Artificial intelligence-based analysis of lower limb muscle mass and fatty degeneration in patients with knee osteoarthritis and its correlation with Knee Society Score.

IF 2.3 3区 医学 Q3 ENGINEERING, BIOMEDICAL International Journal of Computer Assisted Radiology and Surgery Pub Date : 2024-11-03 DOI:10.1007/s11548-024-03284-y
Kohei Kono, Tomofumi Kinoshita, Mazen Soufi, Yoshito Otake, Yuto Masaki, Keisuke Uemura, Tatsuhiko Kutsuna, Kazunori Hino, Takuma Miyamoto, Yasuhito Tanaka, Yoshinobu Sato, Masaki Takao
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Abstract

Purpose: Lower-limb muscle mass reduction and fatty degeneration develop in patients with knee osteoarthritis (KOA) and could affect their symptoms, satisfaction, expectation and functional activities. The Knee Society Scoring System (KSS) includes patient reported outcome measures, which is widely used to evaluate the status of knee function of KOA. This study aimed to clarify how muscle mass and fatty degeneration of the lower limb correlate with the KSS in patients with KOA.

Methods: This study included 43 patients with end-stage KOA, including nine males and 34 females. Computed tomography (CT) images of the lower limb obtained for the planning of total knee arthroplasty were utilized. Ten muscle groups were segmented using our artificial-intelligence-based methods. Muscle volume was standardized by dividing by their height squared. The mean CT value for each muscle group was calculated as an index of fatty degeneration. Bivariate analysis between muscle volume or CT values and KSS was performed using Spearman's rank correlation test. Multiple regression analysis was performed, and statistical significance was set at p  < 0.05.

Results: Bivariate analysis showed that the functional activity score was significantly correlated with the mean CT value of all muscle groups except the adductors and iliopsoas. Multiple regression analysis revealed that the functional activities score was significantly associated with the mean CT values of the gluteus medius and minimus muscles and the anterior and lateral compartments of the lower leg (β = 0.42, p = 0.01; β = 0.33, p = 0.038; and β = 0.37, p = 0.014, respectively).

Conclusion: Fatty degeneration, rather than muscle mass, in the lower-limb muscles was significantly associated with functional activities score of the KSS in patients with end-stage KOA. Notably, the gluteus medius and minimus and the anterior and lateral compartments of the lower leg are important muscles associated with functional activities.

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基于人工智能的膝关节骨关节炎患者下肢肌肉质量和脂肪变性分析及其与膝关节社会评分的相关性。
目的:膝关节骨性关节炎(KOA)患者的下肢肌肉质量下降和脂肪变性可能会影响他们的症状、满意度、期望值和功能活动。膝关节协会评分系统(KSS)包括患者报告的结果测量,被广泛用于评估膝关节骨性关节炎患者的膝关节功能状况。本研究旨在阐明下肢肌肉质量和脂肪变性与 KOA 患者 KSS 的相关性:本研究共纳入 43 例终末期 KOA 患者,其中男性 9 例,女性 34 例。研究使用了为规划全膝关节置换术而获得的下肢计算机断层扫描(CT)图像。我们使用基于人工智能的方法对十个肌肉群进行了分割。肌肉体积的标准化方法是除以高度的平方。每组肌肉的平均 CT 值被计算为脂肪变性指数。使用斯皮尔曼秩相关检验对肌肉体积或 CT 值与 KSS 进行二元分析。进行多元回归分析,统计显著性以 p 为标准:双变量分析表明,除内收肌和髂腰肌外,功能活动评分与所有肌群的平均 CT 值均有显著相关性。多元回归分析显示,功能活动评分与臀中肌和臀小肌以及小腿前外侧间隙的平均 CT 值显著相关(β = 0.42,p = 0.01;β = 0.33,p = 0.038;β = 0.37,p = 0.014):结论:KOA终末期患者下肢肌肉的脂肪变性(而非肌肉质量)与KSS功能活动评分显著相关。值得注意的是,臀中肌和臀小肌以及小腿前部和外侧是与功能活动相关的重要肌肉。
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来源期刊
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery ENGINEERING, BIOMEDICAL-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
5.90
自引率
6.70%
发文量
243
审稿时长
6-12 weeks
期刊介绍: The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.
期刊最新文献
Correction to: Micro-robotic percutaneous targeting of type II endoleaks in the angio-suite. Automated assessment of non-technical skills by heart-rate data. Artificial intelligence-based analysis of lower limb muscle mass and fatty degeneration in patients with knee osteoarthritis and its correlation with Knee Society Score. High-quality semi-supervised anomaly detection with generative adversarial networks. Deep learning-based osteochondritis dissecans detection in ultrasound images with humeral capitellum localization.
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