DOI: 10.1161/circ.148.suppl_1.17610 ISSN: 0009-7322

Abstract 17610: A Novel Approach Using Deep Neural Networks on Clinical Cardiac MRIs Can Identify Patients With Clonal Hematopoiesis of Indeterminate Potential With High Accuracy

Allen Ryu, Anthos Christofides, Carlos Matute Martinez, Ritujith Jayakrishnan, Shawn Ahn, Ana Ferrigno Guajardo, Jeacy Espinoza, Nathan Chen, Alokkumar Jha, Yunju Im, Stephanie Halene, Jennifer VanOudenhove, HAMID MOJIBIAN, James Duncan, Nicha Dvornek, Jennifer M Kwan
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Background: Clonal hematopoiesis of indeterminate potential (CHIP) has been shown to be an independent risk factor for heart disease and heart failure. DNA sequencing is the gold standard to diagnose CHIP. It is unclear if deep learning techniques using routine clinical imaging can aid in identifying those who are likely to have CHIP and thus likely at increased risk for heart disease and heart failure.

Hypothesis: We hypothesized that deep learning models trained on routine cardiac MRI (CMR) would be able to help predict who has CHIP

Methods: We enrolled patients into an IRB approved study to evaluate for CHIP and associated imaging findings. Whole exome sequencing and our somatic variant pipeline was used to identify those who had CHIP. Various machine learning techniques were employed for training and testing, including SVM, random forest and convolutional neural networks (CNN) to predict who had CHIP from CMR imaging.

Results: We enrolled 98 unique patients with CMR and found that 40 (41%) had CHIP. Majority were female 65 (66%) and there was no significant difference in median age between CHIP vs no CHIP (69, IQR 62-78 vs 65, IQR 54-72). There were no significant differences between CHIP vs no CHIP patients in co-morbidities such as CAD, DM, HTN, HF. Compared to SVM and random forest, the best performing model with high sensitivity, specificity and accuracy was derived from CNN (AUC 0.64 vs 0.71 vs 0.85 respectively). We found that multi-view MRI inputs were more accurate than single view MRI inputs for the CNN model. Using an unsupervised approach with CNN and multi-view input, we were able to predict CHIP with 82% accuracy.

Conclusions: CNN is a promising deep learning technique that can accurately classify CHIP and thus those at higher cardiovascular disease risk. Ongoing analyses are underway to validate these findings using the UK Biobank and TOPMED datasets and predict who will develop cardiomyopathy with CHIP.

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