A deep learning system for differential diagnosis of skin diseases

Abstract

Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.

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Fig. 1: Overview of the development and validation of our DLS.
Fig. 2: Performance of the DLS and the dermatologists (Derm), primary care physicians (PCPs) and nurse practitioners (NPs).
Fig. 3: Representative examples of challenging cases missed by non-dermatologists.
Fig. 4: Importance of different inputs to the DLS.

Data availability

The de-identified teledermatology data used in this study are not publicly available due to restrictions in the data-sharing agreement.

Code availability

The deep learning framework (TensorFlow) used in this study is available at https://www.tensorflow.org/. The training framework (Estimator) is available at https://www.tensorflow.org/guide/estimators. The deep learning architecture (Inception-v4) is available at https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v4.py.

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Acknowledgements

We thank W. Chen, J. Yoshimi, X. Ji and Q. Duong for software infrastructure support for data collection. Thanks also go to G. Foti, K. Su, T. Saensuksopa, D. Wang, Y. Gao and L. Tran. We also appreciate the input of C. Chen, M. Howell and A. Paller for their feedback on the manuscript. Last, but not least, this work would not have been possible without the participation of the dermatologists, primary care physicians and nurse practitioners who reviewed cases for this study, and S. Bis who helped to establish the skin condition mapping.

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Authors

Contributions

Yuan Liu, A.J., C.E., D.H.W., K.L. and D.C. prepared the dataset for usage. S.J.H., K.K. and R.H.-W. provided clinical expertise and guidance for the study. Yuan Liu, A.J., C.E., K.L., P.B., G.d.O.M., J.G., D.A., S.J.H. and K.K. worked on the technical, logistical and quality control aspects of label collection. S.J.H. and K.K. established the skin condition mapping. Yuan Liu, K.L., V.G. and D.C. developed the model. Yuan Liu, A.J., N.S. and V.N. performed statistical analysis and additional analysis. Yun Liu guided study design, analysis of the results and statistical analysis. S.G. studied the potential utility of the model. R.C.D. and D.C. initiated the project and led the overall development, with strategic guidance and executive support from G.S.C., L.H.P. and D.R.W. Yuan Liu, Yun Liu and S.J.H. prepared the manuscript with the assistance and feedback from all other co-authors. K.K. and S.J.H. performed the work at Google Health via Advanced Clinical. G.d.O.M. performed the work at Google Health via Adecco Staffing. N.S. performed the work at Google Health.

Corresponding author

Correspondence to Yun Liu.

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Competing interests

K.K. and S.J.H. were consultants of Google LLC. R.H.-W. is an employee of the Medical University of Graz. G.d.O.M. is an employee of Adecco Staffing supporting Google LLC. This study was funded by Google LLC. The remaining authors are employees of Google LLC and own Alphabet stock as part of the standard compensation package. Yuan Liu, A.J., C.E., D.H.W., K.L., P.B., J.G., V.G., D.A., Yun Liu, R.C.D. and D.C. are inventors on a filed patent related to this work. The authors declare no other competing interests.

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Peer review information Javier Carmona was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Performance of the deep learning system (DLS) and clinicians, broken down for each of the 26 categories of skin conditions and ‘other’.

a, Top-1 and top-3 sensitivity of the DLS on validation set A (n=3,756). b, Top-1 and top-3 sensitivity of the DLS and three types of clinicians: dermatologists (Derm), primary care physicians (PCP), and nurse practitioners (NP) on validation set B (n=963). Numbers in parentheses in the x-axes indicate the number of cases. Detailed breakdown of each clinician and the DLS performance on the subset of cases graded by each clinician are in Supplementary Table 8. Error bars indicate 95% CI (see Statistical Analysis).

Extended Data Fig. 2 Performance of the deep learning system (DLS) and the clinicians on the 419-way classification: dermatologists (Derm), primary care physicians (PCP), and nurse practitioners (NP) on validation set A (n=3,756) and validation set B (n=963).

a, Top-1 and top-3 accuracy for the DLS and clinicians across all cases and 419 categories of skin conditions. b, Average overlap (to assess the full differential diagnosis) of the DLS and clinicians. Error bars indicate 95% confidence intervals (see Statistical Analysis).

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–10 and Tables 1–13.

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Liu, Y., Jain, A., Eng, C. et al. A deep learning system for differential diagnosis of skin diseases. Nat Med 26, 900–908 (2020). https://doi.org/10.1038/s41591-020-0842-3

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