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临床医生领导的无代码深度学习用于检测使用光盘成像检测的乳头和伪乳头

Riddhi Shenoy¹, Gurtek Singh Samra¹, Rishi Sekhri¹

¹Ulverscroft Eye Unit, School of Psychology and Vision Sciences, College of Life Sciences, University of Leicester, Leicester, UK.

Translational vision science & technology

|February 20, 2026

概括

自动机器学习在使用OCT图像来区分眼膜炎与其他视觉盘疾病方面表现有前途. 这项技术为临床团队提供了一个可扩展的解决方案,以准确诊断 papilledema.

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:

区分 papilledema 和伪 papilledema 对于及时诊断和避免不必要的程序至关重要.
光学连贯断层扫描 (OCT) 和其近红外反射率 (NIR) 图像提供了详细的视觉神经头部可视化.

研究的目的:

评估自动机器学习 (AutoML) 平台在区分皮囊与正常光盘和光盘干燥 (ODD) 的性能.
评估AutoML模型在使用NIR OCT图像等级 papilledema的严重程度的能力.

主要方法:

一项回顾性队列研究分析了289名患有正常圆盘,乳头瘤或ODD的患者的813张NIR OCT图像.
三个AutoML平台 (亚马逊再识别,Medic Mind,Google Vertex) 进行了分类和严重程度分级任务的测试.
性能指标包括曲线下的面积 (AUC),精度,回忆和F1分数.

主要成果:

亚马逊Rekognition获得了最高的性能,其AUC为0.90,F1得分为0.81,用于区分皮囊和正常/ODD.
对于分级 papilledema 严重程度,亚马逊 Rekognition 也领先,其 AUC 为 0.90,F1 评分为 0.79.
与亚马逊Rekognition相比,Google Vertex和Medic Mind的准确性略低,错误分类率更高.

结论:

自动ML平台可用于使用NIR OCT成像对 papilledema进行分类.
这些模型表明,在临床诊断 papilledema. 这是一个可访问和可扩展的解决方案的潜力.
需要进一步的外部验证,以确认AutoML在 papilledema评估中的临床实用性.

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