Assessment of Clonal Hematopoiesis of Indeterminate Potential from Cardiac Magnetic Resonance Imaging using Deep Learning in a Cardio-oncology Population

Background: We propose a novel method to identify who may likely have clonal hematopoiesis of indeterminate potential (CHIP), a condition characterized by the presence of somatic mutations in hematopoietic stem cells without detectable hematologic malignancy, using deep learning techniques. Methods: We developed a convolutional neural network (CNN) to predict CHIP status using 4 different views from standard delayed gadolinium-enhanced cardiac magnetic resonance imaging (CMR). We used 5-fold cross validation on 82 cardio-oncology patients to assess the performance of our model. Different algorithms were compared to find the optimal patient-level prediction method using the image-level CNN predictions. Results: We found that the best model had an area under the receiver operating characteristic curve of 0.85 and an accuracy of 82%. Conclusions: We conclude that a deep learning-based diagnostic approach for CHIP using CMR is promising.