Automated Arthroscopic Partial Meniscectomy Patient Outcome Prediction using Deep Learning

项目摘要/摘要 杨明瑞博士，生物医学成像研究科学家，具有量化背景，其主要观点是 目标是进行面向临床的多学科研究，以提高对疾病的理解、诊断和治疗 非侵入性医学影像和定量治疗肌肉骨骼等疾病 机器学习等方法。他提出的题为自动关节镜部分切除的研究 使用深度学习的半月板切除术旨在开发膝关节软骨和关节软骨的自动化系统 基于前沿深度学习模型的半月板分割和异常检测 关节镜下半月板部分切除术(APM)后物理治疗失败的一年患者预后预测， 利用深度学习模型确定的磁共振成像生物标记物。 候选人：杨博士是麻省理工学院高级肌肉骨骼成像(PAMI)项目的初级教员 克利夫兰诊所生物医学工程系。他的训练一直专注于量化 包括数学、统计学、计算机科学和磁共振技术发展的科学 (MR)成像。他的目标是过渡到骨关节炎(OA)和医学成像的临床研究。这个 提出的职业发展计划包括四个培训目标，以迫使他走向独立 调查人员：1)了解膝骨性关节炎的相关知识和APM在膝骨性关节炎中的作用；2)了解膝骨性关节炎 骨科手术、患者队列和临床患者结果；3)了解磁共振成像 膝骨性关节炎和APM预后的生物标志物；4)在临床研究中获得专业知识。 环境：杨博士和他的主要导师李晓娟博士已经组建了一个杰出的团队来指导 杨博士的培训和研究计划。作为帕米的一员，他将与研究人员和临床医生密切合作 在生物医学工程、整形外科和诊断成像系任职，为他的职业发展做出贡献。 研究：拟议的研究将分三个阶段进行，与三个目标相对应：目标1 异质临床高级关节软骨病变自动检测的深度学习系统 膝关节磁共振成像；Aim 2开发了一个自动深度学习系统来检测半月板根部的存在 异质临床膝关节MRI图像上的泪水；Aim 3利用深度学习的影像发现(S) 系统(S)和患者人口统计学预测急性早幼粒细胞白血病的临床结果。 摘要：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 临床常规采集的异质膝关节磁共振图像的分割和病变检测。一个 使用这些成像结果与患者人口统计学相结合的预测模型可以帮助预测 接受APM手术的患者。这一建议也将推动杨博士的职业发展朝着 独立从事办公自动化和生物医学影像研究。

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.