  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.
