Geriatrics

Speech-based digital biomarkers for early etiological stratification of Alzheimer's disease and frontotemporal degeneration: a biomarker-confirmed prospective study.

BACKGROUND: Early differentiation between Alzheimer's disease (AD) and frontotemporal lobar degeneration (FTLD) is a prerequisite for secondary prevention and targeted trial enrollment, yet remains challenging at disease onset. We investigated whether automated speech analysis could serve as a digital biomarker for early etiological stratification across clinically heterogeneous presentations. MET...

Evaluation of Brain Age on Magnetic Resonance Imaging Using Machine Learning Techniques in Patients With Hearing Loss.

OBJECTIVES: Hearing impairment is strongly linked to cognitive decline, with individuals having mild-to-severe hearing loss estimated as having a twofold to fivefold increased risk of all-cause dementia. Given the lack of a cure for dementia, addressing conditions associated with increased dementia risk, like hearing loss, is essential. "Brain age," estimated from magnetic resonance imaging (MRI) ...

Digital experts for fibromyalgia: A comparative analysis of ChatGPT, Gemini, and DeepSeek in addressing patient questions with focus on readability and medical accuracy.

BackgroundPatients with fibromyalgia require clear and reliable medical information to manage a complex chronic condition. AI-based tools may offer va...

Independent bone-level diagnostic accuracy study of an AI tool for detecting appendicular skeletal fractures on radiographs.

OBJECTIVES: To perform an in-depth evaluation of the diagnostic test accuracy of a commercially available AI tool for assistance in fracture detection...

Oral microbiome signatures predict biological age and host health.

Identifying robust, non-invasive biomarkers of biological age is key to preventive medicine. While gut aging clocks exist, the oral microbiome remains...

Cogninet: an explainable deep learning model for multi-class MRI-based Alzheimer's disease staging.

PURPOSE: Alzheimer's Disease (AD) is a neurodegenerative condition which presents significant challenges in early diagnosis and clinical decision-maki...

Preoperative frailty for predicting in-hospital mortality in patients after cardiac surgery: an interpretable Machine learning model based on a retrospective multicenter cohort study.

BACKGROUND AND OBJECTIVE: Accurate prediction of mortality risk after cardiac surgery is crucial for perioperative management. The laboratory-based fr...

Large Language Models for the Differentiation of Benign and Malignant Liver Nodules based on Multimodal Prompts in Liver US Cases.

OBJECTIVE: Large language models (LLMs) that can process both images and text are increasingly being used in radiology. This study aimed to evaluate t...

Echo-SMADS: A hierarchical planning model for predicting ejection fraction using echocardiography.

BACKGROUND AND OBJECTIVE: Current deep learning approaches for predicting ejection fraction primarily rely on end-to-end regression. While effective i...

The Assessment of Coronal Plane Lower-Limb Alignment on Pre- and Postoperative Long-Leg Radiographs Using Deep Learning.

INTRODUCTION: Coronal plane lower-limb alignment, defined by metrics including the hip-knee-ankle angle (HKAA), lateral distal femoral angle (LDFA), a...

Imaging without biology: limitations of CT-derived psoas muscle volume as a standalone diagnostic criterion for sarcopenia.

We comment on the study by Choi et al., which proposes CT-derived psoas muscle volume (PV) as a diagnostic indicator for sarcopenia. The study is well...

Ultra-Robust and Hyperelastic Triboelectric Webbing for Self-Powered Rehabilitation Sensing with Invisible and Embedded Design.

Driven by the rapid evolution of flexible electronics, rehabilitation healthcare is shifting toward devices that seamlessly interface with human body....

In silico virtual knockout identifies PXDNL as a fibroblast-specific driver of sarcopenia and GABA as a potential modulator.

Sarcopenia lacks causal mechanisms and translatable targets. We integrated virtual gene knockout with multi-omics (n = 238 biopsies, 5 GEO cohorts) an...

The Insomnia EEG Score: a new tool for the classification of people with poor sleep.

Quantitative features could help objectively identify and grade insomnia severity, though there is currently no pathophysiological biomarker of insomn...

Automated deep learning for real-time focal liver lesions detection in ultrasound videos a multicenter study.

Early detection of focal liver lesions (FLLs) is crucial for clinical practice, but ultrasound performance heavily depends on operator experience. We ...

Molecular Biomarkers and Risk Prediction Models for Acute Kidney Injury Following Liver Transplantation: A Review.

Liver transplantation is the definitive treatment for end-stage liver disease; however, postoperative acute kidney injury (AKI) affects 30%-70% of rec...

Fluid Volumes Longitudinal Modeling to Predict Atrophy and Fibrosis in Neovascular Age-Related Macular Degeneration.

PURPOSE: To evaluate the impact of retinal fluid volumes on the development of atrophy and fibrosis in neovascular age-related macular degeneration (n...

Diagnostic performance of deep learning models on ultrasound images for distinguishing benign from malignant ovarian cysts.

BACKGROUND: Ovarian cysts are a common pelvic disorder in women, and accurate differentiation between benign and malignant types is essential for guid...

Evaluating Large-Language Models Against Providers on Surgical Diagnostic Reasoning Tasks.

INTRODUCTION: Large language models (LLMs) have shown efficacy in surgery-related tasks such as literature screening and performing well on-board exam...

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration.

Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves m...