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.
Open accessAbstract
Although there have been reports of the successful diagnosis of skin disorders using deep learning, unrealistically large clinical image datasets are required for artificial intelligence (AI) training. We created datasets of standardized nail images using a region-based convolutional neural network (R-CNN) trained to distinguish the nail from the background. We used R-CNN to generate training datasets of 49,567 images, which we then used to fine-tune the ResNet-152 and VGG-19 models. The validation datasets comprised 100 and 194 images from Inje University (B1 and B2 datasets, respectively), 125 images from Hallym University (C dataset), and 939 images from Seoul National University (D dataset). The AI (ensemble model; ResNet-152 + VGG-19 + feedforward neural networks) results showed test sensitivity/specificity/ area under the curve values of (96.0 / 94.7 / 0.98), (82.7 / 96.7 / 0.95), (92.3 / 79.3 / 0.93), (87.7 / 69.3 / 0.82) for the B1, B2, C, and D datasets. With a combination of the B1 and C datasets, the AI Youden index was significantly (p = 0.01) higher than that of 42 dermatologists doing the same assessment manually. For B1+C and B2+ D dataset combinations, almost none of the dermatologists performed as well as the AI. By training with a dataset comprising 49,567 images, we achieved a diagnostic accuracy for onychomycosis using deep learning that was superior to that of most of the dermatologists who participated in this study.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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?
Several algorithms incorporating artificial intelligence have been developed to help interpret clinical and dermoscopic images for a variety of skin conditions.
“Several algorithms incorporating artificial intelligence (AI) have been developed to help interpret both clinical11,12,13,14,15 and dermoscopic16,17,18,19,20,21,22,23 images for a variety of skin conditions”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1506.01497 · Cited by 70,812 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684 · see citing works
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741 · see citing works
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works
An Analysis of Deep Neural Network Models for Practical Applications
Canziani, A., Paszke, A., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1605.07678 · Cited by 969 · see citing works
Deep learning ensembles for melanoma recognition in dermoscopy images
Codella, N., Nguyen, Q.-B., Pankanti, S., Gutman, D. A., Helba, B., Halpern, A. C., & Smith, J. R. (2017) · IBM Journal of Research and Development · doi:10.1147/jrd.2017.2708299 · Cited by 665 · see citing works
Methods for Diagnosing Onychomycosis
Lawry, M. A., Haneke, E., Strobeck, K., Martin, S., Zimmer, B. A., & Romano, P. S. (2000) · Archives of Dermatology · doi:10.1001/archderm.136.9.1112 · Cited by 175 · see citing works
Binder, M., Steiner, A., Schwarz, M., Knollmayer, S., Wolff, K., & Pehamberger, H. (1994) · British Journal of Dermatology · doi:10.1111/j.1365-2133.1994.tb03378.x · Cited by 110 · see citing works
Velasquez-Agudelo, V., & Cardona‐Arias, J. A. (2017) · BMC Infectious Diseases · doi:10.1186/s12879-017-2258-3 · Cited by 88 · see citing works
Comparison of direct smear, culture and histology for the diagnosis of onychomycosis
Karimzadegannia, M., Mohammadi, A. M., Bouzari, N., & Firooz, A. (2007) · Australasian Journal of Dermatology · doi:10.1111/j.1440-0960.2007.00320.x · Cited by 66 · see citing works
Jeelani, S., Ahmed, Q. M., Lanker, A., Hassan, I., Jeelani, N., & Fazili, T. (2014) · Mycoses · doi:10.1111/myc.12251 · Cited by 58 · see citing works
Onychomycosis: An Analysis of 50 Cases and a Comparison of Diagnostic Techniques
Borkowski, P., Williams, M., Holewinski, J., & Bakotic, B. W. (2001) · Journal of the American Podiatric Medical Association · doi:10.7547/87507315-91-7-351 · Cited by 43 · see citing works
Avaliação dos métodos diagnósticos para onicomicose
Zanardi, D., Nunes, D. H., Pacheco, A. D. S., Tubone, M. Q., & Filho, J. J. D. S. (2008) · Anais Brasileiros de Dermatologia · doi:10.1590/s0365-05962008000200003 · Cited by 21 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [4]He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. arXiv:151203385. 2015.
Cited by
Sorted by citations
Tschandl, P., Rosendahl, C., & Kittler, H. (2018) · Scientific Data · doi:10.1038/sdata.2018.161 · Cited by 3,628 · see citing works
Liu, X., Faes, L., Kale, A. U., Wagner, S. K., Fu, D. J., Bruynseels, A., Mahendiran, T., Moraes, G., Shamdas, M., Kern, C., et al. (2019) · The Lancet Digital Health · doi:10.1016/s2589-7500(19)30123-2 · Cited by 1,883 · see citing works
Nagendran, M., Chen, Y., Lovejoy, C. A., Gordon, A. C., Komorowski, M., Harvey, H., Topol, E. J., Ioannidis, J. P. A., Collins, G. S., & Maruthappu, M. (2020) · BMJ · doi:10.1136/bmj.m689 · Cited by 1,152 · see citing works
Transfer learning for medical image classification: a literature review
Kim, H. E., Cosa‐Linan, A., Santhanam, N., Jannesari, M., Maros, M. E., & Ganslandt, T. (2022) · BMC Medical Imaging · doi:10.1186/s12880-022-00793-7 · Cited by 1,036 · see citing works
A deep learning system for differential diagnosis of skin diseases
Liu, Y., Jain, A., Eng, C., Way, D. H., Lee, K., Bui, P., Kanada, K., de Oliveira Marinho, G., Gallegos, J., Gabriele, S., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0842-3 · Cited by 853 · see citing works
Han, S. S., Kim, M. S., Lim, W., Park, G., Park, I., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.01.028 · Cited by 715 · see citing works
Artificial intelligence in retina
Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.07.004 · Cited by 779 · see citing works
Goyal, M., Knackstedt, T., Yan, S., & Hassanpour, S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.104065 · Cited by 461 · see citing works
Daneshjou, R., Smith, M. P., Sun, M. D., Rotemberg, V., & Zou, J. (2021) · JAMA Dermatology · doi:10.1001/jamadermatol.2021.3129 · Cited by 403 · see citing works
Artificial Intelligence Versus Clinicians in Disease Diagnosis: Systematic Review
Shen, J., Zhang, C. J. P., Jiang, B., Chen, J., Song, J., Liu, Z., He, Z., Wong, S. Y., Fang, P.-H., & Ming, W.-K. (2019) · JMIR Medical Informatics · doi:10.2196/10010 · Cited by 373 · see citing works
What is AI? Applications of artificial intelligence to dermatology
Du‐Harpur, X., Watt, F. M., Luscombe, N. M., & Lynch, M. D. (2020) · British Journal of Dermatology · doi:10.1111/bjd.18880 · Cited by 355 · see citing works
Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations
Chan, S. Y., Reddy, V. D., Myers, B. A., Thibodeaux, Q. G., Brownstone, N. D., & Liao, W. J. (2020) · Dermatology and Therapy · doi:10.1007/s13555-020-00372-0 · Cited by 275 · see citing works
Artificial Intelligence in Dermatology Image Analysis: Current Developments and Future Trends
Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11226826 · Cited by 279 · see citing works
Artificial Intelligence in Dermatology: A Primer
Young, A. T., Xiong, M., Pfau, J., Keiser, M. J., & Wei, M. L. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.02.026 · Cited by 249 · see citing works
Sollini, M., Antunovic, L., Chiti, A., & Kirienko, M. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04372-x · Cited by 227 · see citing works
Han, S. S., Park, I., Eun Chang, S., Lim, W., Kim, M. S., Park, G. H., Chae, J. B., Huh, C. H., & Na, J.-I. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.01.019 · Cited by 248 · see citing works
Hirani, R., Noruzi, K. A., Khuram, H., Hussaini, A. S., Aifuwa, E., Ely, K., Lewis, J. M., Gabr, A. E., Smiley, A., Tiwari, R. K., et al. (2024) · Life · doi:10.3390/life14050557 · Cited by 256 · see citing works
Artificial Intelligence in Skin Cancer Diagnostics: The Patients' Perspective
Jutzi, T. B., Krieghoff-Henning, E. I., Holland-Letz, T., Utikal, J. S., Hauschild, A., Schadendorf, D., Sondermann, W., Fröhling, S., Hekler, A., Schmitt, M., et al. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00233 · Cited by 196 · see citing works
Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review
Hogarty, D. T., Su, J. C., Phan, K. H., Attia, M., Hossny, M., Nahavandi, S., Lenane, P., Moloney, F. J., & Yazdabadi, A. (2019) · American Journal of Clinical Dermatology · doi:10.1007/s40257-019-00462-6 · Cited by 186 · see citing works
Algorithmic Management: Bright and Dark Sides, Practical Implications, and Research Opportunities
Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022) · Business & Information Systems Engineering · doi:10.1007/s12599-022-00764-w · Cited by 180 · see citing works
Skin disease diagnosis with deep learning: a review
Li, H., Pan, Y., Zhao, J., & Zhang, L. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.096 · Cited by 178 · see citing works
Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4
Zhou, J., He, X., Sun, L., Xu, J., Chen, X., Chu, Y., Zhou, L., Liao, X., Zhang, B., Afvari, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50043-3 · Cited by 189 · see citing works
Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., Sayres, R., Kanada, K., Eng, C., Nagpal, K., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.7249 · Cited by 164 · see citing works
Machine Learning and Its Application in Skin Cancer
Das, K., Cockerell, C. J., Patil, A. D., Pietkiewicz, P., Giulini, M., Grabbe, S., & Goldust, M. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182413409 · Cited by 163 · see citing works
Keratinocytic Skin Cancer Detection on the Face Using Region-Based Convolutional Neural Network
Han, S. S., Moon, I. J., Lim, W., Suh, I. S., Lee, S. Y., Na, J., Kim, S. H., & Chang, S. E. (2019) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.3807 · Cited by 147 · see citing works
Deep learning in dermatology: a systematic review of current approaches, outcomes, and limitations
Jeong, H. K., Park, C., Henao, R., & Kheterpal, M. (2023) · JID Innovations · doi:10.1016/j.xjidi.2022.100150 · Cited by 134 · see citing works
Use of artificial intelligence in dermatology
De, A., Sarda, A., Gupta, S., & DAS, S. K. (2020) · Indian Journal of Dermatology · doi:10.4103/ijd.ijd_418_20 · Cited by 119 · see citing works
Malik, H., Farooq, M. S., Khelifi, A., Abid, A., Nasir Qureshi, J., & Hussain, M. (2020) · IEEE Access · doi:10.1109/access.2020.3004766 · Cited by 104 · see citing works
Deep learning in electron microscopy
Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 105 · see citing works
Systematic review of machine learning for diagnosis and prognosis in dermatology
Thomsen, K. G., Iversen, L., Titlestad, T. L., & Winther, O. (2019) · Journal of Dermatological Treatment · doi:10.1080/09546634.2019.1682500 · Cited by 101 · see citing works
Luca, A. R., Ursuleanu, T. F., Gheorghe, L., Grigorovici, R., Iancu, S., Hlusneac, M., & Grigorovici, A. (2022) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2022.100911 · Cited by 99 · see citing works
Ultrasound image analysis using deep learning algorithm for the diagnosis of thyroid nodules
Song, J., Chai, Y. J., Masuoka, H., Park, S., Kim, S., Choi, J. Y., Kong, H., Lee, K. E., Lee, J., Kwak, N., et al. (2019) · Medicine · doi:10.1097/md.0000000000015133 · Cited by 93 · see citing works
Wang, X., Xuan, H., Evers, B., Shrestha, S. K., Pless, R. B., & Poland, J. (2019) · GigaScience · doi:10.1093/gigascience/giz120 · Cited by 91 · see citing works
Updated Perspectives on the Diagnosis and Management of Onychomycosis
Falotico, J. M., & Lipner, S. R. (2022) · Clinical Cosmetic and Investigational Dermatology · doi:10.2147/ccid.s362635 · Cited by 79 · see citing works
Han, S. S., Kim, Y. J., Moon, I. J., Jung, J. M., Lee, M. Y., Lee, W. J., Won, C. H., Lee, M. W., Kim, S. H., Navarrete‐Dechent, C., et al. (2022) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2022.02.003 · Cited by 77 · see citing works
Basic of machine learning and deep learning in imaging for medical physicists
Manco, L., Maffei, N., Strolin, S., Vichi, S., Bottazzi, L., & Strigari, L. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.026 · Cited by 75 · see citing works
Early automated detection system for skin cancer diagnosis using artificial intelligent techniques
Mahmoud, N. M., & Soliman, A. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-59783-0 · Cited by 72 · see citing works
Deep Learning for Diagnostic Binary Classification of Multiple-Lesion Skin Diseases
Thomsen, K. G., Christensen, A. L., Iversen, L., Lomholt, H. B., & Winther, O. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.574329 · Cited by 68 · see citing works
Deep learning for dermatologists: Part II. Current applications
Puri, P., Comfere, N. I., Drage, L. A., Shamim, H., Bezalel, S. A., Pittelkow, M. R., Davis, M. D. P., Wang, M., Mangold, A. R., Tollefson, M. M., et al. (2020) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.05.053 · Cited by 67 · see citing works
Machine learning applications in the evaluation and management of psoriasis: a systematic review
Yu, K., Syed, M. N., Bernardis, E., & Gelfand, J. M. (2020) · Journal of Psoriasis and Psoriatic Arthritis · doi:10.1177/2475530320950267 · Cited by 65 · see citing works
Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach
Rezk, E., Eltorki, M., & El-Dakhakhni, W. W. (2022) · JMIR Dermatology · doi:10.2196/39143 · Cited by 69 · see citing works
Yoo, T. K., Choi, J. Y., Kim, H. K., Ryu, I. H., & Kim, J. K. (2021) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2021.106086 · Cited by 62 · see citing works
Cho, S. I., Sun, S., Mun, J., Kim, C., Kim, S. Y., Cho, S., Youn, S. W., Kim, H., & Chung, J. H. (2019) · British Journal of Dermatology · doi:10.1111/bjd.18459 · Cited by 59 · see citing works
Diagnosis of Onychomycosis: From Conventional Techniques and Dermoscopy to Artificial Intelligence
Lim, S. S., Ohn, J., & Mun, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.637216 · Cited by 59 · see citing works
Multi-view Convolutional Recurrent Neural Networks for Lung Cancer Nodule Identification
Abid, M. M. N., Zia, T., Ghafoor, M., & Windridge, D. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.06.144 · Cited by 57 · see citing works
Koo, T., Kim, M.-H., & Jue, M. (2021) · PLoS ONE · doi:10.1371/journal.pone.0256290 · Cited by 55 · see citing works
A survey of deep learning models in medical therapeutic areas
Nogales, A., García-Tejedor, Á. J., Martín, D. M., Vara, J. S., & Rodríguez, C. A. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102020 · Cited by 57 · see citing works
Background selection schema on deep learning-based classification of dermatological disease
Zhou, J., Wu, Z., Jiang, Z., Huang, K., Guo, K., & Zhao, S. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105966 · Cited by 53 · see citing works
Yang, Y., Ge, Y., Guo, L., Wu, Q., Peng, L., Zhang, E., Xie, J., Li, Y., & Lin, T. (2020) · Skin Research and Technology · doi:10.1111/srt.12911 · Cited by 53 · see citing works
Artificial intelligence in dermatology and healthcare: An overview
Pai, V. V., & Pai, R. B. (2021) · Indian Journal of Dermatology Venereology and Leprology · doi:10.25259/ijdvl_518_19 · Cited by 53 · see citing works
Skin Disease Classification System Based on Machine Learning Technique: A Survey
Mohammed, S. S., & Al-Tuwaijari, J. M. (2021) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/1076/1/012045 · Cited by 52 · see citing works
Yanagisawa, Y., Shido, K., Kojima, K., & Yamasaki, K. (2023) · Journal of Dermatological Science · doi:10.1016/j.jdermsci.2023.01.005 · Cited by 52 · see citing works
Ground truthing from multi-rater labeling with three-way decision and possibility theory
Campagner, A., Ciucci, D., Svensson, C., Figge, M. T., & Cabitza, F. (2020) · Information Sciences · doi:10.1016/j.ins.2020.09.049 · Cited by 51 · see citing works
Decroos, F., Springenberg, S., Lang, T., Päpper, M., Zapf, A., Metze, D., Steinkraus, V, & Böer-Auer, A. (2021) · Acta Dermato Venereologica · doi:10.2340/00015555-3893 · Cited by 51 · see citing works
Artificial Intelligence in Emergency Medicine: Benefits, Risks, and Recommendations
Vearrier, L., Derse, A. R., Basford, J. B., Larkin, G. L., & Moskop, J. C. (2022) · The Journal of Emergency Medicine · doi:10.1016/j.jemermed.2022.01.001 · Cited by 49 · see citing works
Fatima, F., Imran, M., Ullah, A., Arif, M., & Noor, R. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105424 · Cited by 49 · see citing works
Kim, Y. J., Han, S. S., Yang, H. J., & Chang, S. E. (2020) · PLoS ONE · doi:10.1371/journal.pone.0234334 · Cited by 47 · see citing works
Hossain, S. I., de Hervé, J. D. G., Hassan, M. S., Martineau, D., Petrosyan, E., Corbin, V., Beytout, J., Lebert, I., Durand, J., Carravieri, I., et al. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106624 · Cited by 50 · see citing works
Review of Machine Learning in Predicting Dermatological Outcomes
Du, A., Emam, S., & Gniadecki, R. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00266 · Cited by 42 · see citing works
Xu, H., Tang, T., Zhang, B., & Liu, Y. (2022) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2022.104191 · Cited by 41 · see citing works
Research Techniques Made Simple: Deep Learning for the Classification of Dermatological Images
Cullell-Dalmau, M., Otero-Viñas, M., & Manzo, C. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2019.12.029 · Cited by 41 · see citing works
Diagnosing Onychomycosis: What’s New?
Gupta, A. K., Hall, D. C., Cooper, E. A., & Ghannoum, M. A. (2022) · Journal of Fungi · doi:10.3390/jof8050464 · Cited by 40 · see citing works
Artificial intelligence in dermatology: A practical introduction to a paradigm shift
Eapen, B. R. (2020) · Indian Dermatology Online Journal · doi:10.4103/idoj.idoj_388_20 · Cited by 43 · see citing works
Improving electron micrograph signal-to-noise with an atrous convolutional encoder-decoder
Ede, J. M., & Beanland, R. (2019) · Ultramicroscopy · doi:10.1016/j.ultramic.2019.03.017 · Cited by 38 · see citing works
Toward automated severe pharyngitis detection with smartphone camera using deep learning networks
Yoo, T. K., Choi, J. Y., Jang, Y., Oh, E., & Ryu, I. H. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103980 · Cited by 38 · see citing works
Artificial Intelligence in Diagnostic Dermatology: Challenges and the Way Forward
Sengupta, D. (2023) · Indian Dermatology Online Journal · doi:10.4103/idoj.idoj_462_23 · Cited by 40 · see citing works
Application of artificial intelligence in modern medicine
Xu, N., Yang, D., Arikawa, K., & Bai, C. (2023) · Clinical eHealth · doi:10.1016/j.ceh.2023.09.001 · Cited by 39 · see citing works
Automated severity scoring of atopic dermatitis patients by a deep neural network
Bang, C. H., Yoon, J. W., Ryu, J. Y., Chun, J. H., Han, J. H., Lee, Y. B., Lee, J. Y., Park, Y. M., Lee, S. J., & Lee, J. H. (2021) · Scientific Reports · doi:10.1038/s41598-021-85489-8 · Cited by 38 · see citing works
Clinical spectrum of cutaneous melanoma morphology
Klebanov, N., Gunasekera, N. S., Lin, W. M., Hawryluk, E. B., Miller, D. M., Reddy, B. Y., Christman, M. P., Beaulieu, D., Rajadurai, S., Duncan, L. M., et al. (2018) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2018.08.028 · Cited by 35 · see citing works
Deep learning‐based diagnosis models for onychomycosis in dermoscopy
Zhu, X., Zheng, B., Cai, W., Zhang, J., Lu, S., Li, X., Xi, L., & Kong, Y. (2022) · Mycoses · doi:10.1111/myc.13427 · Cited by 35 · see citing works
Sinha, M., Paul, S., Ghosh, M., Mohanty, S. N., & Pattanayak, R. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-58438-4 · Cited by 34 · see citing works
Skin Type Diversity in Skin Lesion Datasets: A Review
Alipour, N., Burke, T., & Courtney, J. (2024) · Current Dermatology Reports · doi:10.1007/s13671-024-00440-0 · Cited by 46 · see citing works
Lesion-Based Convolutional Neural Network in Diagnosis of Early Gastric Cancer
Yoon, H. J., & Kim, J. (2020) · Clinical Endoscopy · doi:10.5946/ce.2020.046 · Cited by 32 · see citing works
Zaar, O., Larson, A., Polesie, S., Saleh, K., Tarstedt, M., Olives, A., Suárez, A. L., Gillstedt, M., & Neittaanmäki, N. (2020) · Acta Dermato Venereologica · doi:10.2340/00015555-3624 · Cited by 30 · see citing works
The biomedical applications of artificial intelligence: an overview of decades of research
Naskar, S., Sharma, S., Kuotsu, K., Halder, S., Pal, G., Saha, S., Mondal, S., Biswas, U. K., Jana, M., & Bhattacharjee, S. (2025) · Journal of drug targeting (Print) · doi:10.1080/1061186x.2024.2448711 · Cited by 18 · see citing works
The Role of DICOM in Artificial Intelligence for Skin Disease
Caffery, L. J., Rotemberg, V. M., Weber, J., Soyer, H. P., Malvehy, J., & Clunie, D. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2020.619787 · Cited by 32 · see citing works
Comparing Artificial Intelligence Platforms for Histopathologic Cancer Diagnosis.
Borkowski, A., Wilson, C. P., Borkowski, S. A., Thomas, L. B., Deland, L. A., Grewe, S., & Mastorides, S. (2019) · PubMed Central · Cited by 30 · see citing works
Wang, S., Zhang, X.-Y., Liu, J., Tao, C., Zhu, C., Shu, C., Xu, T., & Jin, H. (2020) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001023 · Cited by 30 · see citing works
Sendín‐Martín, M., Lara-Caro, M., Harris, U., Moronta, M., Rossi, A., Lee, E. H., Chen, C. J., Nehal, K. S., Conejo‐Mir, J., Rodríguez, J., et al. (2021) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2021.09.029 · Cited by 30 · see citing works
Gupta, A. K., Wang, T., Cooper, E. A., Summerbell, R. C., Piguet, V., Tosti, A., & Piraccini, B. M. (2023) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.19644 · Cited by 29 · see citing works
Web-based study on Chinese dermatologists’ attitudes towards artificial intelligence
Shen, C., Li, C., Xu, F., Wang, Z., Shen, X., Gao, J., Ko, R., Jing, Y., Tang, X., Yu, R., et al. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2019.12.102 · Cited by 30 · see citing works
Taylor, M., Liu, X., Denniston, A. K., Esteva, A., Ko, J., Daneshjou, R., & Chan, A. (2021) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2021.02.744 · Cited by 28 · see citing works
Artificial intelligence in psoriasis: Where we are and where we are going
Liu, Z., Wang, X., Ma, Y., Lin, Y., & Wang, G. (2023) · Experimental Dermatology · doi:10.1111/exd.14938 · Cited by 28 · see citing works
PENERAPAN METODE RESIDUAL NETWORK (RESNET) DALAM KLASIFIKASI PENYAKIT PADA DAUN GANDUM
Ridhovan, A., & Suharso, A. (2022) · JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) · doi:10.29100/jipi.v7i1.2410 · Cited by 26 · see citing works
Kim, J., Oh, I., Lee, Y. N., Lee, J. H., Lee, Y. I., Kim, J., & Lee, J. H. (2023) · Scientific Reports · doi:10.1038/s41598-023-40395-z · Cited by 26 · see citing works
Zhu, A.-Q., Wang, Q., Shi, Y.-L., Ren, W.-W., Cao, X., Ren, T.-T., Wang, J., Zhang, Y.-Q., Sun, Y.-K., Chen, X.-W., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2023.102391 · Cited by 25 · see citing works
Singla, N., Kundu, R., & Dey, P. (2023) · Cytopathology · doi:10.1111/cyt.13336 · Cited by 25 · see citing works
Yılmaz, A., Göktay, F., Varol, R., Gençoğlan, G., & Üvet, H. (2022) · Mycoses · doi:10.1111/myc.13498 · Cited by 25 · see citing works
Interpretation of the Outputs of a Deep Learning Model Trained with a Skin Cancer Dataset
Han, S. S., Lim, W., Kim, M. S., Park, I., Park, G., & Chang, S. E. (2018) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.05.014 · Cited by 24 · see citing works
Alzamili, A. H., & Ruhaiyem, N. I. R. (2025) · Journal of Intelligent Systems · doi:10.1515/jisys-2024-0381 · Cited by 24 · see citing works
Out-of-Distribution Detection for Skin Lesion Images with Deep Isolation Forest
Li, X., Lu, Y., Desrosiers, C., & Liu, X. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-59861-7_10 · Cited by 23 · see citing works
Bozkurt, A., Köse, K., Coll‐Font, J., Alessi‐Fox, C., Brooks, D. H., Dy, J., & Rajadhyaksha, M. M. (2021) · Scientific Reports · doi:10.1038/s41598-021-90328-x · Cited by 23 · see citing works
Graham, S. S. (2022) · doi:10.1093/oso/9780197644461.001.0001 · Cited by 23 · see citing works
Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review
Gaurav, V., Grover, C., Tyagi, M., & Saurabh, S. (2024) · Indian Dermatology Online Journal · doi:10.4103/idoj.idoj_460_24 · Cited by 22 · see citing works
Deep Neural Forest for Out-of-Distribution Detection of Skin Lesion Images
Li, X., Desrosiers, C., & Liu, X. (2022) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3171582 · Cited by 23 · see citing works
Using DeepLabCut as a Real-Time and Markerless Tool for Cardiac Physiology Assessment in Zebrafish
Suryanto, M. E., Saputra, F., Kurnia, K. A., Vasquez, R. D., Roldan, M. J. M., Chen, K. H. ‐., Huang, J., & Hsiao, C. (2022) · Biology · doi:10.3390/biology11081243 · Cited by 21 · see citing works
Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4Preprint
Zhou, J., He, X., Sun, L., Xu, J., Chen, X., Chu, Y., Zhou, L., Liao, X., Zhang, B., & Gao, X. (2023) · medRxiv · doi:10.1101/2023.06.10.23291127 · Cited by 20 · see citing works
Willem, T., Krammer, S., Böhm, A. ‐., French, L. E., Hartmann, D., Lasser, T., & Buyx, A. (2022) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.18192 · Cited by 18 · see citing works
Tao, Y., Hu, H. M., Li, J., Li, M., Zheng, Q., Zhang, G., & Ni, M. (2022) · Arthroplasty · doi:10.1186/s42836-022-00145-4 · Cited by 19 · see citing works
New Frontiers in Explainable AI: Understanding the GI to Interpret the GO
Cabitza, F., Campagner, A., & Ciucci, D. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-29726-8_3 · Cited by 19 · see citing works
- Authors
- Han, S. S., Park, G. H., Lim, W., Kim, M. S., Na, J. I., Park, I., & Chang, S. E.
- Affiliations
- Hallym University (KR)
- Inje University (KR)
- Seoul National University (KR)
- Chonnam National University (KR)
- Asan Medical Center (KR)
- Corresponding author
- Chang, Sung Eun
- ORCID
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-01-19
- Volume
- 13
- Issue
- 1
- Pages
- e0191493
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC5774804
- OpenAlex
- W2785200097
- Cited by
- 305
- References
- 18
- Citations by year
- 2026: 24 · 2025: 32 · 2024: 40 · 2023: 33 · 2022: 58 · 2021: 37 · 2020: 49 · 2019: 24 · 2018: 7
- FWCI
- 22.75
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Epidemiology › Nail Diseases and Treatments
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- onychomycosis, automated diagnosis, dermatologist comparison, R-CNN, ResNet-152, VGG19, ensemble learning, dermatological image analysis
- MeSH terms
- Machine Learning, Dermatologists, Adult, Aged, Algorithms, Artificial Intelligence, Diagnosis, Computer-Assisted, Female, Foot Dermatoses, Hand Dermatoses, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Onychomycosis, Databases, Factual, Neural Networks, Computer, Area Under Curve, Young Adult
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
