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.

尽管深度学习算法已经证明了专家级的性能，但以前的工作主要是有限疾病的二进制分类。我们用220,680张174种疾病的图像训练了一种算法，并使用爱丁堡（1,300张图像； 10种疾病）和SNU数据集（2,201张图像； 134种疾病）对其进行了验证。该算法可以准确地预测恶性肿瘤，建议主要治疗方案，在134种疾病之间进行多类分类并提高医疗专业人员的绩效。用于检测恶性肿瘤的曲线下面积为0.928±0.002（爱丁堡）和0.937±0.004（SNU）。对于类固醇，抗生素，抗病毒药和抗真菌药，主要治疗建议（SNU）曲线下的面积分别为0.828±0.012、0.885±0.006、0.885±0.006和0.918±0.006。对于多类别分类，前1名和前5名的平均准确度分别为56.7±1.6％和92.0±1.1％（爱丁堡）和44.8±1.2％和78.1±0.3％（SNU）。借助我们的算法，47位临床医生（21位皮肤科医生和26位皮肤科居民）对恶性预测（SNU； 240张图像）的敏感性和特异性提高了12.1％（P <0.0001）和1.1％（P <0.0001）。23名非医学专业人员的恶性肿瘤预测敏感性显着提高了83.8％（P <0.0001）。在134种疾病的多类别分类（SNU； 2,201张图像）中，四位医生的前1位和前3位准确性分别提高了7.0％（P  = 0.045）和10.1％（P  = 0.0020）。结果表明，我们的算法可以用作增强的智能，可以增强医学专家在诊断皮肤病学中的能力。