Development of a commercially viable machine learning product to automatically detect rotator cuff muscle pathology
Ended Project Grant Awarded Last action
- Award ID
- R41AR078720
- Recipient
-
Springbok Inc. UEI HL3NPM25XYG7
- Awarding agency
-
National Insititue of Arthritis and Musculoskeletal and Skin Diseases
National Institutes of Health, Department of Health and Human Services
- Obligated
- $727,430
Award facts
- Assistance listing
- 93.846 Arthritis, Musculoskeletal and Skin Diseases Research
- Completion date
- Funding
-
- $519,226.00 Federal
- $208,204.00 Non-federal
- Place of performance
- Virginia, USA
Award details come from USAspending.gov.
Description
- Abstract
- 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.
Transactions
| Action | Federal | Non-federal | Date |
|---|---|---|---|
| Amendment | $0 | $0 | |
| Amendment | $51,409 | $0 | |
| Amendment | $51,409 | $0 | |
| Amendment | $208,204 | $0 | |
| Original award | $208,204 | $208,204 |
Quick answers
Who received award R41AR078720?
R41AR078720 was awarded to Springbok Inc. by National Insititue of Arthritis and Musculoskeletal and Skin Diseases (National Institutes of Health) on September 27, 2021.
How much is award R41AR078720 worth?
$727,430 has been obligated on R41AR078720: $519,226 in federal funding and $208,204 in non-federal funding.
On GovTribe
See this award's full record
- Every transaction and subaward
- The recipient's other awards and teaming partners
- Similar grant awards
- Ask GovTribe AI about this award