A decision support system for the detection of cutaneous fungal infections using artificial intelligence
Abstract
Cutaneous fungal infections are one of the most common skin conditions, hence, the burden of determining fungal elements upon microscopic examination with periodic acid-Schiff (PAS) and Gomori methenamine silver (GMS) stains, is very time consuming. Despite some morphological variability posing challenges to training artificial intelligence (AI)-based solutions, these structures are favored potential targets, enabling the recruitment of promising AI-based technologies. Herein, we present a novel AI solution for identifying skin fungal infections, potentially providing a decision support system for pathologists. Skin biopsies of patients diagnosed with a cutaneous fungal infection at the Sheba Medical Center, Israel between 2014 and 2023, were used. Samples were stained with PAS and GMS and digitized by the Philips IntelliSite scanner. DeePathology® STUDIO fungal elements were annotated and deemed as ground truth data after an overall revision by two specialist pathologists. Subsequently, they were used to create an AI-based solution, which has been further validated in other regions of interests. The study participants were divided into two cohorts. In the first cohort, the overall sensitivity of the algorithm was 0.8, specificity 0.97, F1 score 0.78; in the second, the overall sensitivity of the algorithm was 0.93, specificity 0.99, F1 score 0.95. The results obtained are encouraging as proof of concept for an AI-based fungi detection algorithm. DeePathology® STUDIO can be employed as a decision support system for pathologists when diagnosing a cutaneous fungal infection using PAS and GMS stains, thereby, saving time and money.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Dependence on special stains meant there was less potential of shortening turnaround times and reducing the use of resources.
“Additionally, the dependence on special stains meant there was less potential of shortening turnaround times and reducing the use of resources [26,27,28,29,30,31,32].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [27]
There is potential for using AI models for the detection of fungal organisms, as already reported in other fields of histopathology.
“Hence, there is potential for using AI models for the detection of fungal organisms, as already reported in other fields of histopathology [27,28,29,30,31].”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [27]
Cites
Fungal keratitis: An overview of clinical and laboratory aspects
Mahmoudi, S., Masoomi, A., Ahmadikia, K., Tabatabaei, S. A., Soleimani, M., Rezaie, S., Ghahvehchian, H., & Banafsheafshan, A. (2018) · Mycoses · doi:10.1111/myc.12822 · Cited by 259 · see citing works
Deep learning approach to describe and classify fungi microscopic images
Zieliński, B., Sroka-Oleksiak, A., Rymarczyk, D., Piekarczyk, A., & Brzychczy-Włoch, M. (2020) · PLOS ONE · doi:10.1371/journal.pone.0234806 · Cited by 126 · see citing works
Deep learning for imaging and detection of microorganisms
Zhang, Y., Jiang, H., Ye, T., & Juhas, M. (2021) · Trends in Microbiology · doi:10.1016/j.tim.2021.01.006 · Cited by 96 · see citing works
A fungus spores dataset and a convolutional neural network based approach for fungus detection
Tahir, M. W., Zaidi, N. A., Rao, A. A., Blank, R., Vellekoop, M. J., & Lang, W. (2018) · IEEE Transactions on NanoBioscience · doi:10.1109/tnb.2018.2839585 · Cited by 88 · see citing works
Fredericksen, M., Zhang, Y., Hazen, M. L., Loreto, R. G., Mangold, C. A., Chen, D. Z., & Hughes, D. P. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1711673114 · Cited by 86 · see citing works
Artificial Intelligence in the Detection of Skin Cancer.
Beltrami, E. J., Brown, A. C., Salmon, P., Leffell, D., Ko, J., & Grant-Kels, J. (2022) · Journal of American Academy of Dermatology · doi:10.1016/j.jaad.2022.08.028 · Cited by 65 · see citing works
Deep learning for sensitive detection of Helicobacter Pylori in gastric biopsies
Klein, S., Gildenblat, J., Ihle, M. A., Merkelbach‐Bruse, S., Noh, K., Peifer, M., Quaas, A., & Büttner, R. (2020) · BMC Gastroenterology · doi:10.1186/s12876-020-01494-7 · Cited by 53 · see citing works
Self-citationArtificial intelligence-based tools applied to pathological diagnosis of microbiological diseases
Marletta, S., L’Imperio, V., Eccher, A., Antonini, P., Santonicco, N., Girolami, I., Dei Tos, A. P., Sbaraglia, M., Pagni, F., Brunelli, M., et al. (2023) · Pathology - Research and Practice · doi:10.1016/j.prp.2023.154362 · Cited by 49 · see citing works
Artificial intelligence-based screening for mycobacteria in whole-slide images of tissue samples
Pantanowitz, L., Wu, U., Seigh, L., LoPresti, E., Yeh, F.-C., Salgia, P., Michelow, P., Hazelhurst, S., Chen, W.-Y., Hartman, D., et al. (2021) · American Journal of Clinical Pathology · doi:10.1093/ajcp/aqaa215 · Cited by 45 · see citing works
Artificial Intelligence in Dermatopathology: New Insights and Perspectives
Cazzato, G., Colagrande, A., Cimmino, A., Arezzo, F., Loizzi, V., Caporusso, C., Marangio, M., Foti, C., Romita, P., Lospalluti, L., et al. (2021) · Dermatopathology · doi:10.3390/dermatopathology8030044 · Cited by 37 · see citing works
Lee, J. J., Jedrych, J. J., Pantanowitz, L., & Ho, J. (2017) · American Journal of Dermatopathology · doi:10.1097/dad.0000000000000888 · Cited by 34 · see citing works
Doeleman, T., Hondelink, L. M., Vermeer, M. H., van Dijk, M. R., & Schrader, A. M. R. (2023) · Seminars in Cancer Biology · doi:10.1016/j.semcancer.2023.06.004 · Cited by 26 · see citing works
Bascuñana, P., Brackhan, M., & Pahnke, J. (2020) · Journal of Alzheimer s Disease · doi:10.3233/jad-201120 · Cited by 23 · see citing works
Fungal Infections of the Skin, Hair and Nails
Suhonen, R. E., Dawber, R. P. R., & Ellis, D. H. (2020) · CRC Press eBooks · doi:10.1201/9781003076643 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Deep learning application to hyphae and spores identification in fungal fluorescence images
Ren, R., Tan, W., Chen, S., Xu, X., Zhang, D., Chen, P., & Zhu, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-11228-y · Cited by 11 · see citing works
Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“Hence, there is potential for using AI models for the detection of fungal organisms, as already reported in other fields of histopathology [27–31].”
Superficial Mycoses: A Mapping Through Bibliometric Research
Wu, J., Tang, Z., Wang, S., Qiu, Y., Nie, X., Li, C., & Wang, R. (2025) · Mycopathologia · doi:10.1007/s11046-025-00947-5 · Cited by 1 · see citing works
AI‐Assisted Fungal Detection in Dermatopathology: A Study on Accuracy, Efficiency, and Usability
Schmidle, P., Lang, T., Springenberg, S., Calvopina, D. A., Köhler, T., Getova, V., Gebing, A.-M., Hetzer, S., Pierrone, J., Aebischer, V., et al. (2026) · JDDG Journal der Deutschen Dermatologischen Gesellschaft · doi:10.1111/ddg.70242 · Cited by 1 · see citing works
Ahmad, A., Waghmare, U., Walambe, R., Rao, P., Munje, A., Yeravdekar, R., & Prakash, O. (2026) · iScience · doi:10.1016/j.isci.2026.116694 · Cited by 1 · see citing works
New Directions in Digital Pathology
Sun, C. W., Lee, J. B., & Motaparthi, K. (2026) · Dermatologic Clinics · doi:10.1016/j.det.2026.01.012
Grari, O., & Hami, A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30563-3_3
Artificial Intelligence in the Diagnosis of Superficial Mycoses: A Scoping Review
(Misăiloaie), D. M. L., Popescu, I. A., Vâță, D., Mocanu, M., & Solovăstru, L. G. (2026) · Journal of Fungi · doi:10.3390/jof12090684
Deep Learning for Pityriasis Versicolor Diagnosis: Automated Fungal Detection in KOH Microscopy
Güney, C. A., Koç, H. A., Kılınç, F., Antink, C. H., & Güney, G. (2026) · Dermatology Practical & Conceptual · doi:10.5826/dpc.160s1a7639
Li, Y., Gu, T., Ma, L., Yao, L., Li, M., Han, S., Li, X., 谷维宽, & Ma, J. (2026) · Antonie van Leeuwenhoek · doi:10.1007/s10482-026-02434-3
Alwahaibi, N. (2026) · SAGE Open Medicine · doi:10.1177/20503121261492483
- Authors
- Rappoport, N., Goldinger, G., Debby, A., Molchanov, Y., Barak, Y., Gildenblat, J., Hadar, O., Sagiv, C., & Barzilai, A.
- Affiliations
- Tel Aviv University (IL)
- Sheba Medical Center (IL)
- Corresponding author
- Rappoport, Naama
- Venue
- Pathology - Research and Practice
- Publisher
- Elsevier BV
- Published
- 2024-09
- Volume
- 261
- Pages
- 155480
- ISSN
- 0344-0338 (ISSN-L)
- OpenAlex
- W4400866432
- Cited by
- 11
- References
- 14
- Citations by year
- 2026: 8 · 2025: 3
- FWCI
- 1.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Pathology - Research and Practice (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Epidemiology › Nail Diseases and Treatments
- Keywords
- fungal skin infection, artificial intelligence, decision support system, PAS staining, Gomori methenamine silver staining, skin biopsy, digital pathology, dermatopathology diagnosis
- MeSH terms
- Artificial Intelligence, Biopsy, Dermatomycoses, Female, Humans, Decision Support Systems, Clinical
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
