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

• 批准号: 10268004
• 负责人: Silvia Salinas Blemker
• 金额: $ 20.82万
• 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 2022-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10268004
• 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

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.

项目概要 肩袖撕裂对于大量患者来说是一个很大的问题，因此仍然是一个非常具有挑战性的问题 大约 20% 到 50% 的 60 岁老人患有已知的肩袖撕裂及其患病率。 肩袖重建手术旨在改善肩部功能并随着年龄的增长而增加。 稳定性，手术成功的程度差异很大。 因为在术前，根据目前的评估方法很难预测哪些患者将从中受益 该项目的重点是开发替代现有技术的独特技术。 对肩袖进行快速、准确评估的方法，能够大规模商业部署。 从临床角度来看，有重要的科学证据表明过度的脂肪浸润和萎缩 肩袖肌肉会导致不良结果，因为脂肪组织的存在限制了旋转的能力 目前的临床实践利用的是肌腱重建后的肌肉恢复和再生。 磁共振成像 (MRI) 使用定性评分系统评估肩袖中的脂肪浸润， 先前的研究已经证实，定性评分与定量评分的相关性相对较低 结合定量测量将显着改善脂肪浸润和萎缩的情况。 然而，现有方法下的这种评估需要大量的时间。 手动输入，因此在临床上不可行。 量化脂肪渗透对于改善结果和减少不必要的手术至关重要。 该提案旨在利用Springbok之前在机器学习图像方面的技术创新 分割以开发能够快速、准确评估肩袖肌肉萎缩的算法 将开发量化和脂肪渗透的算法，以便最终能够无缝集成。 融入当前的临床工作流程，不需要任何额外的临床医生时间，事实上因此可能 在目标 1 中，我们将开发并验证基于深度学习的自动算法。 为了量化肩袖肌肉体积和脂肪浸润，在目标 2 中，我们将开发一个软件。 原型将算法纳入临床工作流程以支持决策过程。 该第一阶段项目的成果将产生一个原型产品，该产品已准备好在第二阶段进行 beta 测试。 骨科中心，使 510(k) 申请能够获得市场许可，该项目将显着改善。 提高肩部病理评估的准确性，从而推进肩部的诊断和治疗 病理学，改善昂贵的骨科手术的结果，甚至有可能消除 不必要的程序，所有这些都将改善患者护理并降低相关成本。