# Automatic Rib Fracture Detection in Pediatric Radiography to Identify Non-Accidental Trauma

> **NIH NIH R21** · MICHIGAN STATE UNIVERSITY · 2020 · $178,149

## Abstract

ABSTRACT
Automatic Rib Fracture Detection in Pediatric Radiography to Identify Non-Accidental Trauma
PI: Adam M. Alessio
Non-accidental trauma caused by physical abuse is a leading cause of death in children in the United States.
Because rib fractures are highly predictive of child abuse and chest radiographs are commonly performed for
multiple indications, pediatric chest radiographs can have a critical role in the identification of abuse. Detection
of rib fractures on pediatric radiographs is challenging and a high percentage of fractures are missed,
particularly in imaging centers with limited pediatric radiology experience. Currently, there are no viable
computer assisted strategies for rib fracture detection on chest radiographs. The purpose of this proposal is to
develop machine learning methodology to detect rib fractures on pediatric radiographs using images from a
network of hospitals. These methods will rely on a two-stage approach including a thoracic cavity segmentation
stage followed by a fracture detection stage. We will explore two fracture detection strategies using novel
supervised learning approaches: a heterogeneous U-net and a multi-modal regional-convolutional neural
network. These methods will be trained and tested with a large set of fracture-absent radiographs (N=1000)
from Seattle Children's Hospital and a diverse set of labelled fracture-present radiographs (N=500) from
collaborating sites. These methods will be developed with an intentionally diverse set of radiographs,
representative of the variety of fracture presentations and image quality in clinical practice, in order to position
this rib fracture detection method for rapid translation to clinical practice. The ultimate goal of this proposal is
to provide a computer assisted rib fracture assessment tool that would be a rapid and widely-available add-on
to all pediatric chest radiograph exams, improving detection of rib fractures and potentially leading to improved
identification of child abuse.

## Key facts

- **NIH application ID:** 9976563
- **Project number:** 5R21HD097609-02
- **Recipient organization:** MICHIGAN STATE UNIVERSITY
- **Principal Investigator:** Adam M Alessio
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $178,149
- **Award type:** 5
- **Project period:** 2019-07-12 → 2022-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9976563

## Citation

> US National Institutes of Health, RePORTER application 9976563, Automatic Rib Fracture Detection in Pediatric Radiography to Identify Non-Accidental Trauma (5R21HD097609-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/9976563. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*