Deep learning model for automated diagnosis of moyamoya disease based on magnetic resonance angiography

Background This study explores the potential of the deep learning-based convolutional neural network (CNN) to automatically recognize MMD using MRA images from atherosclerotic disease (ASD) and normal control (NC). Methods In this retrospective study in China, 600 participants (200 MMD, 200 ASD and 200 NC) were collected from one institution as an internal dataset for training and 60 from another institution were collected as external testing set for validation. All participants were divided into training (N = 450) and validation sets (N = 90), internal testing set (N = 60), and external testing set (N = 60). The input to the CNN models comprised preprocessed MRA images, while the output was a tripartite classification label that identified the patient's diagnostic group. The performances of 3D CNN models were evaluated using a comprehensive set of metrics such as area under the curve (AUC) and accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize the CNN's decision-making process in MMD diagnosis by highlighting key areas. Finally, the diagnostic performances of the CNN models were compared with those of two experienced radiologists. Findings DenseNet-121 exhibited superior discrimination capabilities, achieving a macro-average AUC of 0.977 (95% CI, 0.928-0.995) in the internal test sets and 0.880 (95% CI, 0.786-0.937) in the external validation sets, thus exhibiting comparable diagnostic capabilities to those of human radiologists. In the binary classification where ASD and NC were group together, with MMD as the separate group for targeted detection, DenseNet-121 achieved an accuracy of 0.967 (95% CI, 0.886-0.991). Additionally, the Grad-CAM results for the MMD, with areas of intense redness indicating critical areas identified by the model, reflected decision-making similar to human experts. Interpretation This study highlights the efficacy of CNN model in the automated diagnosis of MMD on MRA images, easing the workload on radiologists and promising integration into clinical workflows.