深度学习的方法来检测儿童的玻璃眼,基于 periocular 照片
Yoshiyuki Kitaguchi1, Rina Hayakawa2, Rumi Kawashima3
1Department of Ophthalmology, Osaka University Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan. kitaguchi@ophthal.med.osaka-u.ac.jp.
Scientific reports
|June 22, 2023
概括
一个新的深度学习模型可以从眼睛照片中准确地检测儿童眼光. 这种人工智能工具在识别人类专家错过的病例方面表现有前途,有助于早期诊断和预防失明.
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:
- 童年玻璃眼瘤是儿童失明的主要原因.
- 准确和及时诊断儿童青光眼的存在重大挑战.
- 早期检测至关重要,以防止不可逆转的视力丧失.
研究的目的:
- 开发和评估一种深度学习 (DL) 模型,用于使用周期性照片检测儿童绿眼病.
- 与人类专家诊断相比,评估DL模型的性能.
- 为了确定DL模型超越人类考试人员的具体情况.
主要方法:
- 从被诊断为青光眼的儿童的回顾性照片收集.
- 使用深度学习框架与RepVGG架构进行自动识别.
- 采用五倍交叉验证来评估模型性能,测量AUC,灵敏度和特异性.
主要成果:
- 在DL模型中,整体AUC达到0.95.95.
- 该模型表现出高灵敏度 (0.85) 和特异性 (0.94).
- 在挑战性病例中,DL模型在检测青光眼方面超过了人类检查人员,例如那些没有角膜不透明或双侧角膜扩大的人.
结论:
- 开发的DL模型是诊断儿童绿眼病的高度准确的工具.
- 这种人工智能驱动的方法显示了识别遗漏病例和改善诊断率的潜力.
- DL模型为儿科眼科医生和玻璃眼专家提供了一个有前途的补充工具.
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