在皮肤镜像上训练并通过近距离图像挑战的卷积神经网络的稳定性
Anastasia Sophie Vollmer1, Julia Katharina Winkler1, Katharina Susanne Kommoss1
1Department of Dermatology, University medical Center Heidelberg, Heidelberg, Germany.
概括
对于皮肤病变诊断的深度学习模型显示,当使用近距离图像而不是皮肤镜像图像时,敏感性降低. 建议对这些模型的临床图像进行微调,以提高在现实环境中的准确性.
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:
- 深度学习-卷积神经网络 (DL-CNNs) 显示了使用皮肤镜检查的皮肤病变诊断的高准确性.
- 临床环境往往缺乏皮肤镜装置,需要使用近距离图像.
研究的目的:
- 为了评估DL-CNN在皮肤镜像上训练的性能,当它应用于近距离皮肤病变图像时.
- 评估DL-CNNs在不同成像条件下的强度和跨域适应性.
主要方法:
- 一个DL-CNN (Moleanalyzer pro) 在129,487张皮肤镜像上接受训练,在350个皮肤病变上进行了测试.
- 每个病变都在临床上 (近距离) 和皮肤上进行了成像.
- 组织病理学或专家共识作为参考标准;灵敏度,特异性和ROC-AUC是主要结局.
主要成果:
- 在皮肤镜图像上,DL-CNNs实现了高性能 (ROC-AUC:0.866).
- 在近距离图像上,性能显著下降,灵敏度降低 (60.5%) 和特异性增加 (79.4%),导致ROC-AUC (0.780) 较低.
- 统计分析证实皮肤镜像和近距离图像之间的性能指标存在显著差异 (p < 0.001对于灵敏度).
结论:
- 经过皮肤镜数据训练的DL-CNN在近距离图像上表现出诊断局限性.
- 降低灵敏度和变化的特异性突出显示了皮肤病人工智能模型中需要提高跨领域适应性的需求.
- 建议使用临床图像微调DL-CNNs,以提高现实世界的诊断准确性.
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