Artificial Intelligence Medical Compendium

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

Showing 451 to 460 of 6,574 articles

Exploring the mechanism of metabolic cell death-related genes AKR1C2 and MAP1LC3A as biomarkers in Parkinson's disease.

Scientific reports
There is a strong relationship between metabolic cell death (MCD) and neurodegenerative diseases. However, the involvement of metabolic cell death (MCD)-related genes (MCDRGs) in Parkinson's disease (PD) pathogenesis remains poorly analyzed. Integrat... read more 

Exploring novel molecular mechanisms underlying recurrent pregnancy loss in decidual tissues.

Scientific reports
Recurrent pregnancy loss (RPL), which affects approximately 2.5% of reproductive-aged women, remains idiopathic in more than 50% of cases, necessitating mechanistic insights and biomarkers. Three RPL decidual tissue transcriptomic datasets (GSE113790... read more 

Research on multi-branch residual connection spectrum image classification based on attention mechanism.

Scientific reports
The acoustic spectrogram arranges the frequencies in the sound along the frequency spread, and translates the spectral changes into the intensity, wavelength and frequency of the electrical signals. Currently, the extensive use of convolutional neura... read more 

A hybrid fog-edge computing architecture for real-time health monitoring in IoMT systems with optimized latency and threat resilience.

Scientific reports
The advancement of the Internet of Medical Things (IoMT) has transformed healthcare delivery by enabling real-time health monitoring. However, it introduces critical challenges related to latency and, more importantly, the secure handling of sensitiv... read more 

Predicting New York Heart Association (NYHA) heart failure classification from medical student notes following simulated patient encounters.

Scientific reports
Random forest models have demonstrated utility in the determination of New York Heart Association (NYHA) Heart Failure Classifications. This study aims to determine the prediction accuracy of a random forest model to derive NYHA Classification from m... read more 

Integrating vision transformer-based deep learning model with kernel extreme learning machine for non-invasive diagnosis of neonatal jaundice using biomedical images.

Scientific reports
Birth complications, particularly jaundice, are one of the leading causes of adolescent death and disease all over the globe. The main severity of these illnesses may diminish if scholars study more about their sources and progress toward effective t... read more 

Preoperative prediction value of 2.5D deep learning model based on contrast-enhanced CT for lymphovascular invasion of gastric cancer.

Scientific reports
To develop and validate artificial intelligence models based on contrast-enhanced CT(CECT) images of venous phase using deep learning (DL) and Radiomics approaches to predict lymphovascular invasion in gastric cancer prior to surgery. We retrospectiv... read more 

Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers.

Scientific reports
Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of the fetus and image quality fluctuation, its interpretation is quite challenging. Although deep learning include Co... read more 

Efficacy of swarm-based neural networks in automated depression detection.

Scientific reports
As depression becomes a global pandemic, this research paper presents a comprehensive study for depression diagnosis using a custom-crafted deep learning model optimized with various swarm intelligence algorithms. Three different optimization algorit... read more 

Uncertainty aware domain incremental learning for cross domain depression detection.

Scientific reports
Deep learning techniques have demonstrated significant promise for detecting Major Depressive Disorder (MDD) from textual data but they still face limitations in real-world scenarios. Specifically, given the limited data availability, some efforts ha... read more