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.

项目摘要 肩袖修复是最常进行的骨科手术之一（美国每年有超过400，000例手术， 但仍然是一个非常具有挑战性的临床问题。虽然手术修复肩袖的目的是改善 肩关节功能和稳定性，手术结果差异很大，因为术前， 根据目前的评估方法，预测哪些患者将从手术中受益，哪些患者将不会受益。 该项目的重点是开发独特的技术，以取代目前的方法， 准确评估能够大规模商业部署的肩袖。 有重要的科学证据表明，肩袖肌肉的过度脂肪浸润和萎缩 导致不良结果，因为脂肪组织的存在限制了肌肉恢复的能力， 肌腱重建后再生。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估肩袖中的脂肪浸润，定性评分几乎没有- 与脂肪浸润和萎缩的定量测量值之间无相关性。定量分析 测量将大大改善临床治疗决策;然而，现有的方法将 需要大量的手动输入，因此在临床上不可行。一种快速准确的分割方法， 肩袖肌肉和脂肪浸润对于改善结果和减少不必要的手术至关重要。 在这个项目的第一阶段，我们成功地开发和验证了一个基于深度学习的 用于量化临床扫描中肩袖肌肉和脂肪浸润的自动算法。通过 建立一个广泛的健康和病理性肩袖临床扫描的数字数据库，我们开发了 一种新的方法来解释扫描范围的变化，这导致了关键肩袖的建立 可以从MR图像中快速精确地导出肌肉指标。我们现在有了一个原型产品 可以进行测试了在第二阶段，我们建议进行一项前瞻性临床研究， 确定哪些MRI衍生的肌肉指标最能预测肩袖修复手术的结果。在 目标1，我们将与多个骨科中心合作，对肩袖进行术前分析， 考虑进行肩袖修复手术，然后将术前指标与术后指标相关联 结果。在目标2中，我们将开发和完善用户界面和相关指标， 用于临床使用。该项目的完成将使510（k）申请获得市场许可。这 该项目将显著提高肩关节病理评估的准确性，从而推进诊断 和治疗肩部病变，改善昂贵的骨科手术的结果，并可能 甚至消除不必要的程序，所有这些都将改善病人护理并降低相关成本。