Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders.

Journal: The Journal of investigative dermatology
PMID: 32243882

Abstract

Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,300 images; 10 disorders) and SNU datasets (2,201 images; 134 disorders). The algorithm could accurately predict malignancy, suggest primary treatment options, render multi-class classification among 134 disorders, and improve the performance of medical professionals. The area under the curves for malignancy detection were 0.928 ± 0.002 (Edinburgh) and 0.937 ± 0.004 (SNU). The area under the curves of primary treatment suggestion (SNU) were 0.828 ± 0.012, 0.885 ± 0.006, 0.885 ± 0.006, and 0.918 ± 0.006 for steroids, antibiotics, antivirals, and antifungals, respectively. For multi-class classification, the mean top-1 and top-5 accuracies were 56.7 ± 1.6% and 92.0 ± 1.1% (Edinburgh) and 44.8 ± 1.2% and 78.1 ± 0.3% (SNU), respectively. With the assistance of our algorithm, the sensitivity and specificity of 47 clinicians (21 dermatologists and 26 dermatology residents) for malignancy prediction (SNU; 240 images) were improved by 12.1% (P < 0.0001) and 1.1% (P < 0.0001), respectively. The malignancy prediction sensitivity of 23 non-medical professionals was significantly increased by 83.8% (P < 0.0001). The top-1 and top-3 accuracies of four doctors in the multi-class classification of 134 diseases (SNU; 2,201 images) were increased by 7.0% (P = 0.045) and 10.1% (P = 0.0020), respectively. The results suggest that our algorithm may serve as augmented intelligence that can empower medical professionals in diagnostic dermatology.

Authors

  • Seung Seog Han
    I Dermatology, Seoul, Korea.
  • Ilwoo Park
    Department of Radiology, Chonnam National University Medical School and Hospital, Gwangju, Korea.
  • Sung Eun Chang
    Department of Dermatology, Asan Medical Center, Ulsan University College of Medicine, Seoul, Korea.
  • Woohyung Lim
    SK Telecom, HMI Tech. Lab, Seoul, Korea.
  • Myoung Shin Kim
    Department of Dermatology, Sanggye Paik Hospital, Inje University College of Medicine, Seoul, Korea.
  • Gyeong Hun Park
    Department of Dermatology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Dongtan, Korea.
  • Je Byeong Chae
    Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Chang Hun Huh
    Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea.
  • Jung-Im Na
    Department of Dermatology, Seoul National University Bundang Hospital, Seongnam, Korea.

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