AIMC Topic: Aged, 80 and over
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Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.
BMC pulmonary medicine
Mar 19, 2025
BACKGROUND: Postoperative pulmonary infection (POI) is strongly associated with a poor prognosis and has a high incidence in elderly patients undergoing major surgery. Machine learning (ML) algorithms are increasingly being used in medicine, but the ...
Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.
BMC medical imaging
Mar 19, 2025
OBJECTIVE: The purpose of the current study is to explore the value of a nomogram that integrates clinical factors and MRI white matter hyperintensities (WMH) radiomics features in predicting the prognosis at 90 days for patients with acute ischemic ...
Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.
Nature communications
Mar 19, 2025
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and v...
Using Deep Learning to Perform Automatic Quantitative Measurement of Masseter and Tongue Muscles in Persons With Dementia: Cross-Sectional Study.
JMIR aging
Mar 19, 2025
BACKGROUND: Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with ...
The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.
International journal of legal medicine
Mar 18, 2025
INTRODUCTION: Age estimation, especially in adults, presents substantial challenges in different contexts ranging from forensic to clinical applications. Bone mineral density (BMD), with its distinct age-related variations, has emerged as a critical ...
Artificial intelligence-based incisive canal visualization for preventing and detecting post-implant injury, using cone beam computed tomography.
International journal of oral and maxillofacial surgery
Mar 17, 2025
The aim of this study was to clinically validate an artificial intelligence (AI)-based tool for automatic segmentation of the mandibular incisive canal (MIC) on cone beam computed tomography (CBCT), enabling prevention and detection of iatrogenic imp...
Utilizing machine learning to identify fall predictors in India's aging population: findings from the LASI.
BMC geriatrics
Mar 17, 2025
BACKGROUND: Depression has a detrimental effect on an individual's mental and musculoskeletal strength multiplying the risk of fall incidents. The current study aims to investigate the association between depression and falls in older adults using ma...
Real time artificial intelligence assisted carotid artery stenting: a preliminary experience.
Journal of neurointerventional surgery
Mar 17, 2025
BACKGROUND: Neurointerventionalists must pay close attention to multiple devices on multiple screens simultaneously, which can lead to oversights and complications. Artificial intelligence (AI) has potential application in recognizing and monitoring ...
A computed tomography angiography-based radiomics model for prognostic prediction of endovascular abdominal aortic repair.
International journal of cardiology
Mar 14, 2025
OBJECTIVE: This study aims to develop a radiomics machine learning (ML) model that uses preoperative computed tomography angiography (CTA) data to predict the prognosis of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysm (AAA) patien...
Unsupervised learning to identify symptom clusters in older adults undergoing chemotherapy.
Journal of geriatric oncology
Mar 14, 2025
INTRODUCTION: Unsupervised machine learning (ML) approaches such as clustering have not been commonly applied to patient-reported data. This study describes ML methods to explore and describe patient-reported symptom trajectories in older adults rece...
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