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Deep Learning for Characterizing Knee Joint Degeneration Predicting Progression of Osteoarthritis and Total Knee Replacement

深度学习表征膝关节退变，预测骨关节炎和全膝关节置换的进展

基本信息

批准号：
10193990
负责人：
Sharmila Majumdar
金额：
$ 40.37万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2022-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10193990
关键词：
3-Dimensional Algorithms Archives Artificial Intelligence Automation Big Data Bone Marrow Cartilage Classification Clinical Clinical Data Clinical/Radiologic Computer Models Consumption Data Data Reporting Data Set Degenerative polyarthritis Detection Development Disease Disease Progression Economics Edema Electronic Health Record Epidemiology Genomics Goals Healthcare Image Incidence Joints Knee Knee Osteoarthritis Knee joint Learning Lesion Ligaments Magnetic Resonance Imaging Medical Imaging Meniscus structure of joint Methodology Modeling Morphology Multimodal Imaging Musculoskeletal Neural Network Simulation Organ Outcome Pattern Phase Physical activity Physicians Picture Archiving and Communication System Play Prevalence Quantitative Evaluations Reporting Research Role Sample Size Scheme Semantics Structure Subchondral Cyst Supervision Synovitis System Techniques Testing Time Tissues Training Translating Visual automated segmentation bone clinical practice clinical translation convolutional neural network cost deep learning deep neural network disease classification drug discovery epidemiology study feature extraction high risk impression improved joint destruction knee replacement arthroplasty large datasets learning strategy musculoskeletal imaging novel parallel processing patient population radiologist research study speech recognition tool treatment response

项目摘要

ABSTRACT This proposal aims to develop deep learning methods to automate the extraction of morphological imaging features relevant to knee osteoarthritis (OA), and total knee replacement. While, quantitative evaluation Magnetic Resonance Imaging (MRI) plays a central role in OA research in the clinical setting MR reports often tend to be subjective, qualitative, and the grading schemes utilized in epidemiological research are not used because they are extraordinarily time consuming and do not lend themselves to the demands of todays changing healthcare scenario. The “Big Data” challenge and opportunity facing us makes it necessary to build enabling tools (i) to automate the extraction of morphological OA imaging features, with the aim of evaluating disease progression prediction capabilities on larger sample sizes that have never been explored before; (ii) to discover latent patterns by uncovering unexplored data-driven imaging features by the application of state of the art deep learning approaches (1); (iii) combine multi-modality imaging with clinical, functional, activity, and other data to define the trajectory of joint degeneration in OA. Leveraging the power of these state of the art techniques, and with the extraordinary availability of a large datasets of annotated images; in this project, we propose to develop an automatic post-processing pipeline able to segment musculoskeletal tissues and identify morphological OA features in Magnetic Resonance Images (MRI), as defined by commonly used MRI grading systems. Automation of morphological grading of the tissues in the joint would be a significant breakthrough in both OA research and clinical practice. It would enable the analysis of large sample sizes, assist the radiologist/clinician in the grading of images, take a relatively short amount of time, reduce cost, and could potentially, improve classification models. The availability of automatic pipelines for the identification of morphological abnormities in MRI would drastically change clinical practice, and include semi-quantitative grades, rather than subjective impressions in radiology clinical reports. In this study, we also aim to develop a complete supervised deep learning approach to obtain data-driven representations as non-linear and semantic aggregation among elementary features able to exploit the latent information hidden in the complexity of a 3D MR images, eliminating the need for nominal grades of selected features. This second aim, while being at high risk has also a potential exceptional high impact; as it departs from the classical hypothesis driven studies, and builds a novel translational platform to revolutionize morphological grading of MR images in research studies, but also is paradigm-shifting in that it may provide a more quantitative feature driven basis for routine radiological clinical reports. The clinical impact of this proposal lies in the third aim (R33 phase), in which we propose to translate the solutions developed in the R61 phase on images in the UCSF clinical archives (PACS), and plan to include also demographic and clinical data in the electronic health records, to build the models defining total knee replacements.

抽象的该提案旨在开发深度学习方法来自动提取形态学图像与膝骨关节炎 (OA) 和全膝关节置换术相关的特征。同时，定量评价磁共振成像 (MRI) 在临床环境中的 OA 研究中发挥着核心作用 MR 报告经常往往是主观的、定性的，并且不使用流行病学研究中使用的分级方案因为它们非常耗时并且无法满足当今的需求改变医疗保健场景。我们面临的“大数据”挑战和机遇使得我们有必要构建支持工具 (i) 自动提取形态学 OA 成像特征，目的是评估以前从未探索过的更大样本量的疾病进展预测能力； (二) 至通过应用状态来发现未探索的数据驱动成像特征，从而发现潜在模式艺术深度学习方法（1）； (iii) 将多模态成像与临床、功能、活动和其他数据来定义 OA 关节退化的轨迹。利用这些最先进技术的力量技术，以及大量带注释图像数据集的非凡可用性；在这个项目中，我们建议开发一种能够分割肌肉骨骼组织和根据常用 MRI 的定义，识别磁共振图像 (MRI) 中的形态学 OA 特征评分系统。关节组织形态分级的自动化将具有重要意义 OA研究和临床实践均取得突破。它将能够分析大样本量，协助放射科医生/临床医生对图像进行分级，花费相对较短的时间，降低成本，并且可能会改进分类模型。用于识别的自动管道的可用性 MRI 中的形态异常将极大地改变临床实践，包括半定量等级，而不是放射学临床报告中的主观印象。在这项研究中，我们还旨在开发一种完整的监督深度学习方法，以获得数据驱动的非线性和语义表示基本特征之间的聚合，能够利用隐藏在 3D 复杂性中的潜在信息 MR 图像，无需选定特征的标称等级。第二个目标虽然很高风险还具有潜在的异常高的影响；因为它偏离了经典假设驱动的研究，并且建立一个新颖的翻译平台，彻底改变研究中 MR 图像的形态分级，而且是范式转变，因为它可以为日常工作提供更加定量的特征驱动基础放射学临床报告。该提案的临床影响在于第三个目标（R33 阶段），其中我们建议将 R61 阶段开发的解决方案转化为 UCSF 临床档案中的图像 (PACS)，并计划将人口统计和临床数据纳入电子健康记录，以建立定义全膝关节置换的模型。

项目成果

期刊论文数量（13）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications.

DOI：
10.1007/s10278-022-00662-3
发表时间：
2023-04
期刊：
JOURNAL OF DIGITAL IMAGING
影响因子：
4.4
作者：
Shah, Rutwik;Nunes, Bruno Astuto Arouche;Gleason, Tyler;Fletcher, Will;Banaga, Justin;Sweetwood, Kevin;Ye, Allen;Patel, Rina;McGill, Kevin;Link, Thomas;Crane, Jason;Pedoia, Valentina;Majumdar, Sharmila
通讯作者：
Majumdar, Sharmila

3D convolutional neural networks for detection and severity staging of meniscus and PFJ cartilage morphological degenerative changes in osteoarthritis and anterior cruciate ligament subjects.