Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 69,181 to 69,190 of 233,923 articles

Deep learning model for identifying significant tricuspid regurgitation using standard 12-lead electrocardiogram.

International journal of cardiology. Cardiovascular risk and prevention
INTRODUCTION: Transthoracic echocardiography (TTE) is the current standard for detecting tricuspid regurgitation (TR); however, it incurs additional costs and is dependent on the operator's skill. In contrast, the 12-lead electrocardiogram (ECG) is w... read more 

Programmable materials: A comprehensive review on shape memory polymers for orthopedic applications.

Journal of orthopaedics
Shape memory polymers (SMP) are smart, active polymers, which can significantly change their properties by external stimuli such as heat or temperature, pH, electric and magnetic fields. SMPs take advantage over traditional metal alloys due to their ... read more 

Gut microbiome profiles and associated functional pathways are linked to Mediterranean diet adherence and blood glucose control in adults with type 1 diabetes mellitus.

Nutrition, metabolism, and cardiovascular diseases : NMCD
BACKGROUND AND AIMS: The Mediterranean diet (MD) has been associated with better glycaemic control in children with type 1 diabetes mellitus (T1DM) and favourable microbiome profiles in healthy individuals. However, it remains unclear whether MD adhe... read more 

Reply to comment on impact of deep learning on CT-based organ-at-risk delineation for flank irradiation in paediatric renal tumours: A SIOP-RTSG radiotherapy committee.

Clinical and translational radiation oncology
We appreciate the commentary from Saad et al., which offers an opportunity to clarify key methodological and clinical aspects of our study assessing the impact of deep learning-based CT auto-contouring for organ-at-risk delineation in paediatric flan... read more 

RivAIrSet: A multitemporal high-resolution UAV imagery dataset for machine learning-based river water segmentation.

Data in brief
Understanding and predicting river dynamics requires frequent and systematic observations from imaging sensors deployed in situ or on different platforms. In this context, machine learning methods are increasingly applied to pixel-based classificatio... read more 

MEFA-Unet: Multi-scale feature extraction and fusion attentional unet for segmenting short process of incus in otologic microsurgical scenarios.

Computer methods and programs in biomedicine
BACKGROUND: The application of convolutional neural networks (CNNs) in microsurgery has been limited. Current CNNs struggle to capture diverse semantic details across various scales and receptive fields, and they also face challenges in establishing ... read more 

Effect of media age on cytotoxicity of drinking water treated by biological activated carbon filters and chlorine disinfection.

Water research
Biological activated carbon (BAC) filters are widely employed for controlling the formation of harmful disinfection byproducts (DBPs) prior to chlorine disinfection. Aged BAC filters are commonly practiced at drinking water treatment plants due to th... read more 

PDE-GANet: Partial differential equation discovery powered by adversarial learning.

Neural networks : the official journal of the International Neural Network Society
Partial differential equations (PDEs) are typically derived through theoretical deduction or empirical observation of the real world phenomena, such as the Navier-Stokes equation and the Boltzmann equation. However, for complex systems in domains lik... read more 

Contrastive learning-based fusion of NIR spectroscopy and visual cues for molecular discrimination of Chinese herbal species.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Accurate identification of Chinese medicinal herbs is essential for ensuring quality control and clinical safety. However, conventional visual inspection and machine vision techniques often struggle to distinguish morphologically similar herbs. This ... read more 

Full-spectrum prompt tuning with sparse MoE for open-set recognition.

Neural networks : the official journal of the International Neural Network Society
Recent advances in open-set recognition leveraging vision-language models (VLMs) predominantly focus on improving textual prompts by exploiting (high-level) visual features from the final layer of the VLM image-encoder. While these approaches demonst... read more