基于深度学习的地理缩细分:在现实世界的临床队列中进行多中心,多设备验证
Hasenin Al-Khersan1, Simrat K Sodhi2, Jessica A Cao1
1Retina Consultants of Texas, Houston, TX 77070, USA.
Diagnostics (Basel, Switzerland)
|October 29, 2025
概括
一个新的深度学习算法准确地对与年龄相关的黄斑变性 (AMD) 患者的地理缩 (GA) 进行细分,使用光学连贯性断层扫描 (OCT) 扫描. 这种自动化方法与手动分级在不同OCT设备和患者类型中具有很高的一致性.
科学领域:
- 眼科医生 眼科 眼科
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 地理缩 (GA) 是与年龄相关的黄斑变性 (AMD) 视力丧失的主要原因.
- 精确的GA细分对于监测疾病进展和评估治疗至关重要.
- 目前的手动细分方法可能耗时且主观.
研究的目的:
- 开发和验证用于自动化GA细分的深度学习算法.
- 用光学连贯断层扫描 (OCT) 图像来评估算法的性能.
- 评估算法在常规临床实践中的适用性.
主要方法:
- 在模型构建中使用了3D U-Net深度学习架构.
- 该算法在GA患者的OCT扫描上受过训练和验证,有和没有新血管AMD (nAMD).
- 模型的准确性用子相似系数 (DSC) 和相关性 (r2) 来量化,将自动细分与手动标签进行比较.
主要成果:
- 该算法实现了Spectralis OCT数据的0.83 (r2=0.91) 的平均DSC,而Cirrus OCT数据的0.82 (r2=0.88).
- 该模型与手动GA分级在两个不同的OCT设备中表现出强烈一致.
- 即使在同步使用nAMD.的病例中,表现仍然很强.
结论:
- 深度学习算法在AMD患者中提供GA的准确和自动细分.
- 该模型在设备和患者子组中的一致性能表明其具有显著的临床实用性.
- 这种自动化方法有可能在常规临床环境中简化GA评估.
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