{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T12:25:06Z","timestamp":1789129506475,"version":"build-2803163510"},"reference-count":85,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Fungi","JoF"],"abstract":"<jats:p>Artificial intelligence (AI) is increasingly proposed for diagnosing superficial mycoses, yet whether this literature covers the diagnostic pathway evenly, and is mature enough for clinical translation, remains unclear. We mapped AI applications onto an operational partition of the diagnostic pathway (D1\u2013D4), a construct of this review: pre-analytical clinician-facing imaging (D1), analytical microscopy and histopathology (D2), analytical molecular identification (D3) and integrative post-analytical interpretation (D4). Thirty-three primary studies (2007 to 2026) were included, 85% published since 2021 and predominantly from Asia. Applications concentrated on D2 (n = 16) and D1 (n = 13), with few in D3 (n = 4) and none in D4. Reported accuracy was frequently high but rested on internal, single-centre validation: complete external validation and prospective preregistration were each present in a single study, only a handful shared both public code and public data, about half reported no demographic data and no D1 study stratified performance by Fitzpatrick phototype. A substantial minority carried conflicts of interest tied to the evaluated tool, concentrated in the molecular domain where independent verification was scarcest; where such re-evaluation was possible, reported accuracy fell markedly (one area under the curve (AUC) from 0.98 to 0.75). This corpus is skewed toward the early diagnostic steps, leaves the integrative post-analytical step unaddressed and shows methodological maturity insufficient for routine clinical translation. Institutionalised external validation, reproducibility through open code and data, declaration of dataset overlap and conflicts of interest, and clinically meaningful outcomes are the priorities for future work.<\/jats:p>","DOI":"10.3390\/jof12090684","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T10:51:37Z","timestamp":1789123897000},"page":"684","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence in the Diagnosis of Superficial Mycoses: A Scoping Review"],"prefix":"10.3390","volume":"12","author":[{"given":"Denisa Mihaela","family":"Loghin (Mis\u0103iloaie)","sequence":"first","affiliation":[{"name":"Department of Dermatology, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy, 700115 Ia\u0219i, Romania"},{"name":"Department of Dermatology, \u201cSt. Spiridon\u201d Emergency County Clinical Hospital, 700111 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0447-3625","authenticated-orcid":false,"given":"Ioana Adriana","family":"Popescu","sequence":"additional","affiliation":[{"name":"Department of Dermatology, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy, 700115 Ia\u0219i, Romania"},{"name":"Department of Dermatology, \u201cSt. Spiridon\u201d Emergency County Clinical Hospital, 700111 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2755-6004","authenticated-orcid":false,"given":"Dan","family":"V\u00e2\u021b\u0103","sequence":"additional","affiliation":[{"name":"Department of Dermatology, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy, 700115 Ia\u0219i, Romania"},{"name":"Department of Dermatology, \u201cSt. Spiridon\u201d Emergency County Clinical Hospital, 700111 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M\u0103d\u0103lina","family":"Mocanu","sequence":"additional","affiliation":[{"name":"Department of Dermatology, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy, 700115 Ia\u0219i, Romania"},{"name":"Department of Dermatology, \u201cSt. Spiridon\u201d Emergency County Clinical Hospital, 700111 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Gheuc\u0103-Solov\u0103stru","sequence":"additional","affiliation":[{"name":"Department of Dermatology, \u201cGrigore T. Popa\u201d University of Medicine and Pharmacy, 700115 Ia\u0219i, Romania"},{"name":"Department of Dermatology, \u201cSt. Spiridon\u201d Emergency County Clinical Hospital, 700111 Ia\u0219i, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ioannidis, J.P.A. (2016). Why Most Clinical Research Is Not Useful. PLoS Med., 13.","DOI":"10.1371\/journal.pmed.1002049"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.2147\/CCID.S522271","article-title":"Artificial Intelligence and Large Language Models in the Fight Against Superficial Fungal Infections: Friend or Foe?","volume":"18","author":"Gupta","year":"2025","journal-title":"Clin. Cosmet. Investig. Dermatol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1038\/s42256-021-00307-0","article-title":"Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans","volume":"3","author":"Roberts","year":"2021","journal-title":"Nat. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.ejca.2019.02.005","article-title":"A convolutional neural network trained with dermoscopic images performed on par with 145 dermatologists in a clinical melanoma image classification task","volume":"111","author":"Brinker","year":"2019","journal-title":"Eur. J. Cancer"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.jid.2019.12.029","article-title":"Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images","volume":"140","author":"Manzo","year":"2020","journal-title":"J. Investig. Dermatol."},{"key":"ref_6","unstructured":"Raghu, M., Zhang, C., Kleinberg, J., and Bengio, S. (2019). Transfusion: Understanding Transfer Learning for Medical Imaging. Advances in Neural Information Processing Systems 32 (NeurIPS 2019), NeurIPS 2019."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Chanyachailert, P., Leeyaphan, C., and Bunyaratavej, S. (2023). Cutaneous Fungal Infections Caused by Dermatophytes and Non-Dermatophytes: An Updated Comprehensive Review of Epidemiology, Clinical Presentations, and Diagnostic Testing. J. Fungi, 9.","DOI":"10.3390\/jof9060669"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kruithoff, C., Gamal, A., McCormick, T.S., and Ghannoum, M.A. (2023). Dermatophyte Infections Worldwide: Increase in Incidence and Associated Antifungal Resistance. Life, 14.","DOI":"10.20944\/preprints202311.1060.v1"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1016\/j.jaad.2016.09.041","article-title":"Cutaneous fungal infections are commonly misdiagnosed: A survey-based study","volume":"76","author":"Yadgar","year":"2017","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Belmokhtar, Z., Djaroud, S., Matmour, D., and Merad, Y. (2024). Atypical and Unpredictable Superficial Mycosis Presentations: A Narrative Review. J. Fungi, 10.","DOI":"10.20944\/preprints202401.0300.v1"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s11046-008-9101-8","article-title":"Clinical Forms of Dermatophytosis (Ringworm Infection)","volume":"166","author":"Degreef","year":"2008","journal-title":"Mycopathologia"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1177\/1203475417733461","article-title":"Confirmatory Testing Prior to Initiating Onychomycosis Therapy Is Cost-Effective","volume":"22","author":"Gupta","year":"2018","journal-title":"J. Cutan. Med. Surg."},{"key":"ref_14","first-page":"1","article-title":"Tinea corporis: An updated review","volume":"9","author":"Leung","year":"2020","journal-title":"Drugs Context"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Velasquez-Agudelo, V., and Cardona-Arias, J.A. (2017). Meta-analysis of the utility of culture, biopsy, and direct KOH examination for the diagnosis of onychomycosis. BMC Infect. Dis., 17.","DOI":"10.1186\/s12879-017-2258-3"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1016\/j.jaad.2018.03.062","article-title":"Onychomycosis","volume":"80","author":"Lipner","year":"2019","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Evrard, S., Minon, C., Lamtiri Laarif, M., De Backer, B., Paridaens, H., Hayette, M.-P., Fr\u00e8re, J., Senterre, J.-M., and Minon, J.-M. (2024). New Diagnostic Strategy for Onychomycosis: First-Line Utilization of DermaGenius\u00ae PCR and Calcofluor Microscopy Combined with Selective Culturing. J. Fungi, 10.","DOI":"10.3390\/jof10080515"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1007\/s11046-025-01012-x","article-title":"Assessing the Validity and Impact of Remote Digital Image Reading in Fungal Diagnostics","volume":"190","author":"Lundgren","year":"2025","journal-title":"Mycopathologia"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"277","DOI":"10.1093\/mmy\/myy020","article-title":"Clinical evaluation of the DermaGenius\u00ae Nail real-time PCR assay for the detection of dermatophytes and Candida albicans in nails","volume":"57","author":"Hayette","year":"2019","journal-title":"Med. Mycol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1556\/1886.2026.00003","article-title":"Detection of dermatophytes, Candida spp., and molds by the EuroArray Dermatomycosis\u00ae microarray versus fungal culture in patients with suspected dermatomycosis","volume":"16","author":"Gellrich","year":"2026","journal-title":"EUJMI"},{"key":"ref_21","first-page":"80","article-title":"Mycological diagnosis of superficial fungal infections: French ANOFEL\/LABAC recommendations","volume":"84","author":"Miossec","year":"2026","journal-title":"Ann. Biol. Clin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e13777","DOI":"10.1111\/myc.13777","article-title":"Next generation mycological diagnosis: Artificial intelligence-based classifier of the presence of Malassezia yeasts in tape strip samples","volume":"67","author":"Zink","year":"2024","journal-title":"Mycoses"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Buli\u0144ska, B., Mazur-Milecka, M., S\u0142awi\u0144ska, M., Rumi\u0144ski, J., and Nowicki, R.J. (2024). Artificial Intelligence in the Diagnosis of Onychomycosis-Literature Review. J. Fungi, 10.","DOI":"10.3390\/jof10080534"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2119","DOI":"10.11124\/JBIES-20-00167","article-title":"Updated methodological guidance for the conduct of scoping reviews","volume":"18","author":"Peters","year":"2020","journal-title":"JBI Evid. Synth."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"467","DOI":"10.7326\/M18-0850","article-title":"PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation","volume":"169","author":"Tricco","year":"2018","journal-title":"Ann. Intern Med."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1016\/j.jid.2023.12.019","article-title":"The Promises and Perils of Foundation Models in Dermatology","volume":"144","author":"Gui","year":"2024","journal-title":"J. Investig. Dermatol."},{"key":"ref_28","unstructured":"Higgins, J.P.T., Thomas, J., Chandler, J., Cumpston, M., Li, T., Page, M.J., and Welch, V.A. (2024). Chapter 4: Searching for and selecting studies. Cochrane Handbook for Systematic Reviews of Interventions, Version 6.5, Cochrane. Available online: https:\/\/www.cochrane.org\/authors\/handbooks-and-manuals\/handbook."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1111\/jocd.14681","article-title":"Diagnosing onychomycosis: A step forward?","volume":"21","author":"Gupta","year":"2022","journal-title":"J. Cosmet. Dermatol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Gupta, A.K., Hall, D.C., Cooper, E.A., and Ghannoum, M.A. (2022). Diagnosing Onychomycosis: What\u2019s New?. J. Fungi, 8.","DOI":"10.3390\/jof8050464"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1309\/AJCPR28HWHSSDNON","article-title":"The Brain-to-Brain Loop Concept for Laboratory Testing 40 Years After Its Introduction","volume":"136","author":"Plebani","year":"2011","journal-title":"Am. J. Clin. Pathol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"The QUADAS-2 Group*. QUADAS-2: A Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1663","DOI":"10.1038\/s41591-021-01517-0","article-title":"A quality assessment tool for artificial intelligence-centered diagnostic test accuracy studies: QUADAS-AI","volume":"27","author":"Sounderajah","year":"2021","journal-title":"Nat. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"e2504943","DOI":"10.7326\/ANNALS-25-04943","article-title":"QUADAS-3 Explanation and Elaboration: Guidance for Quality Assessment of Diagnostic Test Accuracy Studies","volume":"179","author":"Davenport","year":"2026","journal-title":"Ann. Intern Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e58202","DOI":"10.2196\/58202","article-title":"Revised Tool for the Quality Assessment of Diagnostic Accuracy Studies Using AI (QUADAS-AI): Protocol for a Qualitative Study","volume":"13","author":"Guni","year":"2024","journal-title":"JMIR Res. Protoc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e70007","DOI":"10.1111\/myc.70007","article-title":"Superficial Fungal Infections and Artificial Intelligence: A Review on Current Advances and Opportunities: REVISION","volume":"68","author":"Pour","year":"2025","journal-title":"Mycoses"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lim, S.S., Ohn, J., and Mun, J.-H. (2021). Diagnosis of Onychomycosis: From Conventional Techniques and Dermoscopy to Artificial Intelligence. Front. Med., 8.","DOI":"10.3389\/fmed.2021.637216"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"40","DOI":"10.4103\/idoj.idoj_460_24","article-title":"Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review","volume":"16","author":"Gaurav","year":"2025","journal-title":"Indian Dermatol. Online J."},{"key":"ref_39","first-page":"177","article-title":"Machine Learning Techniques for Skin Fungal Infection Detection\u2014A Review","volume":"2024","author":"Shakir","year":"2024","journal-title":"MJAIH"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1080\/14787210.2023.2144232","article-title":"Strategies to improve the diagnosis and clinical treatment of dermatophyte infections","volume":"21","author":"Durdu","year":"2023","journal-title":"Expert Rev. Anti-Infect. Ther."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s40257-019-00462-6","article-title":"Artificial Intelligence in Dermatology-Where We Are and the Way to the Future: A Review","volume":"21","author":"Hogarty","year":"2020","journal-title":"Am. J. Clin. Dermatol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"352","DOI":"10.4103\/ijd.IJD_418_20","article-title":"Use of artificial intelligence in dermatology","volume":"65","author":"De","year":"2020","journal-title":"Indian J. Dermatol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1097\/JD9.0000000000000404","article-title":"Artificial Intelligence in Dermatology: A Systematized Review","volume":"8","author":"Biswas","year":"2025","journal-title":"Int. J. Dermatol. Venereol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"145","DOI":"10.4103\/ds.DS-D-23-00025","article-title":"The emerging role of Artificial Intelligence in diagnosis and clinical analysis of dermatology","volume":"41","author":"Ye","year":"2023","journal-title":"Dermatol. Sin."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., and Ribatti, D. (2025). Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives. Med. Sci., 13.","DOI":"10.3390\/medsci13020070"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Albuqami, N.M., Alkahtani, L.M., Alharbi, Y.A., Aljuhaymi, D.A., Alnufaei, R.D., Al Mashaikhi, A.A., and Sayed, A.A. (2026). Advancements in Artificial Intelligence-Based Diagnostic Tools Used to Detect Fungal Infections: A Systematic Review. Diagnostics, 16.","DOI":"10.3390\/diagnostics16030450"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Han, S.S., Park, G.H., Lim, W., Kim, M.S., Na, J.I., Park, I., and Chang, S.E. (2018). Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0191493"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1111\/myc.13498","article-title":"Deep convolutional neural networks for onychomycosis detection using microscopic images with KOH examination","volume":"65","author":"Yilmaz","year":"2022","journal-title":"Mycoses"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"adv00532","DOI":"10.2340\/00015555-3893","article-title":"A Deep Learning Approach for Histopathological Diagnosis of Onychomycosis: Not Inferior to Analogue Diagnosis by Histopathologists","volume":"101","author":"Decroos","year":"2021","journal-title":"Acta Derm. Venereol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Jansen, P., Creosteanu, A., Matyas, V., Dilling, A., Pina, A., Saggini, A., Schimming, T., Landsberg, J., Burgdorf, B., and Giaquinta, S. (2022). Deep Learning Assisted Diagnosis of Onychomycosis on Whole-Slide Images. J. Fungi, 8.","DOI":"10.3390\/jof8090912"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1111\/bjd.13358","article-title":"British Association of Dermatologists\u2019 guidelines for the management of onychomycosis 2014","volume":"171","author":"Ameen","year":"2014","journal-title":"Br. J. Dermatol."},{"key":"ref_52","first-page":"678","article-title":"S1 Guideline onychomycosis","volume":"21","author":"Nenoff","year":"2023","journal-title":"J. Dtsch. Dermatol. Ges."},{"key":"ref_53","first-page":"1","article-title":"Consensus-based Guideline on tinea capitis","volume":"24","author":"Mayser","year":"2026","journal-title":"J. Dtsch. Dermatol. Ges."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1111\/j.1365-2559.2007.02745.x","article-title":"Evaluation of digital images of histological paraffin sections in the diagnosis of fungal infection: Interobserver reproducibility and pitfalls","volume":"51","author":"Berger","year":"2007","journal-title":"Histopathology"},{"key":"ref_55","first-page":"1","article-title":"Image-Processing Scheme to Detect Superficial Fungal Infections of the Skin","volume":"2015","author":"Quiskamp","year":"2015","journal-title":"Comput. Math. Methods Med."},{"key":"ref_56","first-page":"872","article-title":"A Novel Machine Learning-based Diagnostic Algorithm for Detection of Onychomycosis through Nail Appearance","volume":"27","author":"Duzayak","year":"2023","journal-title":"Sak. Univ. J. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kong, Y., Lan, S., Lu, S., Yang, Z., and Huang, N. (2025). HybirdRVIT-CNN: A Improved Vision Transformer Model for the Recognition of Onychomycosis in Dermatoscopic Images. Proceedings of the 2025 2nd International Conference on Big Data Analytics and Artificial Intelligence Application, Association for Computing Machinery.","DOI":"10.1145\/3788108.3788532"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Miura, K., Min, Z., Sato, T., Waki, H., and Kageyama, Y. (2024, January 13\u201317). Vision Transformer-based Tinea Unguium Classification Approach Using Foot Images. Proceedings of the 11th International Conference on Intelligent Systems and Image Processing 2024, Matsuyama, Japan.","DOI":"10.12792\/icisip2024.044"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1038\/s41598-025-29562-6","article-title":"Comparative performance of deep learning models and non-dermatologists in diagnosing psoriasis, dermatophytosis, and eczema","volume":"16","author":"Yodrabum","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Anton, A., Plinet, M., Peyret, T., Cazaudarr\u00e9, T., Pesant, S., Rouquet, Y., Tricoteaux, M.-A., Bernier, M., Bayette, J., and Fournier, R. (2023). Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP\u00ae Technology. Diagnostics, 13.","DOI":"10.3390\/diagnostics13223430"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tirard-Collet, P., Durupt, F., H\u00e9rault, M., Miossec, C., Lemoine, J.-P., Wallon, M., Dupont, D., Persat, F., and Menotti, J. (2025). Evaluation of the DendrisKIT\u00aeDP for the Diagnosis of Superficial Fungal Infections. J. Fungi, 11.","DOI":"10.3390\/jof11040269"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ivagnes, V., De Carolis, E., Magr\u00ec, C., Arroyo, M.J., Pavan, G., Prigitano, A.C.M., Chowdhary, A., and Sanguinetti, M. (2025). Integrating Machine Learning and Molecular Methods for Trichophyton indotineae Identification and Resistance Profiling Using MALDI-TOF Spectra. Pathogens, 14.","DOI":"10.3390\/pathogens14100986"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"e0015626","DOI":"10.1128\/jcm.00156-26","article-title":"The emergence of superficial dermatophytosis due to Trichophyton indotineae and Trichophyton mentagrophytes genotypes VII and II* in New York: A need for comprehensive testing approaches","volume":"64","author":"Todd","year":"2026","journal-title":"J. Clin. Microbiol."},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Idress, W.M., Zhao, Y., Abouda, K.A., Ahmed, A.A., Hassan, W., Abdalla, O., and Elhindi, T. (2025, January 6\u20138). Enhanced Onychomycosis Diagnosis Using a Dynamic Weighted Ensemble Classifier: Integrating LightGBM and Transfer Learning with a Game Theory Approach. Proceedings of the 2025 IEEE 15th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA.","DOI":"10.1109\/CCWC62904.2025.10903929"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1177\/12034754251391809","article-title":"Deep Learning for Diagnosis of Tinea Corporis and Tinea Cruris","volume":"30","author":"Julanon","year":"2025","journal-title":"J. Cutan. Med. Surg."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kim, Y.J., Han, S.S., Yang, H.J., and Chang, S.E. (2020). Prospective, comparative evaluation of a deep neural network and dermoscopy in the diagnosis of onychomycosis. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0244899"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1074","DOI":"10.1111\/ijd.17060","article-title":"The performance of an artificial intelligence-based computer vision mobile application for the image diagnosis of genital dermatoses: A prospective cross-sectional study","volume":"63","author":"Mehta","year":"2024","journal-title":"Int. J. Dermatol."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"6370416","DOI":"10.1155\/2023\/6370416","article-title":"Fungal Skin Disease Classification Using the Convolutional Neural Network","volume":"2023","author":"Nigat","year":"2023","journal-title":"J. Healthc. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1111\/jdv.16967","article-title":"A machine learning-based, decision support, mobile phone application for diagnosis of common dermatological diseases","volume":"35","author":"Pangti","year":"2021","journal-title":"Acad. Dermatol. Venereol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"284","DOI":"10.18203\/issn.2455-4529.IntJResDermatol20252064","article-title":"Diagnostic accuracy of artificial intelligence dermatology apps compared to clinical evaluation in Indian patients with common skin conditions","volume":"11","author":"Police","year":"2025","journal-title":"Int. J. Res. Dermatol."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"466","DOI":"10.1111\/myc.13427","article-title":"Deep learning-based diagnosis models for onychomycosis in dermoscopy","volume":"65","author":"Zhu","year":"2022","journal-title":"Mycoses"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1016\/j.clindermatol.2023.12.018","article-title":"Novel B-DNA dermatophyte assay for demonstration of canonical DNA in dermatophytes: Histopathologic characterization by artificial intelligence","volume":"42","author":"Gagna","year":"2024","journal-title":"Clin. Dermatol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1111\/myc.13209","article-title":"The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology","volume":"64","author":"Gao","year":"2021","journal-title":"Mycoses"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"He, W., Zheng, C., Yang, L., and Tan, J. (2025). Automatic fluorescence microscopic image analyzer: A novel AI-based tool for early diagnosing superficial fungal infections. BMC Infect. Dis., 25.","DOI":"10.1186\/s12879-025-11296-5"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1111\/cup.70059","article-title":"Diagnostic Accuracy of Histopathologic Features Suggestive of Dermatophytosis: A Retrospective Cohort Selection Study With Machine Learning Evaluation","volume":"53","author":"Hulse","year":"2026","journal-title":"J. Cutan. Pathol."},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Koo, T., Kim, M.H., and Jue, M.-S. (2021). Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network. PLoS ONE, 16.","DOI":"10.1371\/journal.pone.0256290"},{"key":"ref_77","unstructured":"Rajitha, K.V., Bhat, S., Py, P., Rao, R., and Prasad, K. (2022, January 28\u201331). Classification of Microscopic Images of Unstained Skin Samples Using Deep Learning Approach. Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), Kolkata, India."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"25183","DOI":"10.1007\/s00521-025-11571-1","article-title":"Segmentation of microscopic images of dermatophytes in clinical samples using ResNet and attention-based modifications of U-Net","volume":"37","author":"Rajitha","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"116565","DOI":"10.1016\/j.diagmicrobio.2024.116565","article-title":"Automated grading system for quantifying KOH microscopic images in dermatophytosis","volume":"111","author":"Rajitha","year":"2025","journal-title":"Diagn. Microbiol. Infect. Dis."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"155480","DOI":"10.1016\/j.prp.2024.155480","article-title":"A decision support system for the detection of cutaneous fungal infections using artificial intelligence","volume":"261","author":"Rappoport","year":"2024","journal-title":"Pathol.-Res. Pract."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"27222","DOI":"10.1038\/s41598-025-11228-y","article-title":"Deep learning application to hyphae and spores identification in fungal fluorescence images","volume":"15","author":"Ren","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"eabq6147","DOI":"10.1126\/sciadv.abq6147","article-title":"Disparities in dermatology AI performance on a diverse, curated clinical image set","volume":"8","author":"Daneshjou","year":"2022","journal-title":"Sci. Adv."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"100804","DOI":"10.1016\/j.patter.2023.100804","article-title":"Leakage and the reproducibility crisis in machine-learning-based science","volume":"4","author":"Kapoor","year":"2023","journal-title":"Patterns"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"1084","DOI":"10.1515\/cclm-2024-1451","article-title":"EN ISO 15189 revision: EFLM Committee Accreditation and ISO\/CEN standards (C: A\/ISO) analysis and general remarks on the changes","volume":"63","author":"Linko","year":"2025","journal-title":"Clin. Chem. Lab. Med."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Lundh, A., Barbateskovic, M., Hrobjartsson, A., and Gotzsche, P.C. (2010). Conflicts of Interest at Medical Journals: The Influence of Industry-Supported Randomised Trials on Journal Impact Factors and Revenue\u2014Cohort Study. PLoS Med., 7.","DOI":"10.1371\/journal.pmed.1000354"}],"container-title":["Journal of Fungi"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2309-608X\/12\/9\/684\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T11:21:32Z","timestamp":1789125692000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2309-608X\/12\/9\/684"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,11]]},"references-count":85,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["jof12090684"],"URL":"https:\/\/doi.org\/10.3390\/jof12090684","relation":{},"ISSN":["2309-608X"],"issn-type":[{"value":"2309-608X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,11]]}}}