Department of Ophthalmology, Konkuk University School of Medicine, Konkuk University Medical Center, Seoul, Republic of Korea.
Kong Eye Hospital, Seoul, Republic of Korea.
Transl Vis Sci Technol. 2024 Sep 3;13(9):3. doi: 10.1167/tvst.13.9.3.
We evaluated the features predicting visual acuity (VA) after one year in neovascular age-related macular degeneration (nAMD) patients.
A total of 527 eyes of 506 patients were included. Machine learning (ML) models were trained to predict VA deterioration beyond a logarithm of the minimum angle of resolution of 1.0 after 1 year based on the sequential addition of multimodal data. BaseM models used clinical data (age, sex, treatment regimen, and VA), SegM models included fluid volumes from optical coherence tomography (OCT) images, and RawM models used probabilities of visual deterioration (hereafter probability) from deep learning classifiers trained on baseline OCT (OCT0) and OCT after three loading doses (OCT3), fluorescein angiography, and indocyanine green angiography. We applied SHapley Additive exPlanations (SHAP) for machine learning model interpretation.
The RawM model based on the probability of OCT0 outperformed the SegM model (area under the receiver operating characteristic curve of 0.95 vs. 0.91). Adding probabilities from OCT3, fluorescein angiography, and indocyanine green angiography to RawM showed minimal performance improvement, highlighting the practicality of using raw OCT0 data for predicting visual outcomes. Applied SHapley Additive exPlanations analysis identified VA after 3 months and OCT3 probability values as the most influential features over quantified fluid segments.
Integrating multimodal data to create a visual predictive model yielded accurate, interpretable predictions. This approach allowed the identification of crucial factors for predicting VA in patients with nAMD.
Interpreting a predictive model for 1-year VA in patients with nAMD from multimodal data allowed us to identify crucial factors for predicting VA.
我们评估了预测新生血管性年龄相关性黄斑变性（nAMD）患者一年后视力（VA）的特征。
共纳入 506 例 527 只眼。基于多模态数据的序贯添加，训练机器学习（ML）模型来预测一年后对数最小分辨角视力（VA）恶化超过 1.0。BaseM 模型使用临床数据（年龄、性别、治疗方案和 VA），SegM 模型包括来自光学相干断层扫描（OCT）图像的液体量，而 RawM 模型使用基于基线 OCT（OCT0）和三次负荷剂量后（OCT3）的深度学习分类器训练的视觉恶化概率（以下简称概率），荧光素血管造影和吲哚菁绿血管造影。我们应用 SHapley Additive exPlanations（SHAP）进行机器学习模型解释。
基于 OCT0 概率的 RawM 模型优于 SegM 模型（接受者操作特征曲线下面积为 0.95 比 0.91）。将 OCT3、荧光素血管造影和吲哚菁绿血管造影的概率添加到 RawM 中，显示出最小的性能改善，突出了使用原始 OCT0 数据预测视觉结果的实用性。应用 SHapley Additive exPlanations 分析确定了 3 个月后 VA 和 OCT3 概率值是对定量液体段影响最大的特征。
整合多模态数据创建视觉预测模型可实现准确、可解释的预测。这种方法允许识别预测 nAMD 患者 VA 的关键因素。
杨希
