AI Medical Compendium Journal:
Aging clinical and experimental research

Showing 1 to 10 of 13 articles

Cost-effectiveness of opportunistic osteoporosis screening using chest radiographs with deep learning in Germany.

Aging clinical and experimental research
BACKGROUND: Osteoporosis is often underdiagnosed due to limitations in traditional screening methods, leading to missed early intervention opportunities. AI-driven screening using chest radiographs could improve early detection, reduce fracture risk,...

Neuropsychological tests and machine learning: identifying predictors of MCI and dementia progression.

Aging clinical and experimental research
BACKGROUND: Early prediction of progression in dementia is of major importance for providing patients with adequate clinical care, with considerable impact on the organization of the whole healthcare system.

An explainable machine learning-based prediction model for sarcopenia in elderly Chinese people with knee osteoarthritis.

Aging clinical and experimental research
BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disease that leads to loss of muscle mass and function, resulting in adverse health outcomes such as falls, functional decline, and death. Knee osteoarthritis (KOA) is a common chro...

Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.

Aging clinical and experimental research
OBJECTIVES: Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an expla...

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...

A comprehensive review of machine learning algorithms and their application in geriatric medicine: present and future.

Aging clinical and experimental research
The increasing access to health data worldwide is driving a resurgence in machine learning research, including data-hungry deep learning algorithms. More computationally efficient algorithms now offer unique opportunities to enhance diagnosis, risk s...

Advanced age is an independent prognostic factor of disease progression in high-risk prostate cancer: results in 180 patients treated with robot-assisted radical prostatectomy and extended pelvic lymph node dissection in a tertiary referral center.

Aging clinical and experimental research
OBJECTIVES: This study aimed to assess more clinical and pathological factors associated with prostate cancer (PCa) progression in high-risk PCa patients treated primarily with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph nod...

Prediction of postoperative infection in elderly using deep learning-based analysis: an observational cohort study.

Aging clinical and experimental research
Elderly patients are susceptible to postoperative infections with increased mortality. Analyzing with a deep learning model, the perioperative factors that could predict and/or contribute to postoperative infections may improve the outcome in elderly...

Predicting restriction of life-space mobility: a machine learning analysis of the IMIAS study.

Aging clinical and experimental research
BACKGROUND: Some studies have employed machine learning (ML) methods for mobility prediction modeling in older adults. ML methods could be a helpful tool for life-space mobility (LSM) data analysis.