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: 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
• 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; 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.

项目摘要 肩袖撕裂对于大型患者人群高度问题，因此仍然是一个非常挑战 临床问题。大约20％至50％的60岁年龄有已知的肩袖撕裂和患病率 只会随着年龄的增长而增加。肩袖手术重建旨在改善肩部功能和 稳定性，成功的手术结局的程度差异很大。这些截然不同的结果是 因为在术前，在当前评估的方法下很难预测哪些患者将从 手术与那些不会的人。该项目的重点是开发独特的技术来取代当前 对能够进行大规模商业部署的肩袖的快速，准确评估的方法。 从临床角度来看，有大量的科学证据表明 肩袖肌肉导致结果不佳，因为脂肪组织的存在限制了 肌肉恢复和再生肌腱重建。当前的临床实践利用 磁共振成像（MRI）使用定性评分系统评估肩袖的脂肪浸润， 先前的研究已经确定，定性评分与定量相对较低 脂肪浸润和萎缩的度量。合并定量测量将大大改善 临床治疗决策。但是，在现有方法下的这种评估将需要大量 手动输入，因此在临床上不可行。一种快速准确的方法，用于分割肩袖肌肉 量化脂肪浸润对于改善预后和减少不必要的手术至关重要。 该建议旨在利用Springbok以前在机器学习图像中的技术创新 分割以开发一种能够快速，准确评估肩袖肌肉萎缩的算法 数量和脂肪浸润。将开发该算法，以最终可以无缝集成 进入当前的临床工作流程，因此不需要任何额外的临床时间，实际上很可能 实质上减少了这段时间。在AIM 1中，我们将开发和验证基于深度学习的自动算法 用于定量肩袖肌肉体积和脂肪浸润。在AIM 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。完成 该阶段1项目将导致原型产品，该产品准备在II期期间进行β测试 骨科中心，实现了510（k）申请市场清理申请。这个项目将大大改善 肩部病理评估的准确性，从而提高了肩膀的诊断和治疗 病理学，改善昂贵的骨科程序的结果，甚至可能消除 不必要的程序，所有这些程序都将改善患者护理并降低相关成本。