Application of Morphogo based on convolutional neural network for morphological identification of bone marrow nucleated cells

Application of Morphogo based on convolutional neural network for morphological identification of bone marrow nucleated cells | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of Morphogo based on convolutional neural network for morphological identification of bone marrow nucleated cells Qiufang Zhang, Xiaofeng Zhang, Changhui Hua, Tingting Kong, Jingyuan Li, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4126940/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Introduction: The morphological examination of bone marrow (BM) cells, commonly used for diagnosing hematological diseases, heavily relies on the expertise of pathologists. This approach is time-consuming, labor-intensive, subjective, and lacks objectivity. Therefore, it is crucial to develop automated analysis systems to aid in the diagnosis of hematological diseases. Methods The BM smears from patients with hematological diseases were collected from Dian Diagnostics between September 2021 and December 2021. These smears were classified into five groups based on varying degrees of cell morphological alterations. Images of the BM nucleated cells were captured using the Morphogo system, and its performance in cell identification was compared with that of pathologists. Results The Morphogo system demonstrated a high performance in identifying BM nucleated cells, with a sensitivity of 0.9362, specificity of 0.9977, PPV of 0.8354, NPV of 0.9974, and accuracy of 0.9954. Comparison between the percentage of BM nucleated cells identified by the Morphogo system and pathologists showed almost perfect agreement, with an average Kappa value of 0.8695 for 25 cell classes. The practical utility of the Morphogo system was evaluated in hematological diseases, with pathologists achieving averaged sensitivity, specificity, PPV, NPV and accuracy ranging from 0.9098 to 0.9868 when using the system for disease diagnosis. The diagnostic results were consistent with those made by pathologists using a microscope, with an average Kappa value of 0.9096. Conclusion Morphogo system had the potential to assist pathologists in diagnosis of hematological diseases by improving the efficiency of identification of BM nucleated cells. Morphogo system Bone marrow nucleated cells Morphology Hematological diseases Figures Figure 1 Figure 2 1. Introduction The morphological examination of nucleated cells in bone marrow (BM) is a critical diagnostic procedure for hematological diseases, heavily reliant on the expertise of pathologists. However, this process is time-consuming and labor-intensive, potentially leading to delays in treatment initiation for patients [ 1 – 9 ]. Therefore, there is an urgent need for new technology to assist with this manual inspection. Convolutional neural network (CNN) is a type of feedforward neural network that incorporates convolution computation and depth structure [ 10 – 11 ]. As a deep learning algorithm, CNN has demonstrated outstanding performance in image recognition and feature extraction due to its unique hierarchical structure and parameter learning ability [ 8 , 12 ]. In clinicopathological examination, pathological image analysis plays a crucial role in disease diagnosis, and the utilization of CNN significantly enhances the accuracy and efficiency of this process [ 13 – 20 ]. However, due to the complexity of BM cell morphology, CNN is seldom utilized for nucleated cell recognition in BM. In this study, we collected BM smears from 207 patients with different blood diseases, developed the CNN-based Morphogo system, and demonstrated its effectiveness in the identification of BM nucleated cells and its clinical application value in assisting the diagnosis of hematologic diseases. 2. Methods 2.1 Sources and Classification of Sample This was a retrospective study in which 207 BM cases were collected from Dian Diagnostics between September 2021 and December 2021. The smears were categorized into five groups (G1 to G5) based on the degree of pathological and cell morphological changes, as recommended by pathologists. The diseases included in each group are as follows: G1 - acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), myelodysplastic syndrome transformation into acute leukemia (MDS-AL), chronic myeloid leukemia diagnosed in accelerated phase (CML-AP), and lymphoma; G2 - megaloblastic anemia (MA), plasma cell myeloma (PCM), myelodysplastic syndrome (MDS), chronic myeloid leukemia (CML), and myeloproliferative neoplasms (MPN); G3 - immune thrombocytopenia (ITP), chronic lymphoblastic leukemia (CLL) and other diseases with slight changes in cell morphology; G4 - iron deficiency anemia (IDA), secondary anemia (SA), secondary leukaemia (SL), and aplastic anaemia (AA); and G5 - BM dilution cases. All BM smears were well stained using the Wright-Giemsa method, and the quality of both the smear and staining met the recommendations outlined in the nation guide to clinical laboratory procedures (NGCLP, fourth edition) or by the international council for standardization in hematology (ICSH) [ 8 , 21 ]. The study received approval from the institutional review committee of all participating agencies. Detailed information on enrolled BM cases can be found in Table 1 . Table 1 Information statistics table of BM smears. Class of morphology Sample Number G1 MDS-AL 10 AML 16 ALL 5 CML-AP 2 Lymphoma 2 G2 MA 9 PCM 11 MDS 13 CML 4 MPN 5 G3 ITP 7 CLL 17 Others 18 G4 IDA 18 SA 7 SL 5 AA 2 Others 7 G5 Dilution cases 49 MDS-AL: myelodysplastic syndrome transformation into acute leukemia; AML: acute myeloid leukemia; ALL: acute lymphoblastic leukemia; CML-AP: chronic myeloid leukemia diagnosed in accelerated phase; MA: megaloblastic anemia; PCM: plasma cell myeloma; MDS: myelodysplastic syndrome; CML: chronic myeloid leukemia; MPN: myeloproliferative neoplasms; ITP: immune thrombocytopenia; CLL: chronic lymphoblastic leukemia; IDA: iron deficiency anemia; SA: secondary anemia; SL: secondary leukemia; AA: aplastic anemia. 2.2 CNN construction CNN consists of three parts: convolution layer, pooling layer and fully connected layer. The filter of the convolution layer alters or enhances cell images by strengthening or removing image features such as blurring, sharpening, embossing, and edge detection. Additionally, the pooling layer reduces data dimensionality, space size of image features, calculation amount and parameters in the neural network by combining output from neural clusters into a single neuron in the next layer. This also improves stability by controlling overfitting. After the system completes the work of the convolution layer and pooling layer, the fully connected layer is formatted. Then, fully connected layer extracts the advanced image features generated by the convolution layer and pooling layer and classifies the input cell images. Finally, the results of cell images classification are output as vector values. Each value represents the classification probability under different elements [ 14 , 22 – 24 ]. 2.3 Data digitization Morphogo system is a CNN-based artificial Intelligence (AI) system developed by Hangzhou Zhiwei Information and Technology Ltd that is used to perform differential count of BM nucleated cells automatically. After training with more than 2.8 million BM nucleated cells, Morphogo system can identify over 35 types of BM nucleated cells. The acquisition process of digital slides is as follows: (1) the whole slide imaging (WSI) is obtained by scanning the BM smears with a 40X objective lens; (2) the 100X objective lens is used to obtain detailed cell images in the region of interest (ROI), and then AI algorithms locate, segment and identify BM nucleated cells on the digital images; (3) the cell differential count results are automatically calculated for each slide. Morphogo system can classify and count up to 9999 BM nucleated cells per smear, which can satisfy the needs of laboratory work, research and education at the same time. 2.4 Morphogo system workflow The workflow by which pathologists and Morphogo system identify BM nucleated cells was illustrated in Fig. 1 . In a manual morphology examination, pathologists classified and counted 200–500 nucleated cells on every BM smear, then issued a cytology report based on morphology information as well as other clinical information such as CBC and medical history. In an automated morphology assessment using Morphogo system, the system firstly scanned the BM smear to obtain a high-quality digital smear and BM cell images in ROI, then the CNN embedded in Morphogo system was used to locate, segment and identify BM cells in the images. The acquisition process was fully automated. Then, more than three pathologists reviewed the digital BM smears and proofread the results of cell pre-classification based on AI on a computer monitor. If cells were mistakenly identified by Morphogo system and they would be reclassified by pathologists. Finally, Morphogo system automatically calculated the proportion of each BM cell type and the degree of hyperplasia of each BM smear, and then generated a morphology report. 2.5 Morphogo system evaluation To evaluate the cell classification performance of Morphogo system in different hematological disease, the BM nucleated cells were annotated into 25 categories: proerythroblast, early erythroblast, intermediate erythroblast, later erythroblast, myeloblast, promyelocyte, neutrophilic myelocyte, neutrophilic metamyelocyte, band neutrophil, segmented neutrophil, eosinophilic myelocyte, eosinophilic metamyelocyte, band eosinophil, segmented eosinophil, basophil, monoblast, promonocyte, monocyte, lymphoblast, prolymphocyte, mature lymphocyte, plasmablast, immature plasma, plasma cell and others including smudge cell, histocyte and mast cell according to WHO classification. Cell classification performance was evaluated in terms of sensitively, specificity, positive predict value (PPV), negative predict value (NPV) and accuracy [ 25 – 27 ]. Meanwhile, we collected the results of manual microscope examination of all BM smears and other clinical examination results of all cases and compared the kappa values for evaluating the agreement of pathologists using Morphogo system and manual microscope in diseases diagnosis. 2.6 Statistical analysis Excel version 2016 was utilized for the analysis of sensitivity, specificity, PPV, NPV and accuracy of Morphogo’s cell classification, assuming pathologists’ annotations as the gold standard. The correlations of cell proportions were visualized using GraphPad Prism 7. Kappa was calculated using SPSS 20 to assess consistency [ 28 – 29 ]. In this study, the calculation formulas were as follows (TP: true positive; TN: true negative; FP: false positive; FN: false negative): $$Specificity=\frac{\text{T}N}{TN+FP}*100\%$$ $$PPV=\frac{\text{T}P}{\text{T}P+FP}*100\%$$ $$NPV=\frac{\text{T}N}{\text{F}N+TN}*100\%$$ $$Accuracy=\frac{\text{T}P+TN}{\text{T}P+FP+TN+FN}*100\%$$ 3. Results 3.1 The Morphogo system is capable of accurately identifying BM nucleated cells The 68,610 high-resolution digital images of BM nucleated cells captured by the Morphogo system were segmented into 25 groups and annotated. To assess the performance of the Morphogo system in classifying cells across various pathological conditions, it was applied to a dataset encompassing 19 types of hematological diseases in patient cases. The evaluation metrics for each disease condition are summarized in Table 2 . The Morphogo system demonstrated a sensitivity exceeding 0.7995 in classifying BM nucleated cells, with an average sensitivity of 0.9362. The PPV varied significantly among different cell types, ranging from 0.3698 to 0.9991, and exceeding 0.95 for promyelocyte, neutrophil, erythroid cells, mature lymphocyte, and other cell types. Conversely, the PPV for eosinophil, lymphocyte, prolymphocyte, monoblast, promonocyte, plasmablast and immature plasma was below 0.80. The NPV remained consistently high at a range of 0.9745 to 1.0000 across all classes of BM nucleated cells with eosinophilic myelocyte and monoblast exhibiting an NPV of 1.0000. The accuracy of the Morphogo system in classifying BM nucleated cells was remarkably high at a range from 0.9713 to 0.9999 with an average accuracy of 0.9954. Table 2 Statistics of Morphogo system to classify BM nucleated cells. Class of cells Sensitivity Specificity PPV NPV Accuracy Myeloblast 0.9533 0.9960 0.8542 0.9989 0.9950 Promyelocyte 0.9689 0.9998 0.9851 0.9995 0.9993 Neutrophilic myelocyte 0.9509 0.9995 0.9915 0.9971 0.9968 Neutrophilic metamyelocyte 0.9263 0.9997 0.9953 0.9945 0.9945 Band neutrophil 0.9787 0.9998 0.9978 0.9979 0.9979 Segmented neutrophil 0.9742 0.9999 0.9991 0.9960 0.9964 Eosinophilic myelocyte 0.9880 0.9988 0.7440 1.0000 0.9987 Eosinophilic metamyelocyte 0.9950 0.9949 0.7428 0.9999 0.9949 Band eosinophil 0.8814 0.9998 0.7536 0.9999 0.9997 Segmented eosinophil 0.9447 0.9972 0.7341 0.9995 0.9967 Basophil 0.8921 0.9994 0.8851 0.9994 0.9988 Proerythroblast 0.8791 0.9999 0.9524 0.9998 0.9998 Early erythroblast 0.8963 0.9998 0.9629 0.9994 0.9992 Intermediate erythroblast 0.9708 0.9976 0.9760 0.9970 0.9951 Later erythroblast 0.9755 0.9991 0.9898 0.9978 0.9971 Lymphoblast 0.9848 0.9937 0.6015 0.9999 0.9936 Prolymphocyte 0.7995 0.9917 0.3698 0.9988 0.9906 Mature lymphocyte 0.8373 0.9996 0.9975 0.9668 0.9713 Monoblast 0.9972 0.9978 0.7084 1.0000 0.9978 Promonocyte 0.8735 0.9959 0.5648 0.9992 0.9951 Monocyte 0.8257 0.9976 0.8860 0.9961 0.9938 Plasmablast 1.0000 0.9999 0.6316 1.0000 0.9999 Immature plasma 0.9980 0.9866 0.6923 0.9999 0.9869 Plasma cell 0.9137 0.9981 0.8729 0.9987 0.9969 Others 0.9992 0.9995 0.9967 0.9999 0.9995 3.2 The Morphogo system and the pathologist are extremely consistent in identifying BM nucleated cells The Morphogo system was subjected to correlation and consistency analysis in order to enhance our understanding of its concordance with pathologists in the classification and enumeration of BM nucleated cells. Table 3 Evaluation of the consistence of BM nucleated cells between Morphogo system pre-classification and manual proofreading in terms of Cohen kappa coefficient. Class of cells Kappa P value Myeloblast 0.8980 0.0000 Promyelocyte 0.9770 0.0000 Neutrophilic myelocyte 0.9690 0.0000 Neutrophilic metamyelocyte 0.9310 0.0000 Band neutrophil 0.9870 0.0000 Segmented neutrophil 0.9840 0.0000 Eosinophilic myelocyte 0.8480 0.0000 Eosinophilic metamyelocyte 0.8480 0.0000 Band eosinophil 0.8120 0.0000 Segmented eosinophil 0.8250 0.0000 Basophil 0.8880 0.0000 Proerythroblast 0.9140 0.0000 Early erythroblast 0.9280 0.0000 Intermediate erythroblast 0.9710 0.0000 Later erythroblast 0.9810 0.0000 Lymphoblast 0.7440 0.0000 Prolymphocyte 0.5020 0.0000 Mature lymphocyte 0.8930 0.0000 Monoblast 0.8270 0.0000 Promonocyte 0.6840 0.0000 Monocyte 0.8520 0.0000 Plasmablast 0.7740 0.0000 Immature plasma 0.8110 0.0000 Plasma cell 0.8910 0.0000 Others 0.9980 0.0000 The results revealed a robust positive correlation between the Morphogo system and pathologists in classifying 25 BM cell types, with a P-value of less than 0.001 and a mean Kappa value of 0.8695 (Fig. 2 , Table 3 ). It is worth noting that the Morphogo system exhibits limited capability in identifying prolymphocytes, as indicated by a Kappa value of only 0.5020 (Table 3 ). Overall, the findings from both correlation and consistency analyses strongly support substantial agreement between the Morphogo system and pathologists in accurately identifying BM nucleated cells. 3.3 The application of Morphogo system can effectively diagnose hematological system diseases. To further validate the diagnostic utility of the Morphogo system in hematological diseases, we compared the morphological diagnoses made by pathologists utilizing the Morphogo system with the original cytology diagnoses, treating the latter as the gold standard. Table 4 Statistics of Morphogo system to hematological diseases diagnosis with the changes of cells morphologies. Class of morphology Sensitivity Specificity PPV NPV Accuracy G1 0.8857 1.0000 1.0000 0.9773 0.9807 G2 0.8810 0.9939 0.9737 0.9704 0.9710 G3 0.9048 0.9697 0.8837 0.9756 0.9565 G4 0.8974 0.9702 0.8750 0.9760 0.9565 G5 0.9800 1.0000 1.0000 0.9937 1.0000 Based on the data in Table 4 , the Morphogo system exhibited sensitivities ranging from 0.8810 to 0.8974 in G1, G2, and G4 groups, slightly lower than those in G3 and G5. It is worth noting that the system demonstrated high specificities exceeding 0.95 across all groups, reaching a peak of 1.0000 in G1 and G5. The PPV in G1 and G5 was perfect at 1.0000, with an average of 0.9465. The NPVs consistently exceeded 0.95, with a value of 0.9937 reached in group G5 specifically. Furthermore, the accuracy of the Morphogo system was also high, surpassing 0.95 in all groups and peaking at a perfect score of 1.0000 in group G5. Additionally, Table 5 presents the results of the concordance analysis between the Morphogo system and microscopy, further confirming its effectiveness in diagnosing hematological diseases. Table 5 Evaluation of the consistence of hematological diseases between Morphogo system and microscope in terms of Cohen kappa coefficient. Class of morphology Kappa P value G1 0.9280 0.0000 G2 0.9070 0.0000 G3 0.8670 0.0000 G4 0.8590 0.0000 G5 1.0000 0.0000 4. Discussion CNN model trained by large datasets has been proved to be able to achieve human-level ability in identification and classification of various medical images, such as detecting the classification of diabetic retinopathy[ 8 , 27 , 30 ]. However, there is not an AI-based device that is currently in use of BM morphology assessment in clinical laboratories. The morphology of BM nucleated cells varied in different pathological conditions, thus the Morphogo system’s analytical performance was evaluated in 19 types of hematological diseases. Due to small numbers of cases in certain pathological conditions, the BM smears were divided into five groups (G1-G5) according to degrees of cell morphology changes. In this study, the G1 was made up of leukemia with a significantly increased of blasts in the BM including MDS-AL, AML, ALL, CML-AP and malignant lymphoma [ 31 – 33 ]. The G2 consisted of MA, PCM, MDS, CML and MPN. Cells in these diseases has moderate or obvious morphological changes. MA is characterized by the presence of megaloblast in BM due to mature asynchronously between nucleus and cytoplasm [ 34 ]. Plasma cells in PCM are highly pleomorphic, such as diffuse sheet growth pattern and immature cell morphology[ 2 ]. MDS or MPN is usually characterized by dysplasia in one or more myeloid lineages in BM [ 35 , 36 ]. CML is a kind of cancer with an uncontrolled increased in the number of myeloid cells [ 37 ]. Hematological diseases in G3 were accompanied by mild changes in BM nucleated cell morphology, including ITP and CLL. ITP is an acquired autoimmune disease with increased platelet destruction and decrease platelet production [ 38 ]. B lymphocytes debris is a morphology feature of CLL [ 39 ]. IDA, SA, SL, and AA were classified into G4, and these diseases are basically not accompanied by cell morphological changes [ 40 – 42 ]. The G5 was dilution cases. The diagnostic results that pathologists made based on Morphogo system’s morphological findings were compared with the original cytology diagnoses based on manual microscopic examination. Morphogo system displayed a good ability to assist diagnosis in all groups (G1-G5), which were higher than those of prior studies reported [ 1 , 13 , 23 , 25 , 43 ]. Furthermore, the diagnoses made by pathologists using Morphogo system were consistent with the diagnoses of microscopic examination, as evidenced by the Kappa value of more than 0.85. It showed that Morphogo system is a useful tool for diagnosis of hematological diseases. The study provided doctors with a potential way to apply AI to the morphology examination of BM smears. However, as the previous research reported, even for experienced pathologists, subtle differences between cells with similar morphological characteristics are difficult to identify [ 27 ]. This may explain why the sensitivity and PPV performance were not good in the identification of prolymphocyte, but the PPV of prolymphocyte and its similar cells was up to 1.0000. Furthermore, the image quality of BM nucleated cells depends on several factors, including the quality of BM smear preparation, the pathological condition and the imaging process [ 13 ]. This may be another cause of inaccurately identification of BM cells. The morphology of blasts in AL of G1 is more uniform, while they are relatively polymorphic and malformed in MDS of G2 [ 13 ]. Therefore, the blasts are easier to be identified and classified in AL, and difficult to be identified in MDS, which might be the cause of the higher misdiagnosis rate in some cases of Morphogo system compared with pathologists. Pathologists using Morphogo system made the diagnosis results of 16 cases were inconsistent with that made by conventional microscopes. Since the pathologists also refer to the results of other diagnostic tests when using microscope, such as flow cytometry and BM biopsy to diagnose disease. However, due to the massive number of BM samples every working day and the fact that BM cell differential count is so laborious and time-consuming, in some laboratories, while using Morphogo system they can review AI-based cell differential count results on the computer screen, which will dramatically improve the efficiency of laboratory work. We can imagine that Morphogo system can obtain and analyze more cells in a shorter period of time, and pathologists can review more cells to prevent some critical morphological changes from being missed, thus reducing the misdiagnosis rate and missed diagnosis rate. Meanwhile, it can help hospitals in rural area that lacks pathologists and save time for pathologists. In this study, we used a single center method, in which all BM smears were prepared in the same laboratory and digitally processed. However, this study still had some limitations. The study was limited to 19 common diseases, and there were not enough number of cases for all of them. For example, there were only 4 cases of CML, 2 cases of CML-AP and lymphoma, so it was uncertain whether Morphogo system’s AI could have the same performance in all kinds of common hemato-pathological diseases and in even more rare conditions. In the future, more studies can be carried out with larger number of BM samples that consist of more types of hematological diseases from multiple laboratories to further validate the BM cell identification performance of Morphogo system. 5. Conclusion The CNN-based Morphogo system can realize the recognition and classification of BM nucleated cells with high precision, thus providing a good platform for auxiliary pathologists to diagnose hematological diseases with high efficiency. Declarations Ethics approval and consent to participate The use of human samples was approved by the Ethics Committee of Hangzhou D.A. Medical Laboratory. Since BM aspirate smears in this retrospective study have been used in clinical examination, the informed consent of patients was exempted. Consent for publication Not applicable. Availability of data and materials The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Competing Interests All authors are employed by Hangzhou D.A. Medical Laboratory . Funding The authors did not receive support from any organization for the submitted work. Authors’ contributions Conceptualization: Qiufang Zhang; Methodology: Jingyuan Li and Yuan Peng; Formal analysis and investigation: Xiaofeng Zhang, Changhui Hua and Tingting Kong; Writing - original draft preparation: Qiufang Zhang; Writing - review and editing: Jingyan Wu; Resources: Yan Chen; Supervision: Yan Chen. 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Comput Med Imaging Graph 90:101912. https://doi.org/10.1016/j.compmedimag.2021.101912 Additional Declarations Competing interest reported. All authors are employed by Hangzhou D.A. Medical Laboratory. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4126940","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":286210329,"identity":"49125d1d-1238-4615-bec1-7ba82308b614","order_by":0,"name":"Qiufang Zhang","email":"","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Qiufang","middleName":"","lastName":"Zhang","suffix":""},{"id":286210330,"identity":"de30fecf-c900-4f9f-82b0-769b9082305e","order_by":1,"name":"Xiaofeng Zhang","email":"","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Xiaofeng","middleName":"","lastName":"Zhang","suffix":""},{"id":286210331,"identity":"de848e4c-0895-4f02-82bc-690aa67a0818","order_by":2,"name":"Changhui Hua","email":"","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Changhui","middleName":"","lastName":"Hua","suffix":""},{"id":286210332,"identity":"b57f97a5-37ba-48ea-a760-754eaf7bc2e0","order_by":3,"name":"Tingting Kong","email":"","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Tingting","middleName":"","lastName":"Kong","suffix":""},{"id":286210333,"identity":"4773a01e-d15d-4304-b5bb-d31736528458","order_by":4,"name":"Jingyuan Li","email":"","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Jingyuan","middleName":"","lastName":"Li","suffix":""},{"id":286210334,"identity":"54db837d-000c-4e30-9917-ef2877945e41","order_by":5,"name":"Yuan Peng","email":"","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"Peng","suffix":""},{"id":286210335,"identity":"10b01330-80bc-4378-839c-07a83335dd86","order_by":6,"name":"Jingyan Wu","email":"","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Jingyan","middleName":"","lastName":"Wu","suffix":""},{"id":286210336,"identity":"bd644ad6-b4f8-4be8-9816-c8a9c80a1bce","order_by":7,"name":"Yan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACe2YGNhAtx8befoA4LYbtDWAtxnw8ZxKI02Jw5gBYS+I8CQcDIrXcSH724OOOw+ltEgwJDD8qthGjJc3ccOaZw7lt0o0HGHvO3CZGSw6bNG8bUIvMgQRmxjZitNx/wyb9t+1wOptEggGRWkC2MLYdTiBei+GMNDPJ3rZ0wzZgIB8kyi/2EsnPJH62WcvLt7cffPCjgggtUNAMJg8QrR4I6khRPApGwSgYBSMNAABMIz5X31xQCgAAAABJRU5ErkJggg==","orcid":"","institution":"Hangzhou D.A. Medical Laboratory","correspondingAuthor":true,"prefix":"","firstName":"Yan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-03-19 03:29:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4126940/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4126940/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54108340,"identity":"5cc33161-0f1d-44f7-8f86-fac950bde45d","added_by":"auto","created_at":"2024-04-04 17:34:46","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":176817,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIllustrative Study Design for Manual Microscope and Morphogo System Analysis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4126940/v1/1903d2d223670dd27d1c8ee7.png"},{"id":54108341,"identity":"ffb9e68c-b573-4383-8f11-dfa629302ae8","added_by":"auto","created_at":"2024-04-04 17:34:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":415319,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePresents the correlation between the cell counts obtained through the Morphogo system's pre-classification and those verified through manual proofreading. A-Y display scatter plots with linear regression lines, representing the percentage of BM nucleated cells after conducting paired counts of BM smears from 207 patients.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4126940/v1/6809cbe7642635aea8c92a4a.png"},{"id":77974051,"identity":"b5b68acf-c522-4701-a607-a4bf2d7ce982","added_by":"auto","created_at":"2025-03-07 11:24:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1727729,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4126940/v1/6cada9de-b851-4d15-b4fd-bcdfefda5c80.pdf"}],"financialInterests":"Competing interest reported. All authors are employed by Hangzhou D.A. Medical Laboratory.","formattedTitle":"Application of Morphogo based on convolutional neural network for morphological identification of bone marrow nucleated cells","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe morphological examination of nucleated cells in bone marrow (BM) is a critical diagnostic procedure for hematological diseases, heavily reliant on the expertise of pathologists. However, this process is time-consuming and labor-intensive, potentially leading to delays in treatment initiation for patients [\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, there is an urgent need for new technology to assist with this manual inspection.\u003c/p\u003e \u003cp\u003eConvolutional neural network (CNN) is a type of feedforward neural network that incorporates convolution computation and depth structure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. As a deep learning algorithm, CNN has demonstrated outstanding performance in image recognition and feature extraction due to its unique hierarchical structure and parameter learning ability [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In clinicopathological examination, pathological image analysis plays a crucial role in disease diagnosis, and the utilization of CNN significantly enhances the accuracy and efficiency of this process [\u003cspan additionalcitationids=\"CR14 CR15 CR16 CR17 CR18 CR19\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. However, due to the complexity of BM cell morphology, CNN is seldom utilized for nucleated cell recognition in BM.\u003c/p\u003e \u003cp\u003eIn this study, we collected BM smears from 207 patients with different blood diseases, developed the CNN-based Morphogo system, and demonstrated its effectiveness in the identification of BM nucleated cells and its clinical application value in assisting the diagnosis of hematologic diseases.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Sources and Classification of Sample\u003c/h2\u003e\n \u003cp\u003eThis was a retrospective study in which 207 BM cases were collected from Dian Diagnostics between September 2021 and December 2021. The smears were categorized into five groups (G1 to G5) based on the degree of pathological and cell morphological changes, as recommended by pathologists. The diseases included in each group are as follows: G1 - acute myeloid leukemia (AML), acute lymphoblastic leukemia (ALL), myelodysplastic syndrome transformation into acute leukemia (MDS-AL), chronic myeloid leukemia diagnosed in accelerated phase (CML-AP), and lymphoma; G2 - megaloblastic anemia (MA), plasma cell myeloma (PCM), myelodysplastic syndrome (MDS), chronic myeloid leukemia (CML), and myeloproliferative neoplasms (MPN); G3 - immune thrombocytopenia (ITP), chronic lymphoblastic leukemia (CLL) and other diseases with slight changes in cell morphology; G4 - iron deficiency anemia (IDA), secondary anemia (SA), secondary leukaemia (SL), and aplastic anaemia (AA); and G5 - BM dilution cases. All BM smears were well stained using the Wright-Giemsa method, and the quality of both the smear and staining met the recommendations outlined in the nation guide to clinical laboratory procedures (NGCLP, fourth edition) or by the international council for standardization in hematology (ICSH) [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The study received approval from the institutional review committee of all participating agencies. Detailed information on enrolled BM cases can be found in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eInformation statistics table of BM smears.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClass of morphology\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDS-AL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCML-AP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMDS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCML\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMPN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eITP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCLL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDilution cases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eMDS-AL: myelodysplastic syndrome transformation into acute leukemia; AML: acute myeloid leukemia; ALL: acute lymphoblastic leukemia; CML-AP: chronic myeloid leukemia diagnosed in accelerated phase; MA: megaloblastic anemia; PCM: plasma cell myeloma; MDS: myelodysplastic syndrome; CML: chronic myeloid leukemia; MPN: myeloproliferative neoplasms; ITP: immune thrombocytopenia; CLL: chronic lymphoblastic leukemia; IDA: iron deficiency anemia; SA: secondary anemia; SL: secondary leukemia; AA: aplastic anemia.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 CNN construction\u003c/h2\u003e\n \u003cp\u003eCNN consists of three parts: convolution layer, pooling layer and fully connected layer. The filter of the convolution layer alters or enhances cell images by strengthening or removing image features such as blurring, sharpening, embossing, and edge detection. Additionally, the pooling layer reduces data dimensionality, space size of image features, calculation amount and parameters in the neural network by combining output from neural clusters into a single neuron in the next layer. This also improves stability by controlling overfitting. After the system completes the work of the convolution layer and pooling layer, the fully connected layer is formatted. Then, fully connected layer extracts the advanced image features generated by the convolution layer and pooling layer and classifies the input cell images. Finally, the results of cell images classification are output as vector values. Each value represents the classification probability under different elements [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Data digitization\u003c/h2\u003e\n \u003cp\u003eMorphogo system is a CNN-based artificial Intelligence (AI) system developed by Hangzhou Zhiwei Information and Technology Ltd that is used to perform differential count of BM nucleated cells automatically. After training with more than 2.8\u0026nbsp;million BM nucleated cells, Morphogo system can identify over 35 types of BM nucleated cells. The acquisition process of digital slides is as follows: (1) the whole slide imaging (WSI) is obtained by scanning the BM smears with a 40X objective lens; (2) the 100X objective lens is used to obtain detailed cell images in the region of interest (ROI), and then AI algorithms locate, segment and identify BM nucleated cells on the digital images; (3) the cell differential count results are automatically calculated for each slide. Morphogo system can classify and count up to 9999 BM nucleated cells per smear, which can satisfy the needs of laboratory work, research and education at the same time.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Morphogo system workflow\u003c/h2\u003e\n \u003cp\u003eThe workflow by which pathologists and Morphogo system identify BM nucleated cells was illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. In a manual morphology examination, pathologists classified and counted 200\u0026ndash;500 nucleated cells on every BM smear, then issued a cytology report based on morphology information as well as other clinical information such as CBC and medical history. In an automated morphology assessment using Morphogo system, the system firstly scanned the BM smear to obtain a high-quality digital smear and BM cell images in ROI, then the CNN embedded in Morphogo system was used to locate, segment and identify BM cells in the images. The acquisition process was fully automated. Then, more than three pathologists reviewed the digital BM smears and proofread the results of cell pre-classification based on AI on a computer monitor. If cells were mistakenly identified by Morphogo system and they would be reclassified by pathologists. Finally, Morphogo system automatically calculated the proportion of each BM cell type and the degree of hyperplasia of each BM smear, and then generated a morphology report.\u003c/p\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Morphogo system evaluation\u003c/h2\u003e\n \u003cp\u003eTo evaluate the cell classification performance of Morphogo system in different hematological disease, the BM nucleated cells were annotated into 25 categories: proerythroblast, early erythroblast, intermediate erythroblast, later erythroblast, myeloblast, promyelocyte, neutrophilic myelocyte, neutrophilic metamyelocyte, band neutrophil, segmented neutrophil, eosinophilic myelocyte, eosinophilic metamyelocyte, band eosinophil, segmented eosinophil, basophil, monoblast, promonocyte, monocyte, lymphoblast, prolymphocyte, mature lymphocyte, plasmablast, immature plasma, plasma cell and others including smudge cell, histocyte and mast cell according to WHO classification. Cell classification performance was evaluated in terms of sensitively, specificity, positive predict value (PPV), negative predict value (NPV) and accuracy [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Meanwhile, we collected the results of manual microscope examination of all BM smears and other clinical examination results of all cases and compared the kappa values for evaluating the agreement of pathologists using Morphogo system and manual microscope in diseases diagnosis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eExcel version 2016 was utilized for the analysis of sensitivity, specificity, PPV, NPV and accuracy of Morphogo\u0026rsquo;s cell classification, assuming pathologists\u0026rsquo; annotations as the gold standard. The correlations of cell proportions were visualized using GraphPad Prism 7. Kappa was calculated using SPSS 20 to assess consistency [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eIn this study, the calculation formulas were as follows (TP: true positive; TN: true negative; FP: false positive; FN: false negative):\u003c/p\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"https://myfiles.space/user_files/122228_c8a1650c59388082/122228_custom_files/img1712220393.png\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$Specificity=\\frac{\\text{T}N}{TN+FP}*100\\%$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$PPV=\\frac{\\text{T}P}{\\text{T}P+FP}*100\\%$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$NPV=\\frac{\\text{T}N}{\\text{F}N+TN}*100\\%$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$Accuracy=\\frac{\\text{T}P+TN}{\\text{T}P+FP+TN+FN}*100\\%$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The Morphogo system is capable of accurately identifying BM nucleated cells\u003c/h2\u003e \u003cp\u003eThe 68,610 high-resolution digital images of BM nucleated cells captured by the Morphogo system were segmented into 25 groups and annotated. To assess the performance of the Morphogo system in classifying cells across various pathological conditions, it was applied to a dataset encompassing 19 types of hematological diseases in patient cases. The evaluation metrics for each disease condition are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe Morphogo system demonstrated a sensitivity exceeding 0.7995 in classifying BM nucleated cells, with an average sensitivity of 0.9362. The PPV varied significantly among different cell types, ranging from 0.3698 to 0.9991, and exceeding 0.95 for promyelocyte, neutrophil, erythroid cells, mature lymphocyte, and other cell types. Conversely, the PPV for eosinophil, lymphocyte, prolymphocyte, monoblast, promonocyte, plasmablast and immature plasma was below 0.80. The NPV remained consistently high at a range of 0.9745 to 1.0000 across all classes of BM nucleated cells with eosinophilic myelocyte and monoblast exhibiting an NPV of 1.0000. The accuracy of the Morphogo system in classifying BM nucleated cells was remarkably high at a range from 0.9713 to 0.9999 with an average accuracy of 0.9954.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of Morphogo system to classify BM nucleated cells.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass of cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyeloblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8542\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9950\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePromyelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophilic myelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9968\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophilic metamyelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand neutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9979\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented neutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9964\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophilic myelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophilic metamyelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9949\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand eosinophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented eosinophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9967\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8921\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProerythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9524\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly erythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate erythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLater erythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9936\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProlymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9917\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9906\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMature lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonoblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.7084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePromonocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.5648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9951\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8860\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasmablast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmature plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6923\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9869\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9969\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9995\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The Morphogo system and the pathologist are extremely consistent in identifying BM nucleated cells\u003c/h2\u003e \u003cp\u003eThe Morphogo system was subjected to correlation and consistency analysis in order to enhance our understanding of its concordance with pathologists in the classification and enumeration of BM nucleated cells.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of the consistence of BM nucleated cells between Morphogo system pre-classification and manual proofreading in terms of Cohen kappa coefficient.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass of cells\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMyeloblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePromyelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9770\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophilic myelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9690\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophilic metamyelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand neutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented neutrophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophilic myelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEosinophilic metamyelocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBand eosinophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSegmented eosinophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasophil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProerythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEarly erythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntermediate erythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLater erythroblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphoblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProlymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.5020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMature lymphocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonoblast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePromonocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocyte\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasmablast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.7740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImmature plasma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlasma cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOthers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9980\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results revealed a robust positive correlation between the Morphogo system and pathologists in classifying 25 BM cell types, with a P-value of less than 0.001 and a mean Kappa value of 0.8695 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). It is worth noting that the Morphogo system exhibits limited capability in identifying prolymphocytes, as indicated by a Kappa value of only 0.5020 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, the findings from both correlation and consistency analyses strongly support substantial agreement between the Morphogo system and pathologists in accurately identifying BM nucleated cells.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The application of Morphogo system can effectively diagnose hematological system diseases.\u003c/h2\u003e \u003cp\u003eTo further validate the diagnostic utility of the Morphogo system in hematological diseases, we compared the morphological diagnoses made by pathologists utilizing the Morphogo system with the original cytology diagnoses, treating the latter as the gold standard.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistics of Morphogo system to hematological diseases diagnosis with the changes of cells morphologies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass of morphology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNPV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9807\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8810\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.9737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9710\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9760\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9565\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eBased on the data in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the Morphogo system exhibited sensitivities ranging from 0.8810 to 0.8974 in G1, G2, and G4 groups, slightly lower than those in G3 and G5. It is worth noting that the system demonstrated high specificities exceeding 0.95 across all groups, reaching a peak of 1.0000 in G1 and G5. The PPV in G1 and G5 was perfect at 1.0000, with an average of 0.9465. The NPVs consistently exceeded 0.95, with a value of 0.9937 reached in group G5 specifically. Furthermore, the accuracy of the Morphogo system was also high, surpassing 0.95 in all groups and peaking at a perfect score of 1.0000 in group G5. Additionally, Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the concordance analysis between the Morphogo system and microscopy, further confirming its effectiveness in diagnosing hematological diseases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eEvaluation of the consistence of hematological diseases between Morphogo system and microscope in terms of Cohen kappa coefficient.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClass of morphology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKappa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.9070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.8590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.0000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.0000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCNN model trained by large datasets has been proved to be able to achieve human-level ability in identification and classification of various medical images, such as detecting the classification of diabetic retinopathy[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, there is not an AI-based device that is currently in use of BM morphology assessment in clinical laboratories.\u003c/p\u003e \u003cp\u003eThe morphology of BM nucleated cells varied in different pathological conditions, thus the Morphogo system\u0026rsquo;s analytical performance was evaluated in 19 types of hematological diseases. Due to small numbers of cases in certain pathological conditions, the BM smears were divided into five groups (G1-G5) according to degrees of cell morphology changes. In this study, the G1 was made up of leukemia with a significantly increased of blasts in the BM including MDS-AL, AML, ALL, CML-AP and malignant lymphoma [\u003cspan additionalcitationids=\"CR32\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The G2 consisted of MA, PCM, MDS, CML and MPN. Cells in these diseases has moderate or obvious morphological changes. MA is characterized by the presence of megaloblast in BM due to mature asynchronously between nucleus and cytoplasm [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Plasma cells in PCM are highly pleomorphic, such as diffuse sheet growth pattern and immature cell morphology[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. MDS or MPN is usually characterized by dysplasia in one or more myeloid lineages in BM [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. CML is a kind of cancer with an uncontrolled increased in the number of myeloid cells [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Hematological diseases in G3 were accompanied by mild changes in BM nucleated cell morphology, including ITP and CLL. ITP is an acquired autoimmune disease with increased platelet destruction and decrease platelet production [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. B lymphocytes debris is a morphology feature of CLL [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. IDA, SA, SL, and AA were classified into G4, and these diseases are basically not accompanied by cell morphological changes [\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The G5 was dilution cases. The diagnostic results that pathologists made based on Morphogo system\u0026rsquo;s morphological findings were compared with the original cytology diagnoses based on manual microscopic examination. Morphogo system displayed a good ability to assist diagnosis in all groups (G1-G5), which were higher than those of prior studies reported [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Furthermore, the diagnoses made by pathologists using Morphogo system were consistent with the diagnoses of microscopic examination, as evidenced by the Kappa value of more than 0.85. It showed that Morphogo system is a useful tool for diagnosis of hematological diseases.\u003c/p\u003e \u003cp\u003e The study provided doctors with a potential way to apply AI to the morphology examination of BM smears. However, as the previous research reported, even for experienced pathologists, subtle differences between cells with similar morphological characteristics are difficult to identify [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This may explain why the sensitivity and PPV performance were not good in the identification of prolymphocyte, but the PPV of prolymphocyte and its similar cells was up to 1.0000. Furthermore, the image quality of BM nucleated cells depends on several factors, including the quality of BM smear preparation, the pathological condition and the imaging process [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This may be another cause of inaccurately identification of BM cells. The morphology of blasts in AL of G1 is more uniform, while they are relatively polymorphic and malformed in MDS of G2 [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Therefore, the blasts are easier to be identified and classified in AL, and difficult to be identified in MDS, which might be the cause of the higher misdiagnosis rate in some cases of Morphogo system compared with pathologists. Pathologists using Morphogo system made the diagnosis results of 16 cases were inconsistent with that made by conventional microscopes. Since the pathologists also refer to the results of other diagnostic tests when using microscope, such as flow cytometry and BM biopsy to diagnose disease. However, due to the massive number of BM samples every working day and the fact that BM cell differential count is so laborious and time-consuming, in some laboratories, while using Morphogo system they can review AI-based cell differential count results on the computer screen, which will dramatically improve the efficiency of laboratory work. We can imagine that Morphogo system can obtain and analyze more cells in a shorter period of time, and pathologists can review more cells to prevent some critical morphological changes from being missed, thus reducing the misdiagnosis rate and missed diagnosis rate. Meanwhile, it can help hospitals in rural area that lacks pathologists and save time for pathologists.\u003c/p\u003e \u003cp\u003eIn this study, we used a single center method, in which all BM smears were prepared in the same laboratory and digitally processed. However, this study still had some limitations. The study was limited to 19 common diseases, and there were not enough number of cases for all of them. For example, there were only 4 cases of CML, 2 cases of CML-AP and lymphoma, so it was uncertain whether Morphogo system\u0026rsquo;s AI could have the same performance in all kinds of common hemato-pathological diseases and in even more rare conditions. In the future, more studies can be carried out with larger number of BM samples that consist of more types of hematological diseases from multiple laboratories to further validate the BM cell identification performance of Morphogo system.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThe CNN-based Morphogo system can realize the recognition and classification of BM nucleated cells with high precision, thus providing a good platform for auxiliary pathologists to diagnose hematological diseases with high efficiency.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe use of human samples was approved by the Ethics Committee of Hangzhou D.A. Medical Laboratory. Since BM aspirate smears in this retrospective study have been used in clinical examination, the informed consent of patients was exempted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors are employed by \u003cstrong\u003eHangzhou D.A. Medical Laboratory\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Qiufang Zhang; Methodology: Jingyuan Li and Yuan Peng; Formal analysis and investigation: Xiaofeng Zhang, Changhui Hua and Tingting Kong; Writing - original draft preparation: Qiufang Zhang; Writing - review and editing: Jingyan Wu; Resources: Yan Chen; Supervision: Yan Chen.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHuang F, Guang P, Li F, Liu X, Zhang W, Huang W (2020) AML, ALL, and CML classification and diagnosis based on bone marrow cell morphology combined with convolutional neural network: A STARD compliant diagnosis research. 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Comput Med Imaging Graph 90:101912. https://doi.org/10.1016/j.compmedimag.2021.101912\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Morphogo system, Bone marrow nucleated cells, Morphology, Hematological diseases","lastPublishedDoi":"10.21203/rs.3.rs-4126940/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4126940/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eIntroduction:\u003c/h2\u003e \u003cp\u003eThe morphological examination of bone marrow (BM) cells, commonly used for diagnosing hematological diseases, heavily relies on the expertise of pathologists. This approach is time-consuming, labor-intensive, subjective, and lacks objectivity. Therefore, it is crucial to develop automated analysis systems to aid in the diagnosis of hematological diseases.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThe BM smears from patients with hematological diseases were collected from Dian Diagnostics between September 2021 and December 2021. These smears were classified into five groups based on varying degrees of cell morphological alterations. Images of the BM nucleated cells were captured using the Morphogo system, and its performance in cell identification was compared with that of pathologists.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe Morphogo system demonstrated a high performance in identifying BM nucleated cells, with a sensitivity of 0.9362, specificity of 0.9977, PPV of 0.8354, NPV of 0.9974, and accuracy of 0.9954. Comparison between the percentage of BM nucleated cells identified by the Morphogo system and pathologists showed almost perfect agreement, with an average Kappa value of 0.8695 for 25 cell classes. The practical utility of the Morphogo system was evaluated in hematological diseases, with pathologists achieving averaged sensitivity, specificity, PPV, NPV and accuracy ranging from 0.9098 to 0.9868 when using the system for disease diagnosis. The diagnostic results were consistent with those made by pathologists using a microscope, with an average Kappa value of 0.9096.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eMorphogo system had the potential to assist pathologists in diagnosis of hematological diseases by improving the efficiency of identification of BM nucleated cells.\u003c/p\u003e","manuscriptTitle":"Application of Morphogo based on convolutional neural network for morphological identification of bone marrow nucleated cells","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-04 17:34:38","doi":"10.21203/rs.3.rs-4126940/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a77dbfc-2a18-49f9-8e90-2d79fadf3ff7","owner":[],"postedDate":"April 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-07T11:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-04 17:34:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4126940","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4126940","identity":"rs-4126940","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}