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Natural language processing and medical imaging analysis for multi-modality computer assisted diagnosis of ophthalmic diseases

Award Number: R00LM014024
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2024
PERIOD OF PERFORMANCE END DATE: 08/31/2027

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Natural language processing and medical imaging analysis for multi-modality computer assisted diagnosis of ophthalmic diseases - The goal of this study is to develop and evaluate multi-modality artificial intelligence (AI)-assisted diagnosis models. Timely and accurate diagnosis of diseases for early intervention is key to maximizing patient outcomes. This is particularly true in ophthalmology, where timely treatment is crucial for minimizing vision loss. In recent years, AI has been widely applied in medical specialties such as ophthalmology through computer-assisted diagnosis, which provides scalable disease diagnosis with the potential for screening patients earlier. Yet, most methods use a single imaging modality for diagnosis, which fails to capture other modalities such as different imaging types, patient symptoms, medication history, or lab results information documented in electronic health records (EHRs) that are critical for disease diagnosis. Another important concern is the generalization capability of AI models. For example, only a limited number of studies have conducted external validations, and the impact of factors such as age, race, and gender on the generalization of AI models remains largely unknown in ophthalmology. The specific aims are to (1) propose NLP methods for clinical concept recognition and normalization from clinical notes; (2) develop multi-modality AI models that use medical images, free-text notes, and structured information for ophthalmic disease diagnosis; and (3) benchmark and investigate potential solutions to improve the downstream accountability of AI models, including external validations, subgroup analysis, and generalization to other diseases beyond ophthalmology.


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 = $248,980 )
2025	2025	YALE UNIV	150 MUNSON ST	NEW HAVEN	CT	06511	SOUTH CENTRAL CT	USA	Medical Library Assistance	001	4	8/22/2025	NON-COMPETING CONTINUATION	$248,980
2025	2024	YALE UNIV	150 MUNSON ST	NEW HAVEN	CT	06511	SOUTH CENTRAL CT	USA	Medical Library Assistance	000	3	8/8/2025	EXTENSION WITH OR WITHOUT FUNDS	$0
														Subtotal = $248,980

Issue Date FY: 2024 ( Subtotal = $249,000 )
2024	2024	YALE UNIV	150 MUNSON ST	NEW HAVEN	CT	06511	SOUTH CENTRAL CT	USA	Medical Library Assistance	000	3	9/1/2024	EXTENSION WITH OR WITHOUT FUNDS	$249,000
														Subtotal = $249,000

Grand Total All Awards = $497,980

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Natural language processing and medical imaging analysis for multi-modality computer assisted diagnosis of ophthalmic diseases

Award Number: R00LM014024

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/01/2024

PERIOD OF PERFORMANCE END DATE: 08/31/2027

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