AIMC Topic: Aged
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Around the EQUATOR With Clin-STAR: AI-Based Randomized Controlled Trial Challenges and Opportunities in Aging Research.
Journal of the American Geriatrics Society
Feb 5, 2025
The CONSORT 2010 statement is a guideline that provides an evidence-based checklist of minimum reporting standards for randomized trials. With the rapid growth of Artificial Intelligence (AI) based interventions in the past 10 years, the CONSORT-AI e...
Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients.
The journal of prevention of Alzheimer's disease
Feb 5, 2025
OBJECTIVE: Due to the recognition for the importance of early intervention in Alzheimer's disease (AD), it is important to focus on prevention and treatment strategies for mild cognitive impairment (MCI). This study aimed to establish a risk predicti...
A Meta-Analysis of Artificial Intelligence Technologies Use and Loneliness: Examining the Influence of Physical Embodiment, Age Differences, and Effect Direction.
Cyberpsychology, behavior and social networking
Feb 5, 2025
Recent research has investigated the connection between artificial intelligence (AI) utilization and feelings of loneliness, yielding inconsistent outcomes. This meta-analysis aims to clarify this relationship by synthesizing data from 47 relevant st...
You get the best of both worlds? Integrating deep learning and traditional machine learning for breast cancer risk prediction.
Computers in biology and medicine
Feb 5, 2025
Breast Cancer is the most commonly diagnosed cancer worldwide. While screening mammography diminishes the burden of this disease, it has some flaws related to the presence of false negatives. Adapting screening to each woman's needs could help overco...
Shoulder Musculoskeletal Disorder Rehabilitation Using a Robotic Device Based on Electromyography (EMG) Biofeedback: A Retrospective Cohort Study.
Medicina (Kaunas, Lithuania)
Feb 5, 2025
: While shoulder injuries represent the musculoskeletal disorders (MSDs) most encountered in physical therapy, there is no consensus on their management. In attempts to provide standardized and personalized treatment, a robotic-assisted device combin...
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
Feb 5, 2025
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 ...
Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements.
Scientific reports
Feb 5, 2025
Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different ...
Constructing a machine learning model for systemic infection after kidney stone surgery based on CT values.
Scientific reports
Feb 5, 2025
This study aims to develop a machine learning model utilizing Computed Tomography (CT) values to predict systemic inflammatory response syndrome (SIRS) after endoscopic surgery for kidney stones. The goal is to identify high-risk patients early and p...
Habitat-Based Radiomics for Revealing Tumor Heterogeneity and Predicting Residual Cancer Burden Classification in Breast Cancer.
Clinical breast cancer
Feb 4, 2025
PURPOSE: To investigate the feasibility of characterizing tumor heterogeneity in breast cancer ultrasound images using habitat analysis technology and establish a radiomics machine learning model for predicting response to neoadjuvant chemotherapy (N...
Multi-source sparse broad transfer learning for parkinson's disease diagnosis via speech.
Medical & biological engineering & computing
Feb 4, 2025
Diagnosing Parkinson's disease (PD) via speech is crucial for its non-invasive and convenient data collection. However, the small sample size of PD speech data impedes accurate recognition of PD speech. Therefore, we propose a novel multi-source spar...
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