皮肤科医生和深度学习模型对诊断儿童白风的比较研究
Shijuan Yu1, Zhilin Chen2, Jingyi He3
1Department of Dermatology, Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation base of Child development and Critical Disorders, Chongqing Key Laboratory of Child Infection and Immunity, Chongqing 400014, China; The Seventh People's Hospital of Chongqing, Chongqing 400054, China.
使用皮肤扫描图像的深度学习模型在诊断儿童白风时显示出高准确度. 这些人工智能工具可以帮助皮肤科医生识别这种常见的儿科皮肤疾病.
科学领域:
- 皮肤病学 皮肤病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 儿童白风的诊断可能具有挑战性.
- 皮肤透视可提供皮肤病变的放大视图.
- 深度学习 (DL) 在医学图像分析方面表现有前途.
研究的目的:
- 为了评估DL模型的诊断性能,使用皮肤扫描图像用于儿童白风.
- 为了将DL模型的性能与临床医生的评估进行比较.
主要方法:
- 在474名儿科患者的3896张皮肤镜像上训练了ResNet152和DenseNet121 DL模型.
- 评估模型使用AUC,灵敏度,特异性,F1得分和精度.
- 与八位皮肤科医生评估的DL性能进行比较.
主要成果:
- DL模型实现了高诊断性能:ResNet152 (AUC 0.91) 和DenseNet121 (AUC 0.89) 这两种模式.
- ResNet152的准确率为83.08%,回忆率为86.84%.
- 基于临床的评估显示AUC为0.77.
结论:
- 使用皮肤扫描图像的DL模型对于诊断儿童白风是有效的.
- 这些人工智能工具与无助临床医生诊断相比,显示出更高的性能.
- DL模型在儿科皮肤病学中提供了宝贵的帮助,用于检测白风.
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