The rising prevalence of cardiometabolic multimorbidity (CMM), characterized by the coexistence of two or more cardiometabolic disorders, poses a significant public health challenge in aging populations. While ambient air pollution is a recognized en...
Secondary cancers (SCs) following radiotherapy (RT) represent a significant long-term risk of cancer survivors, necessitating accurate predictive models for early intervention. This study developed a machine learning (ML) model integrating clinical, ...
Assessing the efficacy of radiotherapy in patients with high-grade gliomas (HGGs) is challenging due to the occurrence of pseudo-progression and radionecrosis. This study introduces a directed graph network leveraging MR image features at multiple ti...
OBJECTIVE: Mild cognitive impairment (MCI) signals cognitive decline beyond normal aging and increases dementia risk. Early identification enables preventative interventions, yet many patients in primary care go undetected. This study examines whethe...
Early triage of trauma patients requiring massive transfusion (MT) may help to marshal appropriate resources and improve treatment and outcome. Artificial intelligence (AI) and machine learning (ML) offer theoretical advantages compared to convention...
PURPOSE: To propose a multi-parametric ultrasound imaging-based deep learning method for accurately classifying metastatic and non-metastatic axillary lymph nodes in breast cancer patients.
BACKGROUND AND PURPOSE: This study aims to develop a robust and user-friendly prediction model for radiation-induced hypothyroidism (RIHT) in nasopharyngeal carcinoma (NPC) patients.
BMC medical informatics and decision making
Oct 23, 2025
BACKGROUND: Traditional diagnostic methods used by neurosurgeons are limited in their ability to address complex interactions. These limitations have necessitated the use of advanced artificial intelligence approaches capable of analyzing multidimens...
BACKGROUND: This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction.
BACKGROUND/AIMS: This study aimed to investigate whether a real-time artificial intelligence (AI)-assisted polyp detection system can improve adenoma detection rates (ADRs) in real-world colonoscopy practice.
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