Limb salvage prediction in peripheral artery disease patients using angiographic computer vision

BackgroundPeripheral artery disease (PAD) outcomes often rely on the expertise of individual vascular units, introducing potential subjectivity into disease staging. This retrospective, multicenter cohort study aimed to demonstrate the ability of artificial intelligence (AI) to provide disease staging based on inter-institutional expertise by predicting limb outcomes in post-interventional pedal angiograms of PAD patients, specifically in comparison to the inframalleolar modifier in the Global Limb Anatomic Staging System (IM GLASS).MethodsWe used computer vision (CV) based on the MobileNetV2 model, implemented via TensorFlow.js library, for transfer learning and feature extraction from 518 pedal angiograms of PAD patients with known 3-month limb outcomes: 218 salvaged limbs, 140 minor amputations, and 160 major amputations.ResultsAfter 43 epochs of training with a learning rate of 0.001 and a batch size of 16, the model achieved a validation accuracy of 95% and a test accuracy of 93% in differentiating salvaged limbs from amputations. In manual testing with 45 angiograms excluded from the training, validation, and test processes, the AI predicted mean limb salvage probabilities of 96% for actual salvaged limbs, 27% for minor amputations, and 17% for major amputations (p-value < .001). The correlation coefficient between the CV model-predicted outcome and the actual outcome for these 45 angiograms was 0.7, nearly five times higher than that between the IM GLASS pattern and the actual outcome (0.14).ConclusionComputer vision can analyze angiograms and predict disease outcomes, demonstrating a significant correlation between predicted and actual limb salvage rates, outperforming IM GLASS segmentation by a vascular specialist. It has the potential to provide immediate and precise treatment results during vascular interventions, tailored to (inter)institutional expertise, and enhance individualized decision-making.