Geriatrics

Multimodal neuroimaging-based deep learning framework for pattern analysis and early prediction of neurodegenerative diseases.

Neurodegenerative diseases, such as Mild Cognitive Impairment (MCI) and Alzheimer's, pose significant challenges due to their progressive nature and late diagnosis. Early detection remains difficult, particularly when using conventional machine learning approaches that fail to capture complex spatial and temporal patterns in multimodal clinical data. Motivated by the need for accurate, scalable, a...

Results of a new model of videotraining for patients and caregivers for peritoneal dialysis: food for (AI) thought?

BACKGROUND: In an attempt to overcome the space-time limitations of traditional training we used a new telemedicine home-training model (Videotraining). We report our analysis of Videotraining for automated peritoneal dialysis (APD) carried out between October 1, 2017 and September 30, 2023. METHODS: Videotraining is standardized by breaking down the APD procedures (cycler preparation, connection,...

A Synthetic Data-Augmented Deep Learning Framework for Robust Segmentation and Quantification of the Carotid Artery in Ultrasound Images.

OBJECTIVE: To address the severe limitation imposed by the scarcity of annotated data on deep learning-based automated segmentation of the carotid art...

Beyond composite scores in chronotype assessment: item-level predictive patterns in the Morningness-Eveningness Questionnaire.

Chronotype represents individual differences in circadian preferences that influence sleep-wake patterns, cognitive performance, and clinical outcomes...

Agreement between expert gynecologists and ChatGPT in the management of suspected retained products of conception.

OBJECTIVE: To assess agreement between a large language model (ChatGPT version 5.2) and expert gynecologists in the management of suspected retained p...

Assessing pediatricians' readiness for artificial intelligence: a cross-sectional study in Istanbul, Türki̇ye.

BACKGROUND: Artificial intelligence (AI) is increasingly integrated into healthcare, including pediatrics, offering new opportunities for diagnosis, m...

Chest Radiograph-Derived Age Acceleration as an Early Marker of Pulmonary Dysfunction in Middle-Aged Asians.

BACKGROUND: Deep learning-derived, chest radiographic (CXR) age may capture subclinical structural changes associated with spirometric impairment. RES...

Profiling cognition and brain metabolism in amyotrophic lateral sclerosis and frontotemporal dementia.

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are described as a disease continuum, given their shared clinical, genetic and p...

Connected-speech digital biomarkers for monitoring transcranial pulse stimulation in Alzheimer's disease: A pilot study.

BackgroundAlzheimer's disease (AD) lacks effective disease-modifying therapies and scalable, ecologically valid biomarkers to monitor treatment respon...

Fluorescence spectroscopy and machine learning methods for detection of Alzheimer's disease from circulating white blood cells.

BackgroundAlzheimer's disease (AD) is the most common cause of dementia whose prevalence is projected to increase significantly in the coming decades....

pedQTNet: A Deep Learning Approach to Estimate Corrected QT Intervals from Multi-Lead Conventional ECG Waveforms in Pediatric Patients.

Long QT syndrome (LQTS) is a primary risk factor for ventricular arrhythmias and sudden cardiac death in children. Accurate corrected QT intervals (QT...

Development and validation of a machine learning-based clinical decision support tool for stratifying intravenous medication risk in hospitalized patients with heart failure.

INTRODUCTION: Hospitalized patients with heart failure (HF) frequently receive multiple high-risk intravenous (IV) medications, placing them at a subs...

Artificial Intelligence-Mediated Teledentistry for Ageing Populations: Toward Intelligent Intermediary Support in Home-Based Oral Care.

The rapid ageing of the global population is reshaping oral health care delivery, with increasing numbers of older adults receiving care in home-based...

Association of Deep Learning-Derived Temporalis Sarcopenia with Mortality in Acute Ischemic Stroke.

BACKGROUND: Sarcopenia is associated with mortality following acute ischemic stroke (AIS), but diagnosis is time-consuming. Computed tomography (CT) m...

Genetic architecture of white matter microstructure captured by unsupervised deep representation learning of fractional anisotropy maps.

Fractional anisotropy (FA) from diffusion MRI is a widely used marker of white matter (WM) integrity, but conventional FA-based genetic studies typica...

Sign language narrative reveals universal and modality-specific features of cortical timescale hierarchy.

Human languages are unique in their capacity to tell stories. Prior neuroimaging studies show that spoken language narrative comprehension engages a n...

Machine learning and deep learning-based drug-drug interactions prediction: a systematic review focused on anticancer drugs.

Cancer patients are particularly susceptible to Drug-Drug Interactions (DDIs) due to frequent polypharmacy in oncology care. Co-administered drugs can...

Speech-based machine learning for detecting neuropsychiatric symptoms in Alzheimer's disease.

BackgroundNeuropsychiatric symptoms (NPS) are common in Alzheimer's disease (AD) and mild cognitive impairment (MCI), yet their detection relies on su...

Machine-Learning-Assisted Triple-Gated Raman Enhancement Platform for Selectively Quantifying Lysophosphatidylcholine (16:0) as a Potential Biomarker for Cognitive Impairments.

Accurate quantification of structurally similar metabolites as biomarkers in biofluids has remained a longstanding challenge. Here, we report a semico...

ABCA8 drives bone loss via disrupting Th17/Treg imbalance: a novel immunometabolic target for osteoporosis.

BACKGROUND: Osteoporosis is a metabolic bone disease characterized by reduced bone mass and microarchitectural deterioration, with complex involvement...