Artificial Intelligence Chest X-Ray Opportunistic Screening Model for Coronary Artery Calcium Deposition: A...

To create an opportunistic screening model to predict coronary calcium burden and associated cardiovascular risk using only commonly available frontal chest x-rays (CXR) and patient demographics.

We proposed a novel multitask learning framework and trained a model using 2121 patients with paired gated computed tomography scans and CXR images internally (Mayo Clinic) from January 1, 2012, to December 31, 2022, with coronary artery calcification (CAC) scores (0, 1-99, and 100+) as ground truths. Results from the internal training were validated on multiple external datasets (Emory University Healthcare and Taipei Veterans General Hospital-from January 1, 2012, to December 31, 2022) with significant racial and ethnic differences.

Classification performance between 0, 1-99, and 100+ CAC scores performed moderately on both the internal test and external datasets, reaching average f1-scores of 0.71±0.04 for Mayo, 0.65±0.02 for Emory University Healthcare, and 0.70±0.06 for Taipei Veterans General Hospital. For the clinically relevant risk identification, the performance of our model on the internal and 2 external datasets reached area under the receiver operating curves of 0.86±0.02, 0.77±0.03, and 0.82±0.03 for 0 versus 400+, respectively. For 0 versus 100+, we achieved area under the receiver operating curve of 0.83±0.03, 0.71±0.02, and 0.78±0.01, respectively. Prospective evaluation across 3 Mayo Clinic sites is on par with the external validations and reports only minimal temporal drift.

Open-source fusion artificial intelligence-CXR model performed better than existing state-of-the-art models for predicting CAC scores only on internal cohort, with robust performance on external datasets. This proposed model may be useful as a robust, first-pass opportunistic screening method for cardiovascular risk from regular CXR.