Primary Care

Synergistic impact of Gram-negative bacterial co-infection on invasive pulmonary aspergillosis in non-neutropenic patients: a machine learning-based risk stratification approach.

BACKGROUND: Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, where coexisting bacterial infections, particularly with Gram-negative pathogens, may impair susceptibility. However, validated tools for early risk stratification in this population remain unavailable. METHODS: We retrospectively analyzed 437 non-neutropenic adults with bacterial co-infectio...

Auto-LSN: fully automated liver surface nodularity quantification in CT based on deep learning for the evaluation of advanced chronic liver disease.

OBJECTIVES: Liver surface nodularity (LSN) is a recognized non-invasive biomarker of cirrhosis. This study introduces auto-LSN, an artificial intelligence (AI)-based algorithm for fully automated LSN quantification, assesses its association with fibrosis stage and its non-inferiority in diagnostic performance for advanced chronic liver disease (ACLD) and cirrhosis compared to the FDA-approved, sem...

AI software as a third reader in breast cancer screening-a prospective diagnostic observational study.

OBJECTIVE: Despite advances in mammography screening, some cancers remain undetected, prompting the evaluation of artificial intelligence (AI) as an i...

Dual elastography ultrasound for classifying metabolic dysfunction-associated steatotic liver disease: a cross-sectional study within a prospective cohort.

BACKGROUND: The binary diagnostic approach does not reflect the entire spectrum of metabolic dysfunction associated steatotic liver disease (MASLD. We...

Conscience, Care, and Code: Moral Theology, AI, and Ethical Decision-Making at the Thresholds of Life.

Healthcare decisions at the beginning and end of life pose enduring moral challenges that are increasingly intensified by the integration of artificia...

Integrating Regenerative Therapies and Artificial Intelligence for Precision Management of Diabetic Foot Ulcers.

SIGNIFICANCE: Diabetic foot ulcers (DFUs) represent one of the most devastating complications of diabetes, leading to high rates of amputation and mor...

Factors and determinants of primary care to tertiary care referrals in Singapore: A multi-centre analysis using artificial intelligence-powered large language models.

BACKGROUND: As Singapore adopts a population health approach under Healthier Singapore (Healthier SG), optimizing healthcare resources is crucial. We ...

Enhancing nail disease diagnosis: a capsule network with SE attention and dual backbone models.

Nail diseases, including fungal infections and malignancies, pose significant health risks and may lead to severe complications if not accurately diag...

Interpretable machine learning for personalized breast cancer screening recommendations.

Breast cancer is the most common non-skin cancer and the second leading cause of cancer death in U.S. women. Early detection and timely intervention a...

Effectiveness of AI-enhanced colonoscopy: A case-control study using real world evidence in a young screening age population.

BACKGROUND: Computer aided detection (CADe) colonoscopy has been shown in randomized controlled trials to improve detection of adenomatous colorectal ...

Patients' Attitudes and Beliefs Toward Artificial Intelligence Use in Cancer Care: Cross-Sectional Survey Study.

BACKGROUND: Artificial intelligence (AI) is being rapidly integrated into oncologic care, yet little is known about how patients perceive these applic...

Applying biotechnology to overcome cancer drug resistance and improve public health outcomes.

This review examines how biotechnology advances (CRISPR/Cas9, next-generation targeted therapies, nanotechnology-based drug delivery, and immunotherap...

Interpretable machine-learning risk prediction of unplanned extubation among cancer patients with peripherally inserted central catheters.

Unplanned extubation of peripherally inserted central catheters (PICC-UE) in patients with cancer has been linked to factors including women, diabetes...

Development and interpretation of a dual-energy CT-based deep learning radiomics model for predicting new cerebral ischemic lesions after carotid artery stenting: a multicenter study.

OBJECTIVES: Early recognition of individuals at elevated risk for new ipsilateral ischemic lesions (NIILs) after carotid artery stenting (CAS) is vita...

Construction of a depression risk prediction model for hepatitis B patients based on machine learning strategy.

BACKGROUND: Hepatitis B (HBV) is a chronic viral infection that can lead to cirrhosis, liver failure, and liver cancer, and has a profound impact on t...

Artificial intelligence approaches for non-invasive diabetes prediction using ECG signals: A systematic review.

Diabetes is a major global health challenge, with many individuals remaining undiagnosed due to the limitations of traditional screening methods. Arti...

Screening, risk assessment, and rapid detection of mycotoxins in edible herbs based on UPLC-MS/MS and FT-NIR.

Mycotoxin contamination in edible herbs poses substantial health risks and economic losses due to its accumulation. This study employed a modified QuE...

Validation and longitudinal trajectory analysis of an AI-based ECG model for aortic stenosis: from community screening to pre-TAVR risk stratification.

AIMS: Early aortic stenosis (AS) detection remains challenging, with many patients presenting late when left ventricular dysfunction may be irreversib...

Machine learning-based prediction of CAC-defined cardiovascular risk using routine health examination data: a retrospective cross-sectional study in a Taiwanese population.

BACKGROUND: Early identification of individuals at elevated cardiovascular risk using routine health examination data is essential for preventive card...

Prediction of Fontan failure and correlates of Fontan-associated liver disease severity using machine learning and radiomic features from multi-parametric abdominal MRI.

BACKGROUND: Fontan-associated liver disease (FALD) is associated with morbidity and mortality in patients with palliated single ventricle congenital h...