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Leveraging Multi-Modal AI for Selective Use of Ultrasound in Breast Cancer Screening

Award Number: R01EB036530
ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 08/12/2025
PERIOD OF PERFORMANCE END DATE: 05/31/2029

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Leveraging Multi-Modal AI for Selective Use of Ultrasound in Breast Cancer Screening - Project Summary Supplemental breast screening ultrasound is recommended for women with dense breasts, accounting for nearly half of U.S. women over age 40. While breast ultrasound improves cancer detection rates, its cost-effectiveness remains low for women at average risk of breast cancer, with a cost-effectiveness ratio of $728,000 per quality- adjusted life year (QALY) gained. This limited efficiency is driven by high false-positive rates in breast ultrasound interpretation, leading to unnecessary diagnostic workups and biopsies, and by one-size-fits-all guidelines that recommend supplemental ultrasound for all women with dense breasts. This proposal aims to improve the diagnostic accuracy and cost-effectiveness of breast cancer screening by building an AI system to determine the necessity of supplemental screening ultrasound. Our core hypothesis is that in a substantial portion of the screening population, it is possible to forgo currently guideline-recommended supplemental ultrasounds without impacting diagnostic accuracy. To verify this hypothesis, we propose the following aims. First, we will build a multi-modal deep neural network, Multi-Modal Diagnoser (MMD), that integrates current and prior mammographic and ultrasound imaging data to improve detection of breast cancer. To train MMD, we will assemble a substantial dataset of over 2.12 million exams (420,000 patients) from our institution, as well as two external datasets from other institutions for independent evaluation. MMD will serve as the foundation for our second model, the Ultrasound Benefit Predictor (UBP). UBP will utilize screening mammography images to determine the necessity of supplemental ultrasound for women with dense breasts through an interpretability mechanism that highlights specific mammographic regions where ultrasound could add diagnostic value. Finally, we will critically assess these two elements of our AI system through a retrospective study, examining their impact on radiologists' performance and cost-effectiveness through the lens of patients’ QALY. This project seeks to pioneer the integration of AI in breast cancer screening, leading to more personalized patient care and optimized healthcare resource allocation.


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: 2026 ( Subtotal = $654,712 )
2026	2026	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	2	6/9/2026	NON-COMPETING CONTINUATION	$654,712
														Subtotal = $654,712

Issue Date FY: 2025 ( Subtotal = $672,087 )
2025	2025	NEW YORK UNIVERSITY	550 1ST AVE	NEW YORK	NY	10016	NEW YORK	USA	Discovery and Applied Research for Technological Innovations to Improve Human Health	000	1	8/12/2025	NEW	$672,087
														Subtotal = $672,087

Grand Total All Awards = $1,326,799

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Leveraging Multi-Modal AI for Selective Use of Ultrasound in Breast Cancer Screening

Award Number: R01EB036530

ORGANIZATION: NATIONAL INSTITUTE Of BIOMEDICAL IMAGING & BIOENGINEERING

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 08/12/2025

PERIOD OF PERFORMANCE END DATE: 05/31/2029

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