AIMC Topic: Aged, 80 and over

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Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.

European journal of radiology
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...

Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care.

Journal of biomedical informatics
OBJECTIVE: Dementia represents a growing public health challenge, affecting an increasing number of individuals. It encompasses a broad spectrum of cognitive impairments, ranging from mild to severe stages, each of which demands varying levels of car...

Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals.

EBioMedicine
BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patterns. Traditional classification systems based on left ventricular ejection fraction (LVEF) have limitations in capturing the heterogeneity of HF. We a...

Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.

BMC pulmonary medicine
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
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
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
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 ...

A Scoping Review on Long-Term Care Workers' Perceptions of Robot-Assisted Care for Older Adults in Long-Term Care Facilities.

Journal of advanced nursing
AIMS: To synthesise the current research on long-term care workers' perceptions (i.e., attitudes, concerns, and expected functions) of robot-assisted care and their perceived effects of different types of robot-assisted care for older adults in long-...

The impact of multi-modality fusion and deep learning on adult age estimation based on bone mineral density.

International journal of legal medicine
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 ...

Comparing two deep learning spectral reconstruction levels for abdominal evaluation using a rapid-kVp-switching dual-energy CT scanner.

Abdominal radiology (New York)
PURPOSE: Deep Learning Spectral Reconstruction (DLSR) potentially improves dual-energy CT (DECT) image quality, but there is a paucity of research involving human abdominal DECT scans. The purpose of this study was to comprehensively evaluate image q...