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

自动检测肩部肌肉病理的商业可行机器学习算法的临床评估

• 批准号: 10706901
• 负责人: Silvia Salinas Blemker
• 金额: $ 89.19万
• 依托单位: SPRINGBOK, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-27 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10706901
• 关键词: 3-Dimensional; Adipose tissue; Algorithms; Artificial Intelligence platform; Atrophic; Clinical; Clinical Research; Clinical Treatment; Data; Data Display; Databases; Decision Making; Development; Diagnosis; Digital Imaging and Communications in Medicine; Evaluation; Fatty acid glycerol esters; Goals; Healthcare; Human; Image; Infiltration; Institution; Intervention; Lead; Legal patent; MRI Scans; Magnetic Resonance Imaging; Manuals; Manuscripts; Marketing; Measurement; Measures; Methods; Muscle; Muscular Atrophy; Natural regeneration; Operative Surgical Procedures; Orthopedic Procedures; Orthopedic Surgery; Orthopedics; Outcome; Pathologic; Pathology; Patient Care; Patients; Phase; Postoperative Period; Process; Reconstructive Surgical Procedures; Rotator Cuff; Scanning; Secure; Shoulder; Slice; Surgeon; System; Technology; Tendon structure; Testing; Training; Universities; Unnecessary Procedures; Unnecessary Surgery; Virginia; Visual; Wisconsin; Work; artificial intelligence algorithm; automated algorithm; automated segmentation; clinical practice; cloud based; convolutional neural network; cost; cost outcomes; deep learning; demographics; digital; experience; functional restoration; healing; health assessment; improved; improved outcome; innovation; machine learning algorithm; novel; novel strategies; outcome prediction; patient population; postoperative recovery; procedure cost; prospective; prototype; quality assurance; reconstruction; recruit; repaired; research clinical testing; rotator cuff tear; success; supraspinatus muscle; surgery outcome; tool; web site

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

项目总结