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

项目摘要 肩袖撕裂对于大的患者群体来说是高度成问题的，因此仍然是非常具有挑战性的。 临床问题大约20%到50%的60岁以上的人有已知的肩袖撕裂， 只会随着年龄的增长而增加。虽然手术重建肩袖旨在改善肩关节功能， 稳定性，手术成功的程度差异很大。这些大相径庭的结果是 因为，在术前，根据目前的评估方法，很难预测哪些患者将受益于 手术和不手术的人该项目的重点是开发独特的技术，以取代目前的 方法，以产生一个快速，准确的评估肩袖能够大规模的商业部署。 从临床的角度来看，有重要的科学证据表明，过度的脂肪浸润和萎缩， 肩袖肌肉导致不良结果，因为脂肪组织的存在限制了 肌腱重建后肌肉恢复和再生。虽然目前的临床实践利用 磁共振成像（MRI）使用定性评分系统评价肩袖中的脂肪浸润， 以前的研究已经确定，定性评分与定量评分的相关性相对较低， 脂肪浸润和萎缩的测量。简化定量测量将大大改善 临床治疗决策。然而，根据现有方法进行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。一种快速准确的肩袖肌肉分割方法 并且量化脂肪浸润对于改善结果和减少不必要的手术是必不可少的。 这一提议旨在利用Springbok此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 定量和脂肪浸润。该算法将被开发，以便它最终可以无缝集成 进入当前的临床工作流程，从而不需要任何额外的临床医生时间，事实上很可能 大大缩短了这个时间。在目标1中，我们将开发和验证基于深度学习的自动算法 用于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。完成 第一阶段项目的第一阶段将产生一个原型产品，该产品将在第二阶段的多个阶段进行beta测试。 骨科中心，使510（k）申请获得市场许可。该项目将大大改善 提高肩关节病理学评估的准确性，从而推进肩关节疾病的诊断和治疗 病理，改善昂贵的矫形手术的结果，甚至可能消除 所有这些都将改善病人护理并降低相关成本。

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.