在光学连贯断层扫描上早期检测视神经变化,使用深度学习对乳头和玻璃眼的风险分层进行深度学习
Anfei Li1, Anika K Tandon, Grace Sun
1Department of Ophthalmology (AL), New York Presbyterian Hospital, New York, New York; and Department of Ophthalmology (AKT, GS, MJD, CO), Weill Cornell Medicine, New York, New York.
概括
人工智能 (AI) 可以检测微妙的光学连贯断层扫描变化,以预测玻璃眼或乳腺瘤的进展. 这种人工智能方法有助于在人类分析显示没有重大变化时进行早期检测.
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:
- 人工智能 (AI) 越来越多地用于医学,特别是眼科.
- 当前的人工智能应用程序经常复制人类眼科医生的任务.
- 人工智能的分析能力可能能够检测出人类临床医生错过的微特征.
研究的目的:
- 评估AI在检测早期光学连贯性断层扫描 (OCT) 变化方面的能力.
- 使用人工智能预测乳头炎和玻璃眼的进展.
- 为了确定早期的标志性疾病进展的OCT,不明显的临床医生.
主要方法:
- 从患有 papilledema 和玻璃眼患者的预诊断性OCT扫描的回顾性收集.
- 根据杯与盘的比率对照对照组来考虑基线差异.
- 用OCT图像 (视网膜神经纤维层,垂直断层影像等) 重新训练视觉几何组-19模型. 预测疾病的进展.
主要成果:
- 人工智能模型使用视网膜神经纤维层厚度图预测了0.714精度和0.769回忆的乳头炎进展.
- 人工智能模型使用提取的垂直断层图谱预测了0.682精度和0.857回忆的玻璃眼病进展.
- 在精度回忆曲线下的区域为0.826的 papilledema 和0.785的玻璃眼.
结论:
- 人工智能算法可以检测早期的OCT变化,预测疾病进展,对临床医生来说是不可感知的.
- 这项概念验证研究突出了AI在眼科早期诊断和风险分层方面的潜力.
- 进一步的研究可以建立人工智能模型,以支持诊断眼病的临床决策.
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