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Development and Validation of Machine Learning Models for Predicting Early Cognitive Decline Using Home Sensor-Derived Behavioral Data: Sensors in-Home for Elder Wellbeing (SINEW) Cohort Study.

BACKGROUND: As the global population continues to age, the prevalence of geriatric conditions, including dementia and frailty, is also increasing. Early identification of individuals at an elevated risk of these conditions, such as those presenting with mild cognitive impairment (MCI) or prefrailty, can provide a critical window for prompt intervention aimed at preventing or reversing disease prog...

The Patient Monitoring Roundtable as Catalyst for Health Care Innovation: Case Study.

BACKGROUND: In the field of patient monitoring, there often remains a gap between clinical needs and the monitoring technologies available from industry. To conquer this, the Patient Monitoring Roundtable (PMRT) live event series offers a sustainable and structured platform for innovation through focused small-group discussions, prioritizing deep engagement among stakeholders. By establishing a dy...

Training an AI Chatbot to Manage Health in Underserved Populations: Methodological Approach.

BACKGROUND: Health disparities such as morbidity and mortality among childbearing women remain high in the United States, especially among those with ...

Deep-Learning Algorithm Diagnostic Support for Usual Interstitial Pneumonia Pattern Recognition in Fibrotic Interstitial Lung Disease.

BACKGROUND AND OBJECTIVE: High resolution computed tomography (HRCT) scan diagnostic classification for usual interstitial pneumonia (UIP) plays a cri...

Using Arts-Based Methods to Involve People Living in Tower Hamlets With Multiple Long-Term Conditions in the Development of Artificial Intelligence Tools in Healthcare Research.

BACKGROUND: Including public contributors in the development of artificial intelligence (AI) systems in healthcare research is growing, however, tradi...

Benchmarking large language models for quality control of chest radiographs and CT reports: a retrospective multimodal study.

PURPOSE: This study aims to establish a retrospective, single-centre, feasibility-oriented benchmark for medical imaging quality control(QC) and to ev...

Towards Cognitive Impairment Screening in Elderly Communities with Audio-Visual Modal Disentangled Representation Learning.

Alzheimer's disease (AD) is pressing global health concerns, for which early diagnosis is critical to effective intervention. However, conventional ap...

Smartphone-based digital phenotyping for detection of high-risk depression and anxiety in Korean community settings.

BACKGROUND: Smartphones generate continuous behavioral signals such as mobility and activity patterns, offering scalable opportunities for monitoring ...

Transformer-based super-resolution lung CT images improve visualization of multiple diseases.

OBJECTIVES: To assess the Transformer-based Swin2SR model for super-resolution (SR) enhancement of lung CT images and its clinical potential. METHODS:...

Zero-Shot Lung Disease Detection Using Radiological Symptomatic Descriptors and Pretrained Neural Networks.

Aligning radiological features with clinical text descriptions remains a key challenge for zero-shot disease recognition in chest radiography. We prop...

Neoantigen Targeting as a Novel Approach for Therapy-Resistant Tumors.

Neoantigens have emerged as central targets in the development of individualized cancer vaccines, as they are recognized by the immune system as forei...

An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.

Chest radiography remains an essential diagnostic tool for thoracic diseases, yet interpreting overlapping anatomical structures is particularly chall...

Physics-constrained neural ordinary differential equation models to discover and predict microbial community dynamics.

Microbial communities play essential roles in shaping ecosystem functions and predictive modeling frameworks are crucial for understanding, controllin...

PTBD: a machine learning-based non-invasive diagnostic model for pulmonary tuberculosis using large-scale blood transcriptomes.

BACKGROUND: Accurate and rapid diagnosis is essential for controlling pulmonary tuberculosis (PTB) by enabling timely intervention and reducing diseas...

Radiation Dose Optimisation Through Artificial Intelligence (AI)-based Auto-Thorax Collimation.

INTRODUCTION: Artificial intelligence (AI) is increasingly adopted in digital radiography to enhance workflow efficiency, standardisation and support ...

Deciphering pharmaceutical resistance in sulfur-driven autotrophic denitrification: an integrated multi-omics artificial intelligence-driven structural biology approach.

Sulfur-driven autotrophic denitrification (SdAD) is a promising low-carbon technology for nitrogen removal; however, its stability and adaptive mechan...

Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.

BACKGROUND: Recent developments in physiological, imaging and digital biomarkers combined with the approval of new disease-modifying drugs against Alz...

Comparing AI-Assisted Coding and Traditional Qualitative Analysis: a Study Examining Differences in Methods and Results of AI-Assisted Coding and Traditional Content Coding Using Community Engagement Data Collected During the Development of a Municipal Food Plan.

Qualitative data poses a challenge for prevention science and public health, as it is critical to explain the context of communities, health, and beha...

Development and Evaluation of a Human-in-the-Loop Data Curation Training Program to Support a Digital Clinical Trial Platform: Descriptive Feasibility Study.

BACKGROUND: LookUpTrials is a clinician-facing digital platform designed to support point-of-care navigation of institution-specific oncology clinical...

Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data.

BACKGROUND: This study aimed to develop and validate machine learning (ML) models for predicting the risk of cognitive frailty in community-dwelling e...