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Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study

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  • Seung Seog Han
  • Ik Jun Moon
  • Seong Hwan Kim
  • Jung-Im Na
  • Myoung Shin Kim
  • Gyeong Hun Park
  • Ilwoo Park
  • Keewon Kim
  • Woohyung Lim
  • Ju Hee Lee
  • Sung Eun Chang

Abstract

Background: The diagnostic performance of convolutional neural networks (CNNs) for diagnosing several types of skin neoplasms has been demonstrated as comparable with that of dermatologists using clinical photography. However, the generalizability should be demonstrated using a large-scale external dataset that includes most types of skin neoplasms. In this study, the performance of a neural network algorithm was compared with that of dermatologists in both real-world practice and experimental settings. Methods and findings: To demonstrate generalizability, the skin cancer detection algorithm (https://rcnn.modelderm.com) developed in our previous study was used without modification. We conducted a retrospective study with all single lesion biopsied cases (43 disorders; 40,331 clinical images from 10,426 cases: 1,222 malignant cases and 9,204 benign cases); mean age (standard deviation [SD], 52.1 [18.3]; 4,701 men [45.1%]) were obtained from the Department of Dermatology, Severance Hospital in Seoul, Korea between January 1, 2008 and March 31, 2019. Using the external validation dataset, the predictions of the algorithm were compared with the clinical diagnoses of 65 attending physicians who had recorded the clinical diagnoses with thorough examinations in real-world practice. Conclusions: Our algorithm could diagnose skin tumors with nearly the same accuracy as a dermatologist when the diagnosis was performed solely with photographs. However, as a result of limited data relevancy, the performance was inferior to that of actual medical examination. To achieve more accurate predictive diagnoses, clinical information should be integrated with imaging information. Seung Seog Han and colleagues compare the performance of a neural network algorithm with that of dermatologists in diagnosis of skin neoplasms.Why was this study done?: What did the researchers do and find?: What do these findings mean?:

Suggested Citation

  • Seung Seog Han & Ik Jun Moon & Seong Hwan Kim & Jung-Im Na & Myoung Shin Kim & Gyeong Hun Park & Ilwoo Park & Keewon Kim & Woohyung Lim & Ju Hee Lee & Sung Eun Chang, 2020. "Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study," PLOS Medicine, Public Library of Science, vol. 17(11), pages 1-21, November.
    • Handle: RePEc:plo:pmed00:1003381
    DOI: 10.1371/journal.pmed.1003381
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    References listed on IDEAS

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    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    2. Pranav Rajpurkar & Jeremy Irvin & Robyn L Ball & Kaylie Zhu & Brandon Yang & Hershel Mehta & Tony Duan & Daisy Ding & Aarti Bagul & Curtis P Langlotz & Bhavik N Patel & Kristen W Yeom & Katie Shpanska, 2018. "Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists," PLOS Medicine, Public Library of Science, vol. 15(11), pages 1-17, November.
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    1. Young Jae Kim & Jung-Im Na & Seung Seog Han & Chong Hyun Won & Mi Woo Lee & Jung-Won Shin & Chang-Hun Huh & Sung Eun Chang, 2022. "Augmenting the accuracy of trainee doctors in diagnosing skin lesions suspected of skin neoplasms in a real-world setting: A prospective controlled before-and-after study," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-11, January.

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