# Development of a commercially viable machine learning product to automatically detect rotator cuff muscle pathology

> **NIH NIH R41** · SPRINGBOK, INC. · 2022 · $51,409

## Abstract

PROJECT SUMMARY
Rotator cuff tears are highly problematic for large patient populations, and therefore remain a very challenging
clinical problem. Roughly 20% to 50% of those 60 years of age have a known rotator cuff tear and the prevalence
only increases with age. While surgical reconstruction of the rotator cuff seeks to improve shoulder function and
stability, the degrees of successful surgical outcomes vary significantly. These widely differing outcomes are
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.
From a clinical perspective, 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,
previous studies have established that qualitative scoring has a relatively low correlation with quantitative
measures of fat infiltration and atrophy. Incorporating quantitative measurements would dramatically improve
clinical treatment decision-making. However, such evaluation under 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 quantifying fat infiltration is essential for improving outcomes and reducing unnecessary surgeries.
This proposal aims to leverage Springbok’s previous technological innovations in machine learning image
segmentation to develop an algorithm capable of fast, accurate assessment of rotator cuff muscle atrophy
quantification and fat infiltration. The algorithm will be developed so that it can ultimately be seamlessly integrated
into the current clinical workflow, thereby not requiring any additional clinician time, and in fact is likely to
materially reduce that time. In Aim 1, we will develop and validate a deep-learning-based automatic algorithm
for quantification of rotator cuff muscle volumes and fatty infiltration. In Aim 2, we will develop a software
prototype to incorporate the algorithm into clinical workflow to support the decision-making process. Completion
of this Phase 1 project will lead to a prototype product that is ready for beta-testing during Phase II at multiple
Orthopaedic centers, enabling 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 Orthopaedic procedures, and poten...

## Key facts

- **NIH application ID:** 10495191
- **Project number:** 5R41AR078720-02
- **Recipient organization:** SPRINGBOK, INC.
- **Principal Investigator:** Silvia Salinas Blemker
- **Activity code:** R41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $51,409
- **Award type:** 5
- **Project period:** 2021-09-27 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10495191

## Citation

> US National Institutes of Health, RePORTER application 10495191, Development of a commercially viable machine learning product to automatically detect rotator cuff muscle pathology (5R41AR078720-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10495191. Licensed CC0.

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