Asghari Mehran, Ehsani Hossein, Toosizadeh Nima
Department of Rehabilitation and Movement Sciences, School of Health Professions, Rutgers Health, Rutgers University, Newark, NJ, USA.
Department of Neurology, Rutgers Health, Rutgers University, Newark, NJ, USA.
Sci Rep. 2025 Apr 22;15(1):13891. doi: 10.1038/s41598-024-73854-2.
The global increase in the older adult population highlights the need for effective frailty assessment, a condition linked to adverse health outcomes such as hospitalization and mortality. Existing frailty assessment tools, like the Fried phenotype and Rockwood score, have practical limitations, necessitating a more efficient approach. This study aims to enhance frailty prediction accuracy in older adults using a combined biomechanical and deep learning approach. We recruited 312 participants (126 non-frail, 145 pre-frail, 41 frail) and assessed frailty using the Fried index, upper-extremity function (UEF) test, and muscle force calculations. Machine learning (ML) models, including logistic regression and support vector machine (SVM), were employed alongside deep learning with long short-term memory (LSTM) networks. Results showed that incorporating muscle model parameters significantly improved frailty prediction. The LSTM model achieved the highest accuracy (74%), outperforming SVM (67%) and regression (66%), with precision and F1 scores of 81% and 75%, respectively. Notably, muscle co-contraction emerged as a critical predictor, with frail individuals exhibiting substantially higher levels. Our findings demonstrate that integrating UEF tasks with deep learning models provides superior frailty prediction, potentially offering a robust, efficient clinical tool. However, further validation with larger, more diverse populations is needed to confirm the generalizability of our results. This study underscores the potential of advanced computational techniques to improve the identification and monitoring of frailty in older adults.
全球老年人口的增加凸显了有效进行衰弱评估的必要性，衰弱是一种与住院和死亡等不良健康结果相关的状况。现有的衰弱评估工具，如弗里德表型和罗克伍德评分，存在实际局限性，因此需要一种更有效的方法。本研究旨在使用生物力学和深度学习相结合的方法提高老年人衰弱预测的准确性。我们招募了312名参与者（126名非衰弱者、145名衰弱前期者、41名衰弱者），并使用弗里德指数、上肢功能（UEF）测试和肌肉力量计算来评估衰弱情况。机器学习（ML）模型，包括逻辑回归和支持向量机（SVM），与带有长短期记忆（LSTM）网络的深度学习一起使用。结果表明，纳入肌肉模型参数显著提高了衰弱预测能力。LSTM模型达到了最高准确率（74%），优于SVM（67%）和回归模型（66%），精确率和F1分数分别为81%和75%。值得注意的是，肌肉共同收缩成为一个关键预测因素，衰弱个体的水平显著更高。我们的研究结果表明，将UEF任务与深度学习模型相结合可提供卓越的衰弱预测，可能会提供一种强大、高效的临床工具。然而，需要用更大、更多样化的人群进行进一步验证，以确认我们结果的普遍性。本研究强调了先进计算技术在改善老年人衰弱识别和监测方面的潜力。
