AIMC Topic: Aged

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An MRI-based radiomics framework for early identification and progression stratification in knee osteoarthritis: data from the osteoarthritis initiative.

BMC musculoskeletal disorders
OBJECTIVES: To develop a cascaded machine learning model based on MRI radiomics features from cartilage and subchondral bone to predict the incidence and progression of knee osteoarthritis (KOA), thereby addressing the need for early intervention in ...

Characteristics of brain glucose metabolism in Parkinson's disease patients with freezing of gait: a study based on F-FDG PET imaging and deep learning.

BMC neurology
OBJECTIVE: Freezing of gait (FOG) is a common gait disorder in the advanced stages of Parkinson's disease (PD), closely associated with impaired balance and executive function. This study aimed to investigate specific changes in brain glucose metabol...

Application of interpretable machine learning to predict activities of daily living disability in sarcopenia: insights from the CHARLS dataset.

BMC geriatrics
PURPOSE: The decline in activities of daily living (ADL) among older persons is a significant public health concern. Sarcopenia is a major risk factor for ADL disability. This study aimed to develop and validate an interpretable machine learning (IML...

Artificial intelligence improves detection and classification of pulmonary venous hypertension related to left ventricular diastolic dysfunction by chest radiography.

Scientific reports
Isolated-Left Ventricular Diastolic Dysfunction [LVDD] ranges (and may progress) from preclinical asymptomatic, symptomatic-LVDD, to LVDD-predominate Heart Failure [HF] presentations; if recognized early, LVDD progression might be preventable. Curren...

Gene expression signatures from whole blood predict amyotrophic lateral sclerosis case status and survival.

Nature communications
Amyotrophic lateral sclerosis (ALS) is a rare and fatal neurodegenerative disease with a median survival of only 2 to 4 years from diagnosis. Improved tools are needed to shorten diagnostic delays and improve prognostication to benefit clinical care....

A multimodal multitask deep learning model for predicting stroke lesion and functional outcomes using 4D CTP imaging and clinical metadata.

Scientific reports
Acute ischemic stroke is a major global health challenge, leading to long-term disability or death without timely intervention. Among neuroimaging modalities, spatio-temporal (4D) computed tomography perfusion (CTP) is widely used to assess cerebral ...

Optic disc morphometrics as a potential ocular biomarker for depression: evidence from two cross-sectional cohort studies.

Translational psychiatry
Depression, which is increasingly prevalent among older adults, has traditionally been diagnosed through symptom-based questionnaires. However, emerging evidence suggests that retinal changes could serve as objective biomarkers for depression. In thi...

Comparative study of coronary artery disease prediction: conventional QRISK3 versus enhanced machine learning models combined with particle swarm optimisation algorithm.

Open heart
BACKGROUND: Coronary artery disease (CAD) is one of the biggest causes of mortality worldwide. Risk stratification for early detection is essential for the primary prevention of CAD. QRISK3 is known to overestimate future CAD risk in some populations...

Artificial intelligence-powered spatial analysis of tumor microenvironment in patients with non-small cell lung cancer with acquired resistance to EGFR tyrosine kinase inhibitor.

Journal for immunotherapy of cancer
PURPOSE: This study evaluated the dynamic changes in the tumor microenvironment (TME) in patients with non-small cell lung cancer (NSCLC) and acquired resistance to epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs) using an ar...

Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics.

Clinical and experimental medicine
OBJECTIVE: To utilize machine learning techniques to screen contrast-enhanced ultrasound (CEUS) parameters and clinical characteristics, aiming to differentiate diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in patients with diabeti...