{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:45:12Z","timestamp":1785422712804,"version":"3.56.0"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"President\u2019s PhD Scholarships of Imperial College London"},{"id":[{"id":"https:\/\/ror.org\/041kmwe10","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Dermatophytosis is commonly assessed using potassium hydroxide (KOH) microscopy, yet accurate recognition of fungal hyphae is hindered by preparation-related artifacts, heterogeneous keratin clearance, and notable inter-observer variability. This study presents a transformer-based object detection framework using the RT-DETR architecture for precise, query-driven localisation of fungal structures in high-resolution KOH images. A dataset of 2540 routinely acquired microscopy images was manually annotated using a multi-class strategy that explicitly distinguishes fungal elements from confounding artifacts, enabling the model to actively suppress false detections arising from visually similar mimics. To assess architectural trade-offs, RT-DETR was benchmarked against two CNN-based detectors (YOLOv11 and Faster R-CNN) under identical training and inference conditions. Five-fold stratified cross-validation was performed, and each fold-level model was evaluated on the same independent held-out test set (n = 254). Across the five evaluations, RT-DETR achieved a mean AP@0.50 of 89.73%\u00b11.48%, a mean recall of 0.831\u00b10.011, and a mean precision of 0.921\u00b10.014. At the image level, the model achieved a mean sensitivity of 0.989\u00b10.022 on the independent test set, with a mean of 0.2\u00b10.4 missed positive cases across the five evaluations. These results demonstrate the technical feasibility of a transformer-based artificial intelligence (AI) system as a decision-support aid for fungal region detection in KOH microscopy, pending prospective multi-center validation to establish clinical generalisability.<\/jats:p>","DOI":"10.3390\/bioengineering13050591","type":"journal-article","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T15:02:27Z","timestamp":1779375747000},"page":"591","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artifact-Aware Fungal Detection in Dermatophytosis: A Transformer-Based Approach for KOH Microscopy"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1823-7582","authenticated-orcid":false,"given":"Rana","family":"Gursoy","sequence":"first","affiliation":[{"name":"Department of Mechatronics Engineering, Yildiz Technical University, Istanbul 34220, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4922-7391","authenticated-orcid":false,"given":"Abdurrahim","family":"Yilmaz","sequence":"additional","affiliation":[{"name":"Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3725-9044","authenticated-orcid":false,"given":"Baris","family":"Kizilyaprak","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Venereology, Istanbul Research and Training Hospital, Istanbul 34098, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-7930-592X","authenticated-orcid":false,"given":"Esmahan","family":"Caglar","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, Yildiz Technical University, Istanbul 34220, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2390-5091","authenticated-orcid":false,"given":"Burak","family":"Temelkuran","sequence":"additional","affiliation":[{"name":"Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0392-982X","authenticated-orcid":false,"given":"Huseyin","family":"Uvet","sequence":"additional","affiliation":[{"name":"Department of Mechatronics Engineering, Yildiz Technical University, Istanbul 34220, Turkey"},{"name":"Artificial Intelligence Research and Application Center (YZAUM), Istinye University, Istinye 34396, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3457-1958","authenticated-orcid":false,"given":"Ayse Esra","family":"Koku Aksu","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Venereology, Istanbul Research and Training Hospital, Istanbul 34098, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0650-0722","authenticated-orcid":false,"given":"Gulsum","family":"Gencoglan","sequence":"additional","affiliation":[{"name":"Department of Dermatology and Venereology, Medicana Atakoy Hospital, Istanbul 34158, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"659","DOI":"10.4103\/0378-6323.86475","article-title":"Onychomycosis: Diagnosis and management","volume":"77","author":"Singal","year":"2011","journal-title":"Indian J. Dermatol. Venereol. Leprol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1972","DOI":"10.1111\/jdv.16394","article-title":"Onychomycosis: A review","volume":"34","author":"Gupta","year":"2020","journal-title":"J. Eur. Acad. Dermatol. Venereol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"764843","DOI":"10.1155\/2010\/764843","article-title":"The sensitivity and specificity of potassium hydroxide smear and fungal culture relative to clinical assessment in the evaluation of tinea pedis: A pooled analysis","volume":"2010","author":"Levitt","year":"2010","journal-title":"Dermatol. Res. Pract."},{"key":"ref_4","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_5","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_6","doi-asserted-by":"crossref","first-page":"101563","DOI":"10.1016\/j.media.2019.101563","article-title":"Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images","volume":"58","author":"Graham","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"9099","DOI":"10.1109\/ACCESS.2017.2705642","article-title":"Malaria Parasite Detection From Peripheral Blood Smear Images Using Deep Belief Networks","volume":"5","author":"Bibin","year":"2017","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","article-title":"AI in health and medicine","volume":"28","author":"Rajpurkar","year":"2022","journal-title":"Nat. Med."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1146\/annurev-bioeng-071516-044442","article-title":"Deep learning in medical image analysis","volume":"19","author":"Shen","year":"2017","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1038\/s41746-024-01106-8","article-title":"Artificial intelligence in digital pathology: A systematic review and meta-analysis of diagnostic test accuracy","volume":"7","author":"McGenity","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.ejca.2019.04.001","article-title":"Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task","volume":"113","author":"Brinker","year":"2019","journal-title":"Eur. J. Cancer"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2637","DOI":"10.1007\/s13555-022-00833-8","article-title":"Artificial Intelligence in Dermatology: Challenges and Perspectives","volume":"12","author":"Liopyris","year":"2022","journal-title":"Dermatol. Ther."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zhang, Y., Chang, Z., and Li, C. (2024). Sperm YOLOv8E-TrackEVD: A Novel Approach for Sperm Detection and Tracking. Sensors, 24.","DOI":"10.3390\/s24113493"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"691","DOI":"10.1016\/j.bbe.2018.05.007","article-title":"Automatic detection of tuberculosis bacilli from microscopic sputum smear images using deep learning methods","volume":"38","author":"Panicker","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"e4568","DOI":"10.7717\/peerj.4568","article-title":"Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images","volume":"6","author":"Rajaraman","year":"2018","journal-title":"PeerJ"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"116925","DOI":"10.1109\/ACCESS.2019.2936017","article-title":"Segmentation and Classification of Cervical Cells Using Deep Learning","volume":"7","author":"Kurnianingsih","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104649","DOI":"10.1016\/j.compbiomed.2021.104649","article-title":"DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques","volume":"136","author":"Rahaman","year":"2021","journal-title":"Comput. Biol. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1109\/TMI.2018.2865709","article-title":"Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map","volume":"38","author":"Naylor","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_22","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_23","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_24","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_25","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_26","doi-asserted-by":"crossref","unstructured":"Jegham, N., Koh, C.Y., Abdelatti, M., and Hendawi, A. (2024). YOLO Evolution: A Comprehensive Benchmark and Architectural Review of YOLOv12, YOLOv11, and Their Previous Versions. arXiv.","DOI":"10.2139\/ssrn.5175639"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"98","DOI":"10.18231\/j.ijced.2020.021","article-title":"Mimickers of dermatophytes on KOH mount","volume":"6","author":"Garg","year":"2020","journal-title":"IP Indian J. Clin. Exp. Dermatol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1186\/s13071-024-06503-2","article-title":"A lightweight deep-learning model for parasite egg detection in microscopy images","volume":"17","author":"Xu","year":"2024","journal-title":"Parasites Vectors"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107917","DOI":"10.1016\/j.compbiomed.2024.107917","article-title":"Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding the diagnosis of blood diseases","volume":"170","author":"Chen","year":"2024","journal-title":"Comput. Biol. Med."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Nakarmi, S., Pudasaini, S., Thapaliya, S., Upretee, P., Shrestha, R., Giri, B., Neupane, B.B., and Khanal, B. (2024). Deep-learning Assisted Detection and Quantification of (oo)cysts of Giardia and Cryptosporidium on Smartphone Microscopy Images. Mach. Learn. Biomed. Imaging, 2.","DOI":"10.59275\/j.melba.2024-a333"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"100004","DOI":"10.1016\/j.array.2019.100004","article-title":"A review: Deep learning for medical image segmentation using multi-modality fusion","volume":"3-4","author":"Zhou","year":"2019","journal-title":"Array"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). Detrs Beat YolOS on Real-time Object Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4793","DOI":"10.1109\/TNNLS.2020.3027314","article-title":"A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI","volume":"32","author":"Tjoa","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jdrv.2024.12.012","article-title":"Diagnosis and management of nail unit squamous cell carcinoma: A clinical review by an expert panel","volume":"4","author":"Curtis","year":"2025","journal-title":"JAAD Rev."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Venturi, F., Magnaterra, E., Scotti, B., Alessandrini, A., Veneziano, L., Vaccari, S., Baraldi, C., and Dika, E. (2025). Squamous Cell Carcinoma of the Nail Unit: A Comprehensive Review of Clinical Features, Diagnostic Workflow, Management Strategies and Therapeutic Options. Diagnostics, 15.","DOI":"10.3390\/diagnostics15182378"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/5\/591\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T15:35:36Z","timestamp":1779377736000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/13\/5\/591"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,21]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["bioengineering13050591"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering13050591","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,21]]}}}