Enhanced Sarcopenia Detection in Nursing Home Residents Using Ultrasound Radiomics and Machine Learning.
Authors
Affiliations (5)
Affiliations (5)
- Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
- Healthcare Department, West China Hospital, Sichuan University, Chengdu, China.
- West China Fourth Hospital, Sichuan University, Chengdu, China.
- Department of Medical Ultrasound, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China; Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China. Electronic address: [email protected].
- Center of Gerontology and Geriatrics, West China Hospital, Sichuan University, Chengdu, China; National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China; Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, China. Electronic address: [email protected].
Abstract
Ultrasound only has low-to-moderate accuracy for sarcopenia. We aimed to investigate whether ultrasound radiomics combined with machine learning enhances sarcopenia diagnostic accuracy compared with conventional ultrasound parameters among older adults in long-term care. Diagnostic accuracy study. A total of 628 residents from 15 nursing homes in China. Sarcopenia diagnosis followed AWGS 2019 criteria. Ultrasound of thigh muscles (rectus femoris [ReF], vastus intermedius [VI], and quadriceps femoris [QF]) was performed. Conventional parameters (muscle thickness [MT], echo intensity [EI]) and radiomic features were extracted. Participants were split into training (70%)/validation (30%) sets. Conventional (muscle thickness + EI), radiomics, and integrated (MT, echo intensity, radiomics, basic clinical data including age, sex, and body mass index) models were built using 5 machine learning algorithms (including logistic regression [LR]). Performance was assessed in the validation set using the area under the receiver operating characteristic curve (AUC), calibration, and decision curve analysis (DCA). Sarcopenia prevalence was 61.9%. The LR algorithm consistently exhibited superior performance. The diagnostic accuracy of the ultrasound radiomic models was superior to that of the models based on conventional ultrasound parameters, regardless of muscle group. The integrated models further improved the accuracy, achieving AUCs (95% CIs) of 0.85 (0.79-0.91) for ReF, 0.81 (0.75-0.87) for VI, and 0.83 (0.77-0.90) for QF. In the validation set, the AUCs (95% CIs) for the conventional ultrasound models were 0.70 (0.63-0.78) for ReF, 0.73 (0.65-0.80) for VI, and 0.75 (0.68-0.82) for QF. The corresponding AUCs (95% CIs) for the radiomics models were 0.76 (0.69-0.83) for ReF, 0.76 (0.69-0.83) for VI, and 0.78 (0.71-0.85) for QF. The integrated models demonstrated good calibration and net benefit in DCA. Ultrasound radiomics, especially when integrated with conventional parameters and clinical data using LR, significantly improves sarcopenia diagnostic accuracy in nursing home residents. This accessible, noninvasive approach holds promise for enhancing sarcopenia screening and early detection in long-term care settings.