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在遗传性视网膜退化中,模两可的过渡区的光感受体疾病
Alexander Sumaroka1, Malgorzata Swider1, Tomas S Aleman1
1Center for Hereditary Retinal Degenerations, Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
Translational vision science & technology
|August 5, 2025
概括
在遗传性视网膜退化 (IRDs) 中,模两可的转变是常见的. 人工智能准确地对外视网膜疾病进行了分类,识别了患病区域附近的进展预测因素,这对临床试验至关重要.
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:
- 遗传性视网膜退化 (IRDs) 影响光受体外部 (OS).
- 光学连贯断层扫描 (OCT) 测量了内部和外部段结 (IS/OS) 或圆形区域 (EZ) 的IRD结果.
- EZ终点可能含糊不清,使疾病评估复杂化.
研究的目的:
- 通过使用广场OCT在每个视网膜部位的外部细分 (OS) 疾病的特征.
- 开发一个AI模型来分类OS疾病状态.
- 在IRD中确定疾病进展的预测因素.
主要方法:
- 在17名IRD患者 (2名ABCA4相关,15名RHO相关) 进行了广场OCT成像,持续时间约2年.
- 一个双层的人工智能架构将外视网膜疾病分为肯定存在的OS (DPOS),肯定缺席的OS (DAOS) 或可疑缺席的OS (QAOS).
- 将OS地图与串行成像进行比较,以评估稳定性和进展.
主要成果:
- 人工智能模型在分类OS疾病状态时实现了95%的准确性.
- 更广泛的可疑缺席的OS (QAOS) 区域表明从疾病到健康的过渡时间更长.
- 靠近更患病的区域预测了进展;靠近疾病的DPOS和QAOS像素显示出进展的可能性高出10倍.
结论:
- 在IRD结果中突然EZ转变的假设可能与疾病病理生理学不一致.
- 模两可的过渡区及其附近地区是高进展风险的关键指标.
- 这些地区可以作为IRD临床试验的有效终点.
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