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DeepCertainty: Deep Learning for Contextual Diagnostic Uncertainty Measurement in Radiology Reports

Award Number: R21LM014032
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/01/2023
PERIOD OF PERFORMANCE END DATE: 08/31/2026

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DeepCertainty: Deep Learning for Contextual Diagnostic Uncertainty Measurement in Radiology Reports - PE becomes the third leading cause of cardiovascular-related death, and more than 500,000 cases of PE occur in the United States (US) every year, resulting in approximately 200,000 deaths and hospitalization of over 250,000 patients. Rapid and accurate diagnosis of PE are of paramount importance to ensure the highest quality of care. Every year 1-2% of the 120 million emergency department (ED) patients in the US undergo computed tomographic pulmonary angiography (CTPA) for PE. The referring physicians rely heavily on CTPA reports diagnosing or excluding PEs. Clarity of the radiology report is one of the most critical qualities, and the American College of Radiology has emphasized a need for precision communication in radiological reports. Yet communicating uncertainty effectively in radiology reports is challenging. Referring physicians may interpret radiologists’ textual expressions that convey diagnostic confidence differently than intended. The gap between radiologists’ intended message and the referring physicians’ interpretation can not be completely resolved through structured reporting or standardized lexicon. Unnecessary hedging language in CTPA reports may further worsen the reporting ambiguity and may lead to inappropriate treatment of patients. Therefore, we aim to develop a deep learning-based approach for context-aware (un)certainty assessment (DeepCertainty), which is end-to-end trainable, calibratable, generalizable, scalable, and explainable. It would allow for fine- grained uncertainty measurement and standardization, facilitate consistent and accurate diagnostic certainty communication in CTPA reports and thus improve PE care. This study will build the foundation for future implementation and integration of DeepCertainty into clinical workflows to prompt real-time low-certainty alerts for improving PE diagnostic reporting quality and clarity, which will inform better treatment decisions for ED patients with suspected PE.


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 = $0 )
2026	2024	UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL	55 LAKE AVE N	WORCESTER	MA	01655	WORCESTER	USA	Medical Library Assistance	000	2	12/24/2025	NON-COMPETING CONTINUATION	$0
														Subtotal = $0

Issue Date FY: 2024 ( Subtotal = $226,125 )
2024	2024	UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL	55 LAKE AVE N	WORCESTER	MA	01655	WORCESTER	USA	Medical Library Assistance	000	2	9/2/2024	NON-COMPETING CONTINUATION	$226,125
														Subtotal = $226,125

Issue Date FY: 2023 ( Subtotal = $188,438 )
2023	2023	UNIVERSITY OF MASSACHUSETTS MEDICAL SCHOOL	55 LAKE AVE N	WORCESTER	MA	01655	WORCESTER	USA	Medical Library Assistance	000	1	8/17/2023	NEW	$188,438
														Subtotal = $188,438

Grand Total All Awards = $414,563

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DeepCertainty: Deep Learning for Contextual Diagnostic Uncertainty Measurement in Radiology Reports

Award Number: R21LM014032

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

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

PERIOD OF PERFORMANCE START DATE: 09/01/2023

PERIOD OF PERFORMANCE END DATE: 08/31/2026

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