# Clinical evaluation of a commercially viable machine learning algorithm to automatically detect shoulder muscle pathology

> **NIH NIH R44** · SPRINGBOK, INC. · 2024 · $796,748

## Abstract

PROJECT SUMMARY
Rotator cuff repairs are amongst the most performed orthopedic surgeries (>400,000 surgeries in the US per
year) but remain a very challenging clinical problem. While surgical repair of the rotator cuff seeks to improve
shoulder function and stability, the surgical outcomes vary significantly because, pre-operatively, it is difficult
under current evaluative methods to predict which patients will benefit from surgery versus those who will not.
The focus of this project is to develop unique technology that replaces current methods to produce a rapid,
accurate assessment of rotator cuffs capable of large-scale commercial deployment.
 There is significant scientific evidence that excessive fat infiltration and atrophy of the rotator cuff muscles
lead to poor outcomes because the presence of fatty tissue limits the ability for the muscle to recover and
regenerate following tendon reconstruction. While current clinical practice utilizes magnetic resonance imaging
(MRI) to evaluate fat infiltration in the rotator cuff using qualitative scoring systems, qualitative scoring has little-
to-no correlation with quantitative measures of fat infiltration and atrophy. Incorporating quantitative
measurements would dramatically improve clinical treatment decision-making; however, existing methods would
require substantial manual input and thus is not clinically viable. A fast and accurate method for segmenting the
rotator cuff muscles and fat infiltration is essential for improving outcomes and reducing unnecessary surgeries.
 During the Phase I period of this project, we successfully developed and validated a deep-learning-based
automatic algorithm for quantification of rotator cuff muscle and fatty infiltration from clinical scans. Through the
creation of an extensive digital database of both healthy and pathological rotator cuff clinical scans, we developed
a novel method to account for variability in scan coverage, which led to the establishment of key rotator cuff
muscle metrics that can be derived quickly and precisely from the MR images. We now have a prototype product
that is ready for beta-testing. In the Phase II period, we propose to perform a prospective clinical study to
determine which MRI-derived muscle metrics that best predict the outcomes of rotator cuff repair surgeries. In
Aim 1, we will partner with multiple orthopedic centers to perform pre-operative analysis of rotator cuffs that are
being considered for rotator cuff repair surgery, and then relate the pre-operative metrics with post-operative
outcomes. In Aim 2, we will develop and refine the user interface and associated metrics that will be ultimately
deployed for clinical use. Completion of this project will enable a 510(k) application for market clearance. This
project will significantly improve the accuracy of shoulder pathology assessments, thus advancing the diagnosis
and treatment of shoulder pathologies, improving the outcomes of costly orthopedic procedures, and ...

## Key facts

- **NIH application ID:** 10918167
- **Project number:** 5R44AR078720-04
- **Recipient organization:** SPRINGBOK, INC.
- **Principal Investigator:** Silvia Salinas Blemker
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $796,748
- **Award type:** 5
- **Project period:** 2021-09-27 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10918167

## Citation

> US National Institutes of Health, RePORTER application 10918167, Clinical evaluation of a commercially viable machine learning algorithm to automatically detect shoulder muscle pathology (5R44AR078720-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10918167. Licensed CC0.

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