Preventive Care

Risk stratification for postoperative hematoma following anterior cervical spine surgery: a machine learning approach.

BACKGROUND: Anterior approaches to the cervical spine have consistently increased annually, with commonly performed procedures demonstrating low morbidity and mortality rates. However, rare complications of postoperative hematoma requiring readmission or reoperation poses risks such as respiratory compromise and reintubation. This study sought to utilize machine learning algorithms (MLA) on Americ...

Development and temporal external validation of a high-specificity XGBoost rule-in model for diabetes in middle-aged and older Korean adults.

Early identification of diabetes in older adults is essential for preventing complications, yet many high‑risk individuals remain undetected in community settings. Using recent cycles of the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES 2020-2023), we developed and temporally validated an Extreme Gradient Boosting (XGBoost) model to rule-in diabetes amon...

Impact of AI-based Slab Reconstruction Technology on the Diagnostic Accuracy of Screening Digital Breast Tomosynthesis.

Background Digital breast tomosynthesis (DBT) uses 1-mm slices, resulting in a larger number of images and longer interpretation times than convention...

Artificial intelligence-assisted urine cytology based on the Paris System for Reporting Urothelial Carcinoma.

BACKGROUND: Urine cytology is a noninvasive and valuable tool for detecting urothelial carcinoma but suffers from variable sensitivity and observer de...

Machine learning models for reducing false positives in Fluorometric newborn screening of phenylketonuria: Development, validation, and clinical application.

BACKGROUND: Fluorometric newborn screening for phenylketonuria (PKU) is widely used but plagued by high false positives (FPs), leading to unnecessary ...

[Artificial intelligence in diabetic retinopathy screening].

INTRODUCTION: Visual impairment and blindness continue to represent a substantial disease burden in Hungary. According to national epidemiological dat...

Development and External Validation of a Large Language Model-Based Clinical Decision Support System for Colonoscopy Surveillance Intervals.

BACKGROUND: Adherence to guideline-based colonoscopy surveillance intervals remains suboptimal. Large language models (LLMs) show promise for automati...

Development of Machine Learning-Driven Dual-Task Gait Test Model for Cognitive Impairment Screening.

OBJECTIVE: To develop an artificial intelligence (AI)-aided dual-task gait test model for scalable, high-throughput cognitive impairment screening. DE...

AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.

The ability to reliably predict protective immunity against infectious diseases remains a central challenge in immunology, vaccine development, and pu...

Optimizing cervical breath sound acquisition protocol in dysphagia patients: an orthogonal experimental study.

BACKGROUND: Dysphagia is a highly prevalent condition among patients with stroke. Cervical auscultation serves as a crucial screening method for dysph...

Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.

BACKGROUND: The exponential growth of scientific publications has increased the complexity of evidence synthesis. Systematic reviews remain essential ...

Repurposing cardiovascular disease prediction models for cancer.

INTRODUCTION: Population cancer screening detects the presence of early-stage disease rather than assessing future disease risk. We evaluated whether ...

Pediatric post-acute sequelae of SARS-CoV-2 infection in Taiwan: Insights from the DISCOVER cohort.

The post-acute sequelae of SARS-CoV-2 infection (PASC), or long COVID, represent a multifaceted challenge in pediatric populations, characterized by s...

Advances in rapid microbiological testing for animal diseases: A review.

Animal pathogenic microorganisms destabilize livestock economies and jeopardize human health through zoonotic transmission and foodborne illness. Trad...

Multi-task deep learning for sub-clinical screening of mood and sleep disturbances using physical activity biomarkers.

BACKGROUND: Sub-clinical screening for mood disorders and sleep disturbances remains challenging due to limited accessibility of mental health screeni...

External Validation of a mHealth Tool for Detecting Gingival Inflammation in Community-Dwelling Older Adults.

OBJECTIVE: This study aimed to externally validate the screening performance of a pro-social (equitable, accessible), explainable AI (XAI)-guided mobi...

A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1.

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme implicated in cancer immune escape and remains an attractive therapeutic target despi...

An integrated computational-experimental approach identifies Malate Synthase G as therapeutic target to disrupt carbon metabolism in chronic Pseudomonas aeruginosa infection.

Pseudomonas aeruginosa is a leading cause of nosocomial infections, particularly in individuals with a compromised immune system. Due to its strong ad...

Beyond Correlation: Causal Intervention for Multi-Label Medical Image Diagnosis.

This paper addresses the challenge of multi-disease diagnosis by integrating causal reasoning into the diagnostic framework. In clinical practice, mul...

Creation of an mHealth Infrastructure to Support the Development and Delivery of mHealth Interventions: Protocol for Demonstration Projects Addressing Smoking Cessation in Cancer Care.

BACKGROUND: Cancer remains a leading cause of morbidity worldwide. To reduce this burden, scalable, effective approaches are needed to address modifia...