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

开发商业上可行的机器学习产品来自动检测肩袖肌肉病理

基本信息

批准号：
10495191
负责人：
Silvia Salinas Blemker
金额：
$ 5.14万
依托单位：
SPRINGBOK, INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-27 至 2022-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10495191
关键词：
3-Dimensional Adipose tissue Adoption Age Age-Years Algorithms Area Atrophic Client Clinical Clinical Treatment Collection Computer Vision Systems Computer software Data Decision Making Development Diagnosis Digital Imaging and Communications in Medicine Evaluation Fatty acid glycerol esters Health Healthcare Human Image Individual Infiltration Intervention Lead Legal patent Lower Extremity MRI Scans Machine Learning Magnetic Resonance Imaging Manuals Measurement Measures Medical Methods Modeling Muscle Muscular Atrophy Natural regeneration Operative Surgical Procedures Orthopedic Procedures Orthopedics Outcome Output Pathology Patient Care Patient-Focused Outcomes Patients Performance Phase Prevalence Process Protocols documentation Recovery Rotator Cuff Scanning Shoulder Skeletal Muscle Slice System Technology Tendon structure Testing Time Training Unnecessary Procedures Unnecessary Surgery Visual Work automated algorithm automated segmentation base clinical practice convolutional neural network cost cost outcomes deep learning experience functional restoration healing imaging Segmentation improved improved outcome innovation learning strategy patient population prototype radiologist reconstruction repaired rotator cuff tear segmentation algorithm success supraspinatus muscle surgery outcome technological innovation tool

项目摘要

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 potentially even eliminating unnecessary procedures, all of which will improve patient care and lower the associated costs.

项目总结