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

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R41AR078720-01A1
Agency Tracking Number: R41AR078720
Amount: $259,613.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: NIAMS
Solicitation Number: PA20-265
Solicitation Year: 2020
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-09-27
Award End Date (Contract End Date): 2022-08-31
Small Business Information
Charlottesville, VA 22903-1561
United States
DUNS: 079158113
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 (434) 924-6291
Business Contact
Phone: (215) 680-9078
Research Institution
BOX 400195
United States

 Nonprofit College or University

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 potentially even eliminating
unnecessary procedures, all of which will improve patient care and lower the associated costs.PROJECT NARRATIVE
Rotator cuff tears are highly problematic for large patient populations such that clinicians need innovative tools
to better determine who could benefit from surgical repair. This project will result in revolutionary technology
capable of fast, precise measures of muscle atrophy and fat infiltration for accurate pre-intervention evaluation.

* Information listed above is at the time of submission. *

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