# The Application of Deep Learning Methods for Proximal Humerus Fracture Feature Identification

> **NIH NIH P20** · CLEMSON UNIVERSITY · 2024 · $205,807

## Abstract

SUMMARY
Proximal humerus fractures (PHFs) are the third most common fracture in the elderly, with an estimated 200,000
occurring each year in the United States. PHFs can lead to pain, poor shoulder function, plus short and long-
term disability for patients. Substantial controversy persists regarding initial treatment for elderly adults with this
injury. PHFs can be managed conservatively or surgically and great controversy exists over which patients
should be treated surgically. A unique challenge with PHFs is that they have variable presentation and range in
complexity. Unlike the management of other major joint fractures, the initial treatment choice for PHF is highly
dependent on the fracture characteristics. Treatment effectiveness evidence is needed to guide clinical care for
individual patients with PHF. The Neer Classification, first developed in 1970, is the most widely used framework
to describe and classify PHFs. Although the Neer Classification is the most widely used in practice, it is outdated,
incomplete, often incorrectly applied, and suffers from poor interobserver reliability. The absence of a universally
accepted, standardized fracture classification system is a critical barrier in the development of treatment
effectiveness evidence for PHF. The application of deep learning (DL) computational models can automate and
standardize the fracture classification process and identify all relevant fracture characteristics. DL image analysis
models have been shown to be highly accurate at identifying features of interest on diagnostic images. An
automated, standardized PHF classification system will enhance our ability to universally standardize fracture
classification across all orthopaedic clinical care settings, improve the precision and efficiency in fracture care
and generate treatment effectiveness evidence to guide clinical practice. The overall objective for this application,
is to develop and validate a DL computational model capable of identifying fracture features using X-ray images.
Our central hypothesis is that we can develop a DL model that will be as accurate as expert shoulder specialists
in identifying important fracture features on X-ray images. In Aim 1 we will modify the Neer Classification
framework for fracture feature identification. Aim 2 will be the development of a gold standard dataset for deep
learning DL fracture feature identification, and finally Aim 3 will be the training and testing of a DL model to
identify fracture features on X-rays.

## Key facts

- **NIH application ID:** 10912651
- **Project number:** 5P20GM121342-07
- **Recipient organization:** CLEMSON UNIVERSITY
- **Principal Investigator:** Sarah Bauer Floyd
- **Activity code:** P20 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $205,807
- **Award type:** 5
- **Project period:** 2018-09-15 → 2028-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10912651

## Citation

> US National Institutes of Health, RePORTER application 10912651, The Application of Deep Learning Methods for Proximal Humerus Fracture Feature Identification (5P20GM121342-07). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10912651. Licensed CC0.

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