深度学习用于诊断Tinea Corporis和Tinea Cruris的疾病
Narachai Julanon1, Anupol Panitchote2, Praisan Padungweang3
1Division of Dermatology, Department of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Journal of cutaneous medicine and surgery
|November 16, 2025
概括
一个新的深度学习模型准确地诊断着 tinea corporis 和 tinea cruris,性能与皮肤科医生相当. 这种人工智能工具在改善各种临床环境中的真菌感染诊断方面表现有前途.
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:
- 体 (Tinea corporis) 和皮 (Tinea cruris) 经常被误诊.
- 关于用于诊断这些真菌感染的深度学习的研究有限.
研究的目的:
- 开发一种深度学习模型,使用临床图像将皮肤植物病与其他皮肤疾病区分开来.
- 将模型的诊断性能与医生的诊断性能进行比较.
主要方法:
- 对1400张临床图像进行了回顾性分析 (皮肤植物病与非皮肤植物病).
- 采用了集成分类和细分的多任务深度学习模型.
- 模型的性能通过图像测试与皮肤科医生和非皮肤科医生进行了基准测试.
主要成果:
- 深度学习模型的AUC值为0.84,灵敏度为0.80,特异性为0.71.
- 诊断性能与皮肤科医生 (AUC 0.80) 相当,并且优于非皮肤科医生 (AUC 0.75).
- 该模型在诊断亚洲皮肤类型 (菲茨帕特里克III-IV) 的虫感染方面表现出有效性.
结论:
- 深度学习模型可以有效地诊断tinea corporis/cruris.
- 开发的AI模型展示了与皮肤科医生同等的诊断能力.
- 这项技术有可能改善真菌感染的诊断,特别是在多样化的群体中.
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