AI Medical Compendium

Spliceread: improving canonical and non-canonical splice site prediction with residual blocks and synthetic data augmentation.

BMC bioinformatics May 22, 2026

Accurate splice site prediction is fundamental to understanding gene expression and its associated disorders. However, most existing models are biased toward frequent canonical sites, limiting their ability to detect rare but biologically important n... read more

Development of an interpretable machine learning model for predicting stress urinary incontinence following the delivery of a first child.

BMC pregnancy and childbirth May 22, 2026

INTRODUCTION: The goal of this research is to use machine learning (ML) techniques to create a risk prediction model for postpartum stress urinary incontinence (PSUI). To improve screening accuracy and optimize clinical care techniques, the goal is t... read more

Y-Polyp: research on devices for detecting colorectal polyps with limited samples.

BMC medical imaging May 22, 2026

BACKGROUNDS: Colonoscopy plays a crucial role in preventing the malignant transformation of colorectal polyps, with early diagnosis and detection of colorectal cancer being effective approaches to reducing incidence and mortality rates among patients... read more

The role of nitrogen dioxide in the prevalence of adverse cardiovascular and cerebrovascular diseases in China: a national multi-pollutant geospatial analysis.

BMC public health May 22, 2026

INTRODUCTION: Cardiovascular and cerebrovascular diseases (CCVDs) pose a severe global health threat, particularly among middle-aged and elderly populations. Ambient air pollution is a well-recognized environmental risk factor for CCVDs, yet existing... read more

Development of an interpretable machine learning model-based online tool for risk prediction of falls and fall-related injuries in Chinese middle-aged and older adults with depressive symptoms-a longitudinal study based on the CHARLS database.

BMC public health May 22, 2026

BACKGROUND: This study aimed to establish and validate interpretable Machine Learning (ML) models for predicting falls and fall-related injuries in middle-aged and older adults with depressive symptoms (DS) and to develop relevant online computationa... read more

A SHAP-based machine learning approach to decoding the relationship between running posture features and tibial load in novice runners with MTSS: towards accurate and intelligent rehabilitation strategies.

BMC musculoskeletal disorders May 22, 2026

BACKGROUND: This study systematically investigates the relationship between running posture and tibial load in novice runners using machine learning methods from the field of artificial intelligence. The aim is to provide scientific evidence for the ... read more

Radiological staging clinical decision support model for rectal cancer lymph node metastasis detection on MRI.

BMC cancer May 22, 2026

BACKGROUND: Accurate staging of lymph node metastasis (LNM) is crucial for personalising rectal cancer treatment. Lymph nodes (LNs) are the most common sites of rectal cancer metastasis, and malignant LNs are typically treated with neo-adjuvant radio... read more

Assessing diagnostic performance of multimodal LLMs and a custom convolutional neural network in tooth-level caries detection and localization.

BMC oral health May 22, 2026

BACKGROUND: Artificial Intelligence is reshaping dental diagnostics through automated interpretation of images. While Convolutional Neural Networks (CNNs) demonstrate high accuracy via domain-specific training, multimodal Large Language Models (LLMs)... read more

Attitudes toward artificial intelligence tools in university students: associations with body appreciation and e-healthy diet literacy - implications for digital health education.

BMC medical education May 22, 2026

BACKGROUND: This cross-sectional study aimed to determine attitudes toward the use of artificial intelligence tools, body appreciation, and e-healthy diet literacy (e-HDL) levels among university students, and to analytically evaluate the relationshi... read more

Accuracy, readability, and content coverage of AI-generated responses to questions on functional appliances.