中华皮肤科杂志 ›› 2023, Vol. 56 ›› Issue (10): 948-952.doi: 10.35541/cjd.20220925

• 研究报道 • 上一篇    下一篇

一种基于34层ResNet模型的人工智能软件诊断皮肤病的性能评估

朱亚杰    卢枫    Syed Mohammad Nooruddin Mahmood    刘欣    李小红    于建斌    董慧婷   

  1. 郑州大学第一附属医院皮肤科,郑州  450052
    卢枫为郑州大学第一附属医院皮肤科专硕研究生,现在河南省儿童医院  郑州大学附属儿童医院  郑州儿童医院皮肤科,郑州  450018
    Syed Mohammad Nooruddin Mahmood为郑州大学第一附属医院皮肤科留学生,现在DrSyeds医疗保健,印度特伦甘纳海得拉巴  500001
    刘欣为郑州大学第一附属医院皮肤科进修医师,现在新乡市第二人民医院皮肤科,新乡  453000

  • 收稿日期:2022-12-29 修回日期:2023-06-08 发布日期:2023-10-08
  • 通讯作者: 董慧婷 E-mail:huiting.dong@zzu.edu.cn

Evaluation of the performance of a 34-layer ResNet model-based artificial intelligence application, in the diagnosis of skin diseases

Zhu Yajie, Lu Feng, Syed Mohammad Nooruddin Mahmood, Liu Xin, Li Xiaohong, Yu Jianbin, Dong Huiting   

  1. Department of Dermatology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
    Lu Feng was a postgraduate student in the Department of Dermatology, The First Affiliated Hospital of Zhengzhou University, and is now working at the Department of Dermatology, Henan Children′s Hospital, The Affiliated Children′s Hospital of Zhengzhou University, Zhengzhou Children′s Hospital, Zhengzhou 450018, China
    Syed Mohammad Nooruddin Mahmood was a foreign student in the Department of Dermatology, The First Affiliated Hospital of Zhengzhou University, and is now working at DrSyeds HealthCare, Hyderabad, Telangana 500001, India
    Liu Xin attended a refresher course in the Department of Dermatology, The First Affiliated Hospital of Zhengzhou University, and is now working at the Department of Dermatology, The Second People′s Hospital of Xinxiang, Xinxiang 453000, Henan, China
  • Received:2022-12-29 Revised:2023-06-08 Published:2023-10-08
  • Contact: Dong Huiting E-mail:huiting.dong@zzu.edu.cn

摘要: 【摘要】 目的 评估人工智能软件Autoderm在我国患者中诊断皮肤病的性能。方法 在郑州大学第一附属医院皮肤科前瞻性招募诊断明确的920例患者,每例患者上传1张临床图像至Autoderm进行诊断,然后计算其诊断的灵敏度、特异度、准确度和kappa值。结果 920例患者中,Autoderm可以诊断871例(94.7%),不能诊断49例(5.3%)。Autoderm的第1位/前3位诊断平均灵敏度为41.8%和65.8%,平均特异度为96.8%和91.5%,平均准确度为92.9%和89.9%。第1位/前3位诊断与皮肤科医生诊断总体一致性为中等(κ = 0.420、0.464)。但Autoderm对其不能诊断的病种也会给出5个肯定错误的诊断。结论 Autoderm可以诊断我国大部分患者的常见皮肤病,显示出中等灵敏度、高特异度和高准确度,但存在一定的误诊率。

关键词: 人工智能, 皮肤疾病, 诊断

Abstract: 【Abstract】 Objective To evaluate the performance of Autoderm, an artificial intelligence application, in the diagnosis of skin diseases in Chinese patients. Methods Totally, 920 patients with confirmed skin diseases were prospectively recruited in the Department of Dermatology, the First Affiliated Hospital of Zhengzhou University. A patient-provided clinical image per case was uploaded onto the Autoderm application for the diagnosis of skin diseases. The diagnostic sensitivity, specificity and accuracy of the Autoderm application were estimated, and the kappa values for the diagnostic agreement between the Autoderm application and dermatologists were calculated. Results Among the 920 patients, 871 (94.7%) could be diagnosed with an Autoderm′s in-distribution skin disease, whereas 49 (5.3%) had out-of-distribution skin diseases. According to the top 1 and 3 diagnoses given by the Autoderm application for the 920 patients separately, its mean diagnostic sensitivities were 41.8% and 65.8%, mean specificities 96.8% and 91.5%, and mean accuracies 92.9% and 89.9%, respectively, and there was moderate overall agreement between the Autoderm application and dermatologists (κ = 0.420, 0.464, respectively). However, for an out-of-distribution skin disease, the Autoderm application could output 5 definitely false diagnoses. Conclusion Autoderm may be used as as clinical decision support tool for the diagnosis of common skin diseases in most Chinese patients, with moderate diagnostic sensitivity, high specificity, and high accuracy, but misdiagnosis may occur.

Key words: Artificial intelligence, Skin disease, Diagnosis

引用本文

朱亚杰 卢枫 Syed Mohammad Nooruddin Mahmood 刘欣 李小红 于建斌 董慧婷. 一种基于34层ResNet模型的人工智能软件诊断皮肤病的性能评估[J]. 中华皮肤科杂志, 2023,56(10):948-952. doi:10.35541/cjd.20220925

Zhu Yajie, Lu Feng, Syed Mohammad Nooruddin Mahmood, Liu Xin, Li Xiaohong, Yu Jianbin, Dong Huiting. Evaluation of the performance of a 34-layer ResNet model-based artificial intelligence application, in the diagnosis of skin diseases[J]. Chinese Journal of Dermatology, 2023, 56(10): 948-952.doi:10.35541/cjd.20220925