Automated Arthroscopic Partial Meniscectomy Patient Outcome Prediction using Deep Learning

项目总结/摘要 Mingrui Yang博士是一位具有定量背景的生物医学成像研究科学家， 目标是进行临床导向的多学科研究，以提高认识，诊断， 通过非侵入性医学成像和定量分析治疗肌肉骨骼和其他疾病 比如机器学习。他提出的这项研究题为自动关节镜部分 使用深度学习的半月板切除术旨在开发膝关节软骨的自动化系统， 基于尖端深度学习模型的半月板分割和异常检测，以及 当物理治疗失败时，关节镜下部分椎间盘切除术（APM）后1年患者结局预测， 利用由深度学习模型识别的MR成像生物标志物。 候选人：杨博士是一个初级教员在高级肌肉骨骼成像（PAMI）的程序， 克利夫兰诊所的生物医学工程系他的训练一直集中在定量 包括数学、统计学、计算机科学和磁共振技术发展在内的科学 (MR)显像他的目标是过渡到骨关节炎（OA）和医学成像的临床研究。的 建议的职业发展计划包括四个培训目标，迫使他走向独立 研究者：1）了解膝关节OA和APM在膝关节OA中的作用; 2）了解 骨科手术、患者队列和临床患者结局; 3）了解MR成像 膝关节OA和APM结局的生物标志物; 4）获得临床研究的专业知识。 环境：杨博士和他的主要导师李小娟博士组建了一个杰出的团队， 博士杨的训练和研究计划。作为PAMI的成员，他将与研究人员和临床医生密切合作， 在生物医学工程，骨科和诊断成像部门为他的职业发展。 研究：拟议的研究将按照三个目标分三个阶段进行：目标1建立 一个深度学习系统，用于自动检测异构临床上的高级关节软骨病变 膝关节MR图像; Aim 2开发了一种自动深度学习系统，用于检测膝关节根部的存在 异质临床膝关节MR图像上的撕裂;目标3利用深度学习的成像发现 系统和患者人口统计学数据，以预测APM后的临床结局。 总结：该提案将为软骨和半月板提供一种新颖，自动化和一致的工具 对从临床常规实践中收集的异质膝关节MR图像进行分割和病变检测。一 使用这些成像结果与患者人口统计学数据的预测模型可以帮助预测 接受APM手术的患者。这一提议也将推动杨博士的职业发展， OA和生物医学成像研究的独立研究者。

PROJECT SUMMARY / ABSTRACT Mingrui Yang, PhD, is a biomedical imaging research scientist with quantitative background whose overarching goal is to conduct clinically oriented multidisciplinary research to improve the understanding, diagnosis, and treatment of musculoskeletal and other disorders through non-invasive medical imaging and quantitative methods such as machine learning. The study he proposes entitled Automated Arthroscopic Partial Meniscectomy using Deep Learning aims to develop automated systems for knee articular cartilage and meniscus segmentation and abnormality detection based on cutting-edge deep-learning models, as well as one- year patient outcome prediction after arthroscopic partial meniscectomy (APM) when physical therapy fails, utilizing MR imaging biomarkers identified by the deep-learning models. Candidate: Dr. Yang is a junior faculty member in the Program of Advanced Musculoskeletal Imaging (PAMI) of the Department of Biomedical Engineering at Cleveland Clinic. His training has been focused on quantitative sciences including mathematics, statistics, computer science, and technical development of magnetic resonance (MR) imaging. He aims to transition to clinically research in osteoarthritis (OA) and medical imaging. The proposed career development plan consists of four training goals to compel him toward an independent investigator: 1) Gain knowledge in knee OA and the role of APM in knee OA; 2) Gain an understanding of orthopaedic surgery, patient cohort, and clinical patient outcomes; 3) Gain an understanding of MR imaging biomarkers for knee OA and APM outcomes; 4) Gain expertise in clinical research. Environment: Dr. Yang and his primary mentor, Xiaojuan Li, PhD, have assembled a prominent team to guide Dr. Yang’s training and research plans. As a member of PAMI, he will work closely with researchers and clinicians in departments of Biomedical Engineering, Orthopaedics, and Diagnosis Imaging for his career development. Research: The proposed study will be carried out in three phases corresponding to the three aims: Aim 1 builds a deep learning system for automatic high-grade articular cartilage lesion detection on heterogeneous clinical knee MR images; Aim 2 develops an automated deep learning system to detect the presence of meniscal root tears on heterogeneous clinical knee MR images; Aim 3 utilizes the imaging finding(s) from the deep learning system(s) and patient demographics to predict the clinical outcomes after APM. Summary: The proposal will provide a novel, automated, and consistent tool for cartilage and meniscus segmentation and lesion detection on heterogeneous knee MR images collected from clinical routine practice. A prediction model using these imaging findings with patient demographics can help predict clinical outcomes for patients undergoing APM surgery. This proposal will also advance Dr. Yang’s career development toward an independent investigator in OA and biomedical imaging research.