clinical variables included in the textual modality at the beginning of the Methods section. Specifically, clinical information incorporated into the text data for non–atopic dermatitis (non-AD) cases comprised sex, age, chief complaint with onset, history of exposure to allergens or irritants, source of infection, current triggering factors, duration of illness, lesion location, past medical history, and family medical history. For atopic dermatitis (AD) cases, additional inputs included major and minor diagnostic criteria. Importantly, several of these variables—such as symptom onset, exposure history, triggering factors, and family history—are not directly observable from visual skin analysis and therefore provide complementary, non-visual clinical context beyond the information contained in the images. 2. Clarification of the specialists' experience Reviewer comment: It is worth clarifying what experience the specialists have had in describing the data. Response: Thank you for this suggestion. We have clarified the expertise of the annotators in the Methods section. Specifically, all images were independently annotated by two board-certified dermatologists, each with over 20 years of clinical experience in inflammatory dermatoses, achieving 100% inter-rater agreement. 3. Clarification of how the photo descriptions was selected Reviewer comment: Is it worth clarifying how the ground truth of the photo descriptions was selected? What would be the procedure if the specialists (with a completely independent evaluation) assigned different ratings to the photos? Response: Thank you for this comment. Ground truth photo descriptions were established based on independent annotations performed by two board-certified dermatologists. Complete inter-rater agreement (100%) was achieved across all images; therefore, all annotated photo descriptions were retained for analysis. We have added clarification that, in the event of discrepant ratings, images with discordant annotations and their associated clinical data would have been excluded from the dataset to avoid introducing ambiguity in the ground truth labels. 4. Revision of the results section Reviewer comment: Sections regarding descriptions of dataset characteristics, details of training models and initial data preparation should be moved from "Results" to "Methods" or create a separate chapter, e.g. "Materials". The details of the training do not apply to the results. This chapter should contain the final results. Response: Thank you for this comment. We have revised the manuscript structure by moving the descriptions of dataset characteristics, model training details, and initial data preparation from the Results section to a separate chapter entitled Materials, as suggested. The Results section has been streamlined to present only the final performance outcomes of the models. 5. Clarification of the comparison between ResNet and Vision Transformer (ViT) Reviewer comment: Table 2 should be included in the paragraph with the first reference to. In this table, the authors present the result of the comparison of the accuracy of the classification obtained on the ViT and ResNet architectures. It is worth mentioning that these results, with the current data set, should not be summarised with each other. One of the important requirements of the Transformer architecture is a sufficiently large set of training data, where most often the minimum requirements apply to several hundred thousand images, and reasonably, at least a million. With such a small dataset, the ViT architecture is doomed to failure. With such a small dataset, other CNN-based architectures should be compared, e.g. ResNet, Inception and Xception. Response: Thank you for this valuable comment. Table 2 has been relocated to appear immediately after its first mention in the text. In response to the reviewer’s concern, the direct performance comparison with the Vision Transformer (ViT) architecture has been removed from Table 2. We have also revised the Methods section to clarify that the ViT model was included for exploratory purposes only. Given the limited dataset size, ViT was not expected to achieve optimal performance, as transformer-based architectures generally require substantially larger training datasets. Accordingly, ViT results were not used as a basis for direct performance comparison with CNN-based models. In addition, the Discussion section has been expanded to explicitly state that any comparison between CNN-based architectures and ViT should be interpreted with caution. The relatively small dataset in this study likely constrained the performance of the ViT model, consistent with the known data requirements of transformer-based approaches. 6. Clarification of the final multimodal architecture and information about the training Reviewer comment: The article lacks a coherent scheme presenting the final multimodal architecture and information about the training. Yes, Figure 3 presents a complete flow from data collection stage to inference. However, there is no information about the final model itself. Were the models that process the image and text learned together with the final classifier, jointly correcting errors? Or is the final classifier a separate model that classifies independent image and text inputs? Response: Thank you for this comment. We have clarified the final multimodal architecture and training strategy in the Methods section. Once ResNet50 and MPNet processed their respective modalities and generated modality-specific feature embeddings, the resulting vectors were concatenated to form a unified multimodal representation. This late-fusion strategy integrates fine-grained visual information from images with contextual information derived from clinical text. The image and text encoders were trained independently, and their parameters were kept fixed during multimodal training. The concatenated feature vector was subsequently passed through several fully connected layers, which were trained as a separate classifier to distinguish between atopic dermatitis (AD) and non–atopic dermatitis (non-AD) cases. No end-to-end joint optimization across modalities was performed. 7. Revision of results section and removal of redundant figures Reviewer comment: Figures 1 and 2 only provide information about how the quality of the classification indicators changed during the training iterations. In my opinion, they do not contribute anything significant to the results. In addition, Figure 1 shows the features of overfitting model based on the ResNet architecture, which raises serious doubts about the reliability of the results. Response: Thank you for this insightful comment. We agree that Figures 1 and 2 primarily illustrate training dynamics rather than final model performance and do not add substantial value to the presentation of the results. Moreover, as the learning curves may raise concerns regarding overfitting, we have removed Figures 1 and 2 from the revised manuscript. The Results section has been refocused on the final evaluation metrics obtained on the test set, which more accurately reflect the clinical performance of the proposed models. 8. Handling Class Imbalance and Evaluation Metrics Reviewer comment: The dataset used in the experiment was unbalanced in terms of the number of photos in decision classes. The authors do not specify how they solved this problem in the context of training and evaluating model results. According to the names of the quality measures, the authors refer to their global versions intended for balanced datasets. Response: Thank you for this comment. The dataset was imbalanced with respect to the number of samples per class. To address this issue during model development, we have added information indicating that stratified splitting was applied to preserve class proportions across the training, validation, and test sets. For performance evaluation, class-imbalance–aware metrics were used, including weighted precision, weighted recall, and weighted F1-score, in addition to overall accuracy. These weighted metrics compute class-wise scores and aggregate them according to class prevalence, thereby providing a more reliable assessment under imbalanced data conditions. 9. Inclusion of research flow diagrams Reviewer comment: Figures 4 and 5 are trivial and contribute nothing but to the repetition of the content of the previous paragraphs. Response: Thank you for this comment. Figures 2 and 3 (previously Figures 4 and 5) were included to comply with the STARD 2015 reporting guidelines, specifically the recommended flow of participants diagram. These figures provide a transparent visual summary of the study flow, including participant inclusion and data processing steps, which may not be fully conveyed through narrative text alone. 10. Addition of confidence intervals and diagnostic performance table Reviewer comment: The final results of the accuracy of clinical models are standard given in confidence intervals at least using typical statistical indicators: sensitivity, specificity, PPV and NPV. For better reception, it is worth listing them in the table. Thank you for this important comment. We have revised the manuscript to report the final diagnostic performance of the multimodal ResNet50–MPNet model using standard clinical metrics, including accuracy, sensitivity, specificity, PPV, and NPV, each accompanied by 95% confidence intervals. The final accuracy of 98.28% (95% confidence interval = 96.93 - 99.65), sensitivity of 99.71% (95% confidence interval = 99.14 - 100), specificity of 97.21% (95% confidence interval = 95.01 - 99.41), Negative Predictive Value (NPV) 99.5% and Positive Predictive Value (PPV) 98.3%. These results are now summarized in a dedicated table entitled “Diagnostic Performance of the Multimodal ResNet50–MPNet Model” to improve clarity and clinical interpretability. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Krzywicki T. Peer Review Report For: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.5256/f1000research.186388.r446710) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-952/v1#referee-response-446710 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Sitaru S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 29 Oct 2025 | for Version 1 Sebastian Sitaru , Technical University of Munich, Munich, Munich, Germany 0 Views copyright © 2025 Sitaru S. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions In their manuscript, the authors describe the creation and validation of a multimodal image-text AI algorithm for the diagnosis of atopic dermatitis. The topic is highly relevant and the technical approach seems sound. The manuscript is overall well written and concise. It is commendable that the authors decided to share their source code. I have a couple of minor points which could be improved before Indexing: - In the methods it says, "Two board-certified dermatologists independently annotated all images". Do you mean all cases, so image+text? Or did they just annotate the images alone? - Was the skin tone of the patients recorded? Even if not, I think this point deserves a mention in the discussion Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Yes Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Data analysis and AI applications in dermatology I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (1) Author Response 09 Feb 2026 Alida Widiawaty, Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia Dear Sebastian Sitaru Technical University of Munich, Munich, Germany We thank the reviewer for the thorough evaluation of our manuscript and for the constructive comments that have substantially improved the clarity, transparency, and methodological rigor of the study. All points raised have been carefully addressed, and the manuscript has been revised accordingly. 1. Clarification of annotation scope Reviewer comment: In the methods it says, "Two board-certified dermatologists independently annotated all images". Do you mean all cases, so image + text? Or did they just annotate the images alone? Response: Thank you for this insightful comment. We have clarified this point in the manuscript to state that two board-certified dermatologists independently annotated both the clinical images and the corresponding text data for all cases. 2. Clarification of patients' skin tone Reviewer comment: Was the skin tone of the patients recorded? Even if not, I think this point deserves a mention in the discussion Response: Thank you for this comment. Skin tone was not explicitly recorded as a standalone variable in this study. However, the study population predominantly consisted of Indonesian patients, who generally fall within Fitzpatrick skin phototypes III–IV. Within this relatively homogeneous skin tone range, no systematic variation in clinical presentation or image-based model performance attributable to skin tone was observed. Therefore, skin tone was not considered a confounding factor influencing the study outcomes. Nonetheless, we acknowledge in the Discussion that external validation in cohorts with a broader range of Fitzpatrick skin types would be valuable to confirm the model’s robustness across more diverse populations. We have revised the manuscript in accordance with all the reviewers’ comments. Thank you once again for your constructive guidance. Please let us know if any further clarification or revisions are required. Warm regards, Alida Widiawaty Faculty of Medicine, Universitas Riau, Pekanbaru, 28133, Indonesia The Doctoral Program in Medical Sciences, Faculty of Medicine Universitas Indonesia, Jakarta, 10430, Indonesia View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Sitaru S. Peer Review Report For: Multimodal Machine Learning Approach for Diagnosing Atopic Dermatitis [version 1; peer review: 1 approved, 1 approved with reservations] . F1000Research 2025, 14 :952 ( https://doi.org/10.5256/f1000research.186388.r420623) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/14-952/v1#referee-response-420623 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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