Advances and limitations of artificial intelligence-assisted identification of pathogenic fungi

Objectives We developed and tested multiple computer-vision image classifiers, for their ability to identify a large set of common and rare pathogenic molds. Aim of the study was to create a comprehensive global benchmark towards the novel, emerging field of computer vision driven diagnostics of pathogenic microbes. If successfully implemented, a high-resource clinical setting could greatly benefit from this adjunct technique to supplement molecular sequencing and mass spectrometry driven methodologies, while in a low resource setting, it could provide enormous possibilities to enhance diagnostic precision in rural and remote geographies.

We selected 114 representative fungal pathogens represented by 123 strains obtained from the unique images implemented within the ‘Atlas of Clinical Fungi’, to serve as core dataset. The image classifiers were designed with a rigorous testing and evaluation strategy, at a yet unprecedented level of detail. We designed the framework, within the TENSORFLOW environment, testing multiple transfer-learning approaches, as well hybrid architectures comprising both, features of convolutional neural networks (CNN) and advanced vision transformers (ViT).

We achieved a global identification accuracy of > 88% for the validation partition with our best model (Test accu. 87%, Train. accu. 96%). Simulations indicated that extended training time would lead to further accuracy improvements, particularly with greater data richness. Our results also highlight complex de-black-boxing approaches in interpreting image classification, never shown for microbial computer vision diagnostics to date.

Besides quantitative limitations of representative strains per tested species, our approach reflects a significant scientific novelty to the field. While tested mainly on molds and a small subset of common bacteria as a control set, the methodology is universally applicable to yeasts and bacteria rendering the technique attractive for future diagnostics in the clinical setting. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study did not receive any funding Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data availability Images are available from the corresponding author upon reasonable collaborative request. [The code will be made available upon manuscript acceptance at https://github.com/BenjaminS81 and reasonable collaborative request].