The lack of reliable biomarkers makes predicting the conversion from intermediate to neovascular age-related macular degeneration (iAMD, nAMD) a challenging task. We develop a Deep Learning (DL) model to predict the future risk of conversion of an eye from iAMD to nAMD from its current OCT scan. Although eye clinics generate vast amounts of longitudinal OCT scans to monitor AMD progression, only a small subset can be manually labeled for supervised DL. To address this issue, we propose Morph-SSL, a novel Self-supervised Learning (SSL) method for longitudinal data. It uses pairs of unlabelled OCT scans from different visits and involves morphing the scan from the previous visit to the next. The Decoder predicts the transformation for morphing and ensures a smooth feature manifold that can generate intermediate scans between visits through linear interpolation. Next, the Morph-SSL trained features are input to a Classifier which is trained in a supervised manner to model the cumulative probability distribution of the time to conversion with a sigmoidal function. Morph-SSL was trained on unlabelled scans of 399 eyes (3570 visits). The Classifier was evaluated with a five-fold cross-validation on 2418 scans from 343 eyes with clinical labels of the conversion date. The Morph-SSL features achieved an AUC of 0.766 in predicting the conversion to nAMD within the next 6 months, outperforming the same network when trained end-to-end from scratch or pre-trained with popular SSL methods. Automated prediction of the future risk of nAMD onset can enable timely treatment and individualized AMD management.

翻译：缺乏可靠的生物标志物使得预测从中间型年龄相关性黄斑变性（iAMD）向新生血管型年龄相关性黄斑变性（nAMD）的转化成为一项具有挑战性的任务。我们开发了一种深度学习（DL）模型，用于根据当前OCT扫描预测患者眼睛从iAMD转化为nAMD的未来风险。尽管眼科诊所大量采集纵向OCT扫描以监测AMD进展，但只有少量子集可通过人工标注用于监督学习。为解决该问题，我们提出Morph-SSL——一种针对纵向数据的新型自监督学习（SSL）方法。该方法利用不同就诊时获取的无标注OCT扫描对，通过将前一次就诊的扫描形变至下一次就诊的扫描来运作。解码器预测形变所需的变换，并确保生成平滑的特征流形，从而通过线性插值生成两次就诊间的中间扫描。随后，经Morph-SSL训练的特征被输入分类器，该分类器以监督学习方式训练，使用S型函数模拟转化为nAMD时间的累积概率分布。Morph-SSL在399只眼睛（3570次就诊）的无标注扫描数据上训练，而分类器则采用五折交叉验证，在343只眼睛（2418次扫描）的临床标注转化日期数据上评估。在预测未来6个月内转化为nAMD的任务中，Morph-SSL特征取得了0.766的AUC值，优于从头端到端训练同一网络或使用其他流行SSL方法预训练的结果。自动预测未来nAMD发病风险可实现及时治疗与个性化AMD管理。