Project Summary/Abstract AutoChamber is part of an initiative spearheaded by HeartLung.AI, in collaboration with a team of esteemed physician researchers from leading US academic institutions. The AI tool is designed to opportunistically screen existing chest CT scans stored in hospital PACS or new scans obtained for any medical evaluations such as coronary artery calcium scan, lung cancer screening or diagnostic work ups following accidents or pneumonia and such, to detect asymptomatic enlarged cardiac chambers and thick left ventricular wall without any X-ray contrast enhanced agent. The human eye cannot distinguish between the inside of cardiac chambers and the cardiac wall without contrast enhanced agent whereas, the AutoChamber AI which was trained using contrast-enhanced cardiac CT scans can accurately detect and measure cardiac chambers volume and left ventricular wall mass. The project underscores a significant stride towards addressing the unmet needs in early detection and prevention of heart failure and stroke, potentially saving lives, and reducing healthcare costs. It aligns with the mission of fostering innovative solutions that offer opportunistic diagnoses using existing CT scans or CT scans obtained for other reasons unrelated to cardiovascular disease. Over 76 million CT scans were performed in the United States in 2019, an estimated 230 CT procedures per 1,000 people with chest CT accounting for 12.7 million that can be used for AutoChamber™ screening. AutoChamber™ is the newest AI technology that has received a Medical Device Breakthrough Designation from the FDA as recently as August 30, 2023. Building upon the encouraging results gleaned from the MESA database, the project aims to extend its research to the Framingham Heart Study database, the largest prospective cohort study of cardiovascular diseases with a rich repository of thousands of CT scans to further validate the performance of AutoChamber™. The project delineates three specific aims: Aim 1: To apply AutoChamber™ on CAC scans from the Framingham database and compare the cardiac chamber volume measurements with those obtained from contrast-enhanced MRI. Aim 2 To evaluate the performance of AutoChamber™ in predicting incidents of atrial fibrillation and stroke, benchmarking against existing standards like the CHARGE-AF Score and CHADS-VASc Score. Aim 3 To assess the capability of AutoChamber™ in detecting prevalent left ventricular hypertrophy and asymptomatic left ventricular dysfunction, and predicting incidents of heart failure, using parameters like left ventricular and left ventricular wall volumetry.