预测视觉敏度的病态近视与近视性胆道神经血管化治疗与抗血管内皮生长因子使用基于光学连贯断层图像的深度神经网络的视觉敏度的预测
Migyeong Yang1, Jinyoung Han1,2, Ji In Park3
1Department of Applied Artificial Intelligence, Sungkyunkwan University, Seoul 03603, Republic of Korea.
Biomedicines
|August 26, 2023
概括
人工智能模型准确地预测了近视性冠状腺新血管化 (mCNV) 患者的视力敏度. 整合OCT图像和患者数据可以提高视觉预后和治疗评估的预测准确性.
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:
- 近视性冠状腺新血管化 (mCNV) 是病态近视症视力丧失的重要原因.
- 预测mCNV患者的视觉预后目前具有挑战性.
研究的目的:
- 开发和评估人工智能 (AI) 模型,用于预测mCNV患者的视力敏度 (VA).
- 为了比较AI模型的性能,使用不同的光连贯断层扫描 (OCT) 数据输入.
主要方法:
- 开发了两种AI模型,使用了279名mCNV患者的基线数据,包括OCT图像 (水平/垂直切割与25个体积扫描),VA和人口统计数据.
- 使用确定系数 (R2) 和根平均平方误差 (RMSE) 在治疗后1,2,3年评估模型性能.
主要成果:
- 两种人工智能模型都在1,2,3年内预测VA方面表现出很高的表现.
- 使用多体积OCT扫描的模型与仅使用水平/垂直切割的模型相比,显示出更好的预测性能.
- 高的R2值 (0.911,0.894,0.891) 和低的RMSE值表明强大的预测准确性.
结论:
- 结合基线VA,OCT图像和注射后数据的AI模型可以有效地预测mCNV患者的视力敏度.
- 这些人工智能工具可以帮助预测视觉预后,并评估mCNV的治疗结果.
- 使用多体积的OCT扫描提高了AI模型对mCNV的预测能力.
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