基于深度学习的眼睛表面瘤疾病诊断模型
Rie Sakata1, Taiyo Shijo1, Yuta Ueno2
1Department of Ophthalmology, Tokyo Dental College Ichikawa General Hospital, Chiba, Japan.
American journal of ophthalmology
|February 26, 2026
概括
一种深度学习 (DL) 模型在诊断常见的眼睛表面瘤 (如皮和肢体皮质瘤) 中显示出高准确性. 然而,它对黑色素瘤等罕见恶性瘤的性能需要进一步改进.
科学领域:
- 眼科医生 眼科 眼科
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 眼表面瘤包括一系列良性和恶性疾病.
- 准确和及时的诊断对于有效的治疗和患者的结果至关重要.
- 深度学习 (DL) 为医学诊断中的自动图像分析提供了潜力.
研究的目的:
- 开发一种深度学习 (DL) 模型,用于诊断眼表面瘤.
- 评估开发的DL模型的诊断性能.
- 为了比较DL模型的性能与人类专家诊断.
主要方法:
- 开发了一个基于YOLOv5架构的DL模型.
- 该模型在7个疾病类别的1491张眼睛表面图像上进行了训练.
- 使用299张外部图像验证了表现,并与角膜专家,眼科医生和住院医生的评估进行了比较.
主要成果:
- DL模型实现了96.0%的正预测值 (PPV),超过了人类专家.
- 对于pterygium (96.7%) 和肢体皮质 (93.5%) 观察到高度疾病特异性的PPV.
- 该模型对大多数常见疾病具有高灵敏度和特异性,对罕见恶性瘤 (PPV 38.5%) 和MALT淋巴瘤 (PPV 57.9%) 等罕见恶性瘤的性能较低.
结论:
- 该DL模型显示显著的希望作为一个支持的诊断工具,用于常见的眼睛表面瘤.
- 需要进一步改进,以提高罕见和侵袭性恶性瘤的诊断准确度.
- 这项研究强调了人工智能在增强眼科诊断能力方面的潜力.
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