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

> **NIH NIH R21** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2024 · $226,125

## Abstract

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.

## Key facts

- **NIH application ID:** 10909001
- **Project number:** 5R21LM014032-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Feifan Liu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $226,125
- **Award type:** 5
- **Project period:** 2023-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10909001

## Citation

> US National Institutes of Health, RePORTER application 10909001, DeepCertainty: Deep Learning for Contextual Diagnostic Uncertainty Measurement in Radiology Reports (5R21LM014032-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10909001. Licensed CC0.

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