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Deep Learning Assessment of the Right Ventricle: Function, Etiology, and Prognosis

Award Number: R00HL157421
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 04/15/2025
PERIOD OF PERFORMANCE END DATE: 01/31/2026

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Deep Learning Assessment of the Right Ventricle: Function, Etiology, and Prognosis - ABSTRACT Heart failure imposes a tremendous burden of morbidity and mortality, costing the United States in excess of $31 billion annually. An increasingly recognized major determinant of outcomes in heart failure is right ventricular (RV) dysfunction. However, the nature and character of RV contribution to cardiovascular outcomes remains poorly understood, largely due to the imprecision of imaging and interpretation of RV morphology and function. Echocardiography, with its high temporal resolution and low cost of acquisition, serves as frontline cardiovascular imaging and a mainstay in approaches to assessing RV morphology and function. However, echocardiographic imaging of the RV is limited by factors that include technical variation in image acquisition and heterogeneity in image assessment as well as overall interpretation. We postulate that deep learning based phenotyping can offer the ability to not only more precisely characterize RV function but also classify RV imaging phenotypes according to etiologic disease states and, even further, refine prognostic evaluations of future cardiovascular risk. Therefore, in Aim 1, we will use video-based deep learning segmentation models to assess RV function, evaluate its cross-sectional relation with a range of expert-measured parameters, and examine its variation in the context of patient characteristics derived from large hospital-based cohorts. In Aim 2, we will use video-based deep learning models to produce imaging-based classification of RV disease and assess the ability of unsupervised approaches to classify RV dysfunction into various categories of disease etiology. In Aim 3, we will use models developed in part from training in Aims 1 and 2 to predict major cardiovascular outcomes including heart failure in addition to coronary artery disease, stroke, and cardiovascular death in both hospital- based and community-based cohorts. The overarching goal of this proposal is to improve the precision and standardization of RV phenotyping and determine the extent to which deep learning models can augment human assessment of the RV. This research will be accomplished in the setting of a comprehensive career development program designed to provide the candidate with the skills needed to become an independent physician-scientist in cardiovascular medicine and translational imaging science. An advisory committee of established scientists/mentors in the fields of cardiac imaging, deep learning, data science, and translational science will guide the candidate in his transition to scientific independence over the course of the award period.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2025 ( Subtotal = $222,739 )
2025	2025	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Cardiovascular Diseases Research	003	5	6/25/2025	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$11,874
2025	2025	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Cardiovascular Diseases Research	000	5	4/15/2025	CHANGE OF GRANTEE / TRAINING INSTITUTION / AWARDING INSTITUTION	$222,739
2025	2024	CEDARS-SINAI MEDICAL CENTER	8700 BEVERLY BLVD	WEST HOLLYWOOD	CA	90048	LOS ANGELES	USA	Cardiovascular Diseases Research	002	4	6/25/2025	NON-COMPETING CONTINUATION	-$11,874
2025	2024	CEDARS-SINAI MEDICAL CENTER	8700 BEVERLY BLVD	WEST HOLLYWOOD	CA	90048	LOS ANGELES	USA	Cardiovascular Diseases Research	001	4	5/9/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $222,739

Issue Date FY: 2024 ( Subtotal = $248,979 )
2024	2024	CEDARS-SINAI MEDICAL CENTER	8700 BEVERLY BLVD	WEST HOLLYWOOD	CA	90048	LOS ANGELES	USA	Cardiovascular Diseases Research	000	4	1/22/2024	NON-COMPETING CONTINUATION	$248,979
														Subtotal = $248,979

Issue Date FY: 2023 ( Subtotal = $248,979 )
2023	2023	CEDARS-SINAI MEDICAL CENTER	8700 BEVERLY BLVD	WEST HOLLYWOOD	CA	90048	LOS ANGELES	USA	Cardiovascular Diseases Research	000	3	1/23/2023	EXTENSION WITH OR WITHOUT FUNDS	$248,979
														Subtotal = $248,979

Grand Total All Awards = $720,697

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Deep Learning Assessment of the Right Ventricle: Function, Etiology, and Prognosis

Award Number: R00HL157421

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 04/15/2025

PERIOD OF PERFORMANCE END DATE: 01/31/2026

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