AIMC Topic: Sarcopenia

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Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.

BMC geriatrics
OBJECTIVES: This study aims to develop sarcopenia risk prediction models for Chinese older adults at different time intervals and to identify and compare modifiable factors contributing to sarcopenia development.

Machine learning based on nutritional assessment to predict adverse events in older inpatients with possible sarcopenia.

Aging clinical and experimental research
BACKGROUND: The accuracy of current tools for predicting adverse events in older inpatients with possible sarcopenia is still insufficient to develop individualized nutrition-related management strategies. The objectives were to develop a machine lea...

Personalised screening tool for early detection of sarcopenia in stroke patients: a machine learning-based comparative study.

Aging clinical and experimental research
BACKGROUND: Sarcopenia is a common complication in patients with stroke, adversely affecting recovery and increasing mortality risk. However, no standardised tool exists for its screening in this population. This study aims to identify factors influe...

Development of an artificial intelligence-based application for the diagnosis of sarcopenia: a retrospective cohort study using the health examination dataset.

BMC medical informatics and decision making
BACKGROUND: Medical imaging techniques for diagnosing sarcopenia have been extensively investigated. Studies have proposed using the T-score and patient information as key diagnostic factors. However, these techniques have either been time-consuming ...

Identifying semaphorin 3C as a biomarker for sarcopenia and coronary artery disease via bioinformatics and machine learning.

Archives of gerontology and geriatrics
OBJECTIVE: Sarcopenia not only affects patients' quality of life but also may exacerbate the pathological processes of coronary artery disease (CAD). This study aimed to identify potential biomarkers to improve the combined diagnosis and treatment of...

Artificial intelligence for body composition assessment focusing on sarcopenia.

Scientific reports
This study aimed to address the limitations of conventional methods for measuring skeletal muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for direct computed tomography (CT) analysis. The primary focus was ...

Exploring determinant factors influencing muscle quality and sarcopenia in Bilbao's older adult population through machine learning: A comprehensive analysis approach.

PloS one
BACKGROUND: Sarcopenia and reduced muscle quality index have garnered special attention due to their prevalence among older individuals and the adverse effects they generate. Early detection of these geriatric pathologies holds significant potential,...

Correlation between individual thigh muscle volume and grip strength in relation to sarcopenia with automated muscle segmentation.

PloS one
INTRODUCTION: Grip strength serves as a significant marker for diagnosing and assessing sarcopenia, particularly in elderly populations. The study aims to explore the relationship between individual thigh muscle volumes and grip strength, leveraging ...

Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.

European radiology
OBJECTIVES: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial inte...

Assessing the prognostic impact of body composition phenotypes on surgical outcomes and survival in patients with spinal metastasis: a deep learning approach to preoperative CT analysis.

Journal of neurosurgery. Spine
OBJECTIVE: The prognostic significance of body composition phenotypes for survival in patients undergoing surgical intervention for spinal metastases has not yet been elucidated. This study aimed to elucidate the impact of body composition phenotypes...