How feasible is the stratification of osteoarthritis phenotypes by means of artificial intelligence?
How feasible is the stratification of osteoarthritis phenotypes by means of artificial intelligence?
复制标题
通过人工智能，骨关节炎表型的分层有多可行？
DOI：
10.1080/23808993.2021.1848424
复制
发表时间：
2021
影响因子：
1.2
通讯作者：
Nelson AE
中科院分区：
文献类型：
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作者：
Nelson AE
骨关节炎（OA）是一种常见且严重的疾病，累及受累关节的所有组织（例如，软骨、骨、半月板、肌腱/韧带、滑膜），并可累及个体的一个或多个关节，最常见的是指关节、膝关节、髋关节和脊柱[1]。OA是全球残疾的主要且不断增长的因素，与合并症和死亡率增加相关[1]。OA的管理重点是适度有效的生活方式/行为干预，如增加体力活动和减肥，药物治疗旨在暂时缓解症状[2]。虽然已经进行了许多临床试验，但仍然没有有效的疾病修饰疗法，没有经过验证的方法来预防疾病进展，也没有治愈方法。这至少部分是由于在迄今为止的试验中缺乏对这种复杂疾病的异质性的认识和解释[3]。一般而言，大多数试验招募了所有膝关节OA患者，膝关节OA定义为至少一个膝关节出现症状（例如疼痛、酸痛和僵硬）和中度至重度影像学改变（例如骨赘或关节间隙狭窄）。这并不能解释疾病发展的不同机制，这些机制可能是由于机械功能障碍、先前损伤、代谢因素、炎症或这些因素的组合。它也没有解决表现的多样性、疾病负担（即，受累关节的数量/严重程度）、慢性或给定个体疾病过程的许多其他方面，这些方面随后可能影响其对拟定治疗的反应。这篇简短的编辑评论旨在总结机器学习和骨关节炎表型领域的最新工作。
Osteoarthritis (OA) is a common and serious disease that involves all of the tissues of an affected joint (eg, cartilage, bone, meniscus, tendon/ligament, synovium) and can affect one or multiple joints in an individual person, most often the finger joints, knees, hips, and spine [1]. OA is a major and growing contributor to disability worldwide and is associated with increased comorbidity and excess mortality [1]. Management of OA is focused on modestly effective lifestyle/behavioral interventions such as increased physical activity and weight loss, with pharmacologic therapies directed toward temporary symptomatic relief [2]. Although many clinical trials have been conducted, there are still no effective disease-modifying therapies, no proven way to prevent progression, and no cure. This is at least in part due to the lack of appreciation of, and accounting for, the heterogeneity of this complex disease in trials to date [3]. In general, most trials have enrolled all individuals with knee OA defined as the presence of symptoms (eg, pain, aching, and stiffness) and moderate to severe radiographic change (eg, osteophytes or joint space narrowing) in at least one knee. This does not account for the diverse mechanisms of disease development, which can be due to mechanical dysfunction, prior injury, metabolic factors, inflammation, or combinations of these. Nor does it address the diversity of presentations, burden of disease (ie, number/severity of involved joints), chronicity, or numerous other aspects of the disease process in a given individual that may subsequently affect their response to the proposed therapy. This brief editorial review seeks to summarize recent work in the area of machine learning and osteoarthritis phenotyping.