%0 Generic %1 liu2019learning %A Liu, Yuan %A Jain, Ayush %A Eng, Clara %A Way, David H. %A Lee, Kang %A Bui, Peggy %A Kanada, Kimberly %A Marinho, Guilherme de Oliveira %A Gallegos, Jessica %A Gabriele, Sara %A Gupta, Vishakha %A Singh, Nalini %A Natarajan, Vivek %A Hofmann-Wellenhof, Rainer %A Corrado, Greg S. %A Peng, Lily H. %A Webster, Dale R. %A Ai, Dennis %A Huang, Susan %A Liu, Yun %A Dunn, R. Carter %A Coz, David %D 2019 %K ai diseases examples medical skin %T A deep learning system for differential diagnosis of skin diseases %U http://arxiv.org/abs/1909.05382 %X Skin conditions affect an estimated 1.9 billion people worldwide. A shortage of dermatologists causes long wait times and leads patients to seek dermatologic care from general practitioners. However, the diagnostic accuracy of general practitioners has been reported to be only 0.24-0.70 (compared to 0.77-0.96 for dermatologists), resulting in referral errors, delays in care, and errors in diagnosis and treatment. In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories). The DLS distinguishes between 26 skin conditions that represent roughly 80% of the volume of skin conditions seen in primary care. The DLS was developed and validated using de-identified cases from a teledermatology practice serving 17 clinical sites via a temporal split: the first 14,021 cases for development and the last 3,756 cases for validation. On the validation set, where a panel of three board-certified dermatologists defined the reference standard for every case, the DLS achieved 0.71 and 0.93 top-1 and top-3 accuracies respectively. For a random subset of the validation set (n=963 cases), 18 clinicians reviewed the cases for comparison. On this subset, the DLS achieved a 0.67 top-1 accuracy, non-inferior to board-certified dermatologists (0.63, p<0.001), and higher than primary care physicians (PCPs, 0.45) and nurse practitioners (NPs, 0.41). The top-3 accuracy showed a similar trend: 0.90 DLS, 0.75 dermatologists, 0.60 PCPs, and 0.55 NPs. These results highlight the potential of the DLS to augment general practitioners to accurately diagnose skin conditions by suggesting differential diagnoses that may not have been considered. Future work will be needed to prospectively assess the clinical impact of using this tool in actual clinical workflows.

@misc{liu2019learning, abstract = {Skin conditions affect an estimated 1.9 billion people worldwide. A shortage of dermatologists causes long wait times and leads patients to seek dermatologic care from general practitioners. However, the diagnostic accuracy of general practitioners has been reported to be only 0.24-0.70 (compared to 0.77-0.96 for dermatologists), resulting in referral errors, delays in care, and errors in diagnosis and treatment. In this paper, we developed a deep learning system (DLS) to provide a differential diagnosis of skin conditions for clinical cases (skin photographs and associated medical histories). The DLS distinguishes between 26 skin conditions that represent roughly 80% of the volume of skin conditions seen in primary care. The DLS was developed and validated using de-identified cases from a teledermatology practice serving 17 clinical sites via a temporal split: the first 14,021 cases for development and the last 3,756 cases for validation. On the validation set, where a panel of three board-certified dermatologists defined the reference standard for every case, the DLS achieved 0.71 and 0.93 top-1 and top-3 accuracies respectively. For a random subset of the validation set (n=963 cases), 18 clinicians reviewed the cases for comparison. On this subset, the DLS achieved a 0.67 top-1 accuracy, non-inferior to board-certified dermatologists (0.63, p<0.001), and higher than primary care physicians (PCPs, 0.45) and nurse practitioners (NPs, 0.41). The top-3 accuracy showed a similar trend: 0.90 DLS, 0.75 dermatologists, 0.60 PCPs, and 0.55 NPs. These results highlight the potential of the DLS to augment general practitioners to accurately diagnose skin conditions by suggesting differential diagnoses that may not have been considered. Future work will be needed to prospectively assess the clinical impact of using this tool in actual clinical workflows.}, added-at = {2019-11-07T09:59:40.000+0100}, author = {Liu, Yuan and Jain, Ayush and Eng, Clara and Way, David H. and Lee, Kang and Bui, Peggy and Kanada, Kimberly and Marinho, Guilherme de Oliveira and Gallegos, Jessica and Gabriele, Sara and Gupta, Vishakha and Singh, Nalini and Natarajan, Vivek and Hofmann-Wellenhof, Rainer and Corrado, Greg S. and Peng, Lily H. and Webster, Dale R. and Ai, Dennis and Huang, Susan and Liu, Yun and Dunn, R. Carter and Coz, David}, biburl = {https://www.bibsonomy.org/bibtex/20eb199306062bd5b38a94b3aa151fa35/nosebrain}, description = {A deep learning system for differential diagnosis of skin diseases}, interhash = {6a3bb20c84056016d38bedb5093e6754}, intrahash = {0eb199306062bd5b38a94b3aa151fa35}, keywords = {ai diseases examples medical skin}, note = {cite arxiv:1909.05382}, timestamp = {2019-11-07T09:59:40.000+0100}, title = {A deep learning system for differential diagnosis of skin diseases}, url = {http://arxiv.org/abs/1909.05382}, year = 2019 }