# Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fractures

> **NIH NIH K08** · UNIVERSITY OF MARYLAND BALTIMORE · 2021 · $186,183

## Abstract

PROJECT SUMMARY/ABSTRACT: Arterial hemorrhage after pelvic fractures is a leading reversible cause of
death after blunt trauma. Prediction of arterial bleeding risk is difficult, and currently determined using
subjective criteria, often based on qualitative results of admission computed tomography (CT). Segmented
hematoma and contrast extravasation (CE) volumes predict need for angioembolization, major transfusion, and
mortality but cannot be applied in real-time. The ill-defined multi-focal nature of pelvic hematomas and CE
prevents reliable estimation using diameter-based measurements. Dr. Dreizin is a trauma radiologist at the
University of Maryland School of Medicine. His early work has focused on improving the speed and reliability of
volumetric analysis of pelvic hematomas using semi-automated techniques, and derivation of a logistic
regression-based prediction tool for major arterial injury after pelvic fractures. Dr. Dreizin’s goal for this four-
year K08 mentored career development award proposal is to gain the skills needed to 1) implement deep
learning architectures for automated hematoma volume segmentation and 2) develop computational models
for outcome prediction after pelvic trauma. These tools could greatly improve the speed and accuracy of
clinical decision making in the setting of life-threatening traumatic pelvic bleeding. Fully convolutional neural
networks (FCNs) have emerged as the most robust and scalable method for automated medical image
segmentation. Intuitive software platforms for training FCN implementations and generating multivariable
machine learning models have been developed in the Python programming environment. The training
objectives and research activities of this proposal are necessary to provide Dr. Dreizin with new skills and
practical experience in Python programming, deep learning software, and computational modeling software. By
understanding the principles and computational infrastructure behind modern machine learning, Dr. Dreizin will
be able to train and validate state-of-the-art algorithms independently and effectively lead a team of
researchers in this area. To achieve his goals, Dr. Dreizin has assembled a multidisciplinary team of mentors,
advisors, and collaborators with world-leading expertise in computer vision in medical imaging, probability
theory, data science, and comparative effectiveness research. Dr. Dreizin will focus on two specific aims. In
Aim 1, he will train and validate deep learning architectures for segmentation of traumatic pelvic hematomas
and CE by computing the Dice metric, time effort, and correlation with clinical outcomes. In Aim 2, he will
generate and test quantitative models for predicting major arterial bleeding after pelvic trauma based on a rich
multi-label dataset of segmented features. The training and pilot data will be necessary for Dr. Dreizin’s long-
term goal of research independence and R01 support to develop automated segmentation algorithms for the
spect...

## Key facts

- **NIH application ID:** 10189581
- **Project number:** 5K08EB027141-03
- **Recipient organization:** UNIVERSITY OF MARYLAND BALTIMORE
- **Principal Investigator:** David Dreizin
- **Activity code:** K08 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $186,183
- **Award type:** 5
- **Project period:** 2019-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10189581

## Citation

> US National Institutes of Health, RePORTER application 10189581, Machine learning-based segmentation and risk modeling for real-time prediction of major arterial bleeding after pelvic fractures (5K08EB027141-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10189581. Licensed CC0.

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