Geriatrics

Machine learning ensemble meets clinical practice: developing a real-world risk prediction model for metabolic syndrome using super learner and scorecard approaches.

INTRODUCTION: Metabolic syndrome (MetS) refers to a cluster of metabolic abnormalities that significantly increase the risk of developing cardiovascular diseases (CVDs). Traditional binary definitions of MetS fall short in capturing its severity spectrum, limiting personalized risk stratification. OBJECTIVES: We aimed to develop a super learner model and multilevel risk scorecard to improve MetS r...

Utility of machine learning algorithms in classification of progressive cognitive impairment in Alzheimer's disease: A retrospective cohort based on China.

BackgroundDistinct risk factors influence Alzheimer's disease (AD) stage stratification, yet effective tools for early diagnosis and prognosis remain limited, especially in middle-aged populations.ObjectiveTo develop machine learning models for predicting cognitive decline and identifying early markers of stage stratification in a middle-aged Chinese cohort.MethodsWe conducted a retrospective stud...

Difference between eyes-open and eyes-closed resting state quantitative electroencephalography (qEEG) for predicting cognitive impairment using deep learning.

OBJECTIVE: Electroencephalography (EEG) is a method that offers detailed observations of electrical activities occurring in the brain's cerebral corte...

Evolving Perceptions and Attitudes to Adopting Generative AI in Professional Settings: Multicenter Longitudinal Qualitative Study of Senior Chinese Hospital Leaders.

BACKGROUND: The rapid evolution of generative artificial intelligence (GenAI) is transforming health care globally. In China, hospitals are rapidly em...

End-to-end Learning of Safe Stimulation Parameters for Cortical Neuroprosthetic Vision.

Direct electrical stimulation of the brain via cortical visual neuroprostheses is a promising approach to restore basic sight for the visually impaire...

Plasma lipidomic fingerprinting enables high-accuracy biomarker discovery for Alzheimer's disease: a targeted LC-MRM/MS approach.

Dysregulation of lipid metabolism is increasingly recognized as a key factor in the pathogenesis of Alzheimer's disease (AD). Unfortunately, an accura...

γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.

γ neuromodulation has emerged as a promising strategy for addressing neurological and psychiatric disorders, particularly in regulating executive and ...

Development and validation of a combined clinical and MRI-based biomarker model to differentiate mild cognitive impairment from mild Alzheimer's disease.

BACKGROUND: Two of the most common complaints seen in neurology clinics are Alzheimer's disease (AD) and mild cognitive impairment (MCI), characterize...

AI-based CT assessment of sarcopenia in borderline resectable pancreatic Cancer: A narrative review of clinical and technical perspectives.

BACKGROUND: Sarcopenia, defined as the progressive loss of skeletal muscle mass and function, has been associated with poor prognosis in patients with...

Misalignment of age clocks.

Biological aging is a complex non-linear process, with markedly distinct starting and end points, yet the biomarkers of its progression remain elusive...

Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods.

BACKGROUND: The peripheral immune system is essential for maintaining central nervous system homeostasis. This study investigates the effects of perip...

Novel insights from comprehensive analysis: The role of cuproptosis and peripheral immune infiltration in Alzheimer's disease.

BACKGROUND: Cuproptosis is increasingly recognized as an essential factor in the pathological process of Alzheimer's disease (AD). However, the specif...

Multimodal Language Model for Jaw Osteonecrosis Diagnosis and Treatment.

Owing to the complexity of the clinical manifestations in patients with osteonecrosis of the jaw (ONJ), particularly in settings lacking oral and maxi...

Artificial intelligence for predicting the risk of bone fragility fractures in osteoporosis.

Osteoporosis is widespread with a high incidence rate, resulting in fragility fractures which are a major contributor to mortality among the elderly. ...

AI in Medical Questionnaires: Innovations, Diagnosis, and Implications.

This systematic review aimed to explore the current applications, potential benefits, and issues of artificial intelligence (AI) in medical questionna...

Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach.

BACKGROUND: As the global population ages, the economic burden of dementia continues to rise. Social isolation-which includes limited social interacti...

Machine learning-based prediction of amyloid positivity using early-phase F-18 flutemetamol PET.

BackgroundPrevious studies have suggested that early-phase imaging of amyloid positron emission tomography (PET) may offer information for predicting ...

Axial Skeletal Assessment in Osteoporosis Using Radiofrequency Echographic Multi-spectrometry: Diagnostic Performance, Clinical Utility, and Future Directions.

Osteoporosis, a prevalent skeletal disorder, necessitates accurate and accessible diagnostic tools for effective disease management and fracture preve...

The impact of dietary restriction on transcriptional profiles of hematopoietic stem cells in aged female mice.

This study investigates the impact of dietary restriction (DR) on gene expression in hematopoietic stem cells (HSCs) derived from aged mice. RNA seque...

AI in Neurology: Everything, Everywhere, All at Once Part 1: Principles and Practice.

Artificial intelligence (AI) is rapidly transforming healthcare, yet it often remains opaque to clinicians, scientists, and patients alike. This revie...