Deep learning for identifying Alzheimer’s disease and mild cognitive impairment with optical coherence tomography angiography

Background/aims To develop a machine learning (ML) method with deep learning (DL) models for detecting Alzheimer’s disease (AD) and mild cognitive impairment (MCI) using optical coherence tomography angiography (OCTA) images. Methods OCTA images of 173 eyes of 129 subjects with AD (40 eyes, 28 subjects), MCI (62 eyes, 36 subjects) or cognitively normal (CN) (71 eyes, 65 subjects older than 49 years) were acquired. Two pretrained DL models, DINO and ResNet50, were used to extract features from OCTA images of the superficial vascular plexus and the deep vascular plexus. The extracted features were used to train three ML models, support vector machine (SVM), XGBoost and logistic regression with elastic net regularisation (LR-EN), to predict AD or MCI. For performance comparison, convolutional neural networks were also trained to predict AD or MCI. In addition, 14 biomarkers such as vessel density and foveal avascular zone area were extracted from OCTA images and used to train SVM, XGBoost and LR-EN. Results SVM with DINO features achieved the best performance with an area under the receiver operating characteristic curve (AUC) of 0.820, 0.756 and 0.695 in binary classification for discriminating AD versus CN, MCI versus CN and AD versus MCI, respectively. It also achieved the best average AUC of 0.741 in three-class classification for distinguishing AD, MCI and CN. Conclusion Our ML method achieved significantly better prediction accuracy than existing ML methods. Moreover, since our method trains classifiers using features extracted from OCTA images, it enables multiple organisations to collaborate on training an ML/DL model without sharing raw data, which can significantly increase the sample size of the training data, thereby improving the prediction accuracy of the trained model.