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深度学习视觉场全球指数预测与光学连贯性断层扫描参数在玻璃眼患者

Dongbock Kim¹, Sat Byul Seo¹, Seong Joon Park¹

¹Department of Mathematics Education, School of Education, Kyungnam University, 7 Kyugnamdaehak‑ro, Masanhappo‑gu, Changwon, Gyeongsangnam-do, 51767, Republic of Korea.

Scientific reports

|October 25, 2023

概括

一个深度学习模型准确地从光学连贯断层扫描 (OCT) 扫描中预测视野索引 (MD,PSD,VFI). 这种人工智能工具有助于青光眼的诊断和监测,特别是当视觉现场测试不切实际时.

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:

青光眼的诊断和监测依赖于视野 (VF) 测试和光学连贯性断层扫描 (OCT).
从OCT参数预测关键VF全球指数 (平均偏差,模式标准偏差,视野指数) 可以简化患者管理.
目前的VF测试是耗时和主观的,需要替代的诊断方法.

研究的目的:

开发和评估一个深度学习模型,从光学相干断层扫描 (OCT) 参数预测视野 (VF) 全球索引.
评估深度神经网络 (DNN) 在估计平均偏差 (MD),模式标准偏差 (PSD) 和视野指数 (VFI) 中的准确性.

主要方法:

使用OCT参数训练了一个深度神经网络 (DNN) 算法,包括Bruch的膜开放-最小边缘宽度 (BMO-MRW) 和视网膜神经纤维层 (RNFL) 厚度.
这项研究包括了多样化的眼睛队列,其中包括青光眼的疑似患者和各种类型的青光眼 (NTG,PACG,PEXG,POAG).
使用平均绝对误差 (MAE) 和皮尔森相关系数来评估模型性能.

主要成果:

DNN模型对VF指数表现出强大的预测能力:MAE在MD的1.9-2.9dB,PSD的1.6-2.0dB,VFI的5.0-7.0%之间.
皮尔森的相关系数表明预测和实际VF值之间的良好一致,在MD的0.76-0.85,PSD的0.74-0.82和VFI的0.70-0.81之间.
该模型在预测不同青光眼分类的VF全球指数方面取得了高准确性.

结论:

开发的深度学习模型显示出有很大的潜力,有助于诊断和跟踪治疗眼.
这种由人工智能驱动的方法为预测视野状态提供了有价值的替代方案,特别是当传统的视野测试不可行时.
该模型能够从OCT数据中预测VF指数,这可能会提高绿内障护理的效率和可访问性.

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