Artificial Intelligence Medical Compendium

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

Showing 20,261 to 20,270 of 215,962 articles

Deep neural networks in medical microbiology for bacterial colonies classification.

Scientific reports May 12, 2026
While automation has transformed many areas inside clinical laboratories, microbiology still relies heavily on manual tasks, particularly the culture of samples on agar plates and their subsequent manual review for microorganism identification and an... read more 

TaWSA-WRN: a Taylor wave search optimized WideResNet framework for intrusion detection with response-aware mitigation in cloud computing.

Scientific reports May 12, 2026
Targeting cloud computing environments has become increasingly attractive to sophisticated cyberattackers due to their open, scalable, and distributed nature. Intrusion Detection Systems (IDSs) analyse traffic patterns to detect these attacks; deep l... read more 

Plasma acylcarnitine dysregulation associated with CPT1-mediated metabolism contributes to oral carcinogenesis.

Scientific reports May 12, 2026
Growing evidence suggests that lipid metabolic reprogramming occurs in oral cancer (OC). We characterized circulating metabolic alterations associated with lipid metabolic reprogramming in OC. Semi-targeted and targeted plasma metabolomic profiling w... read more 

Distribution-informed machine learning for flash flood susceptibility: integrating weibull extreme value theory with interpretable models.

Scientific reports May 12, 2026
Flash floods represent one of the deadliest weather-related hazards globally, yet their prediction remains fundamentally challenged by extreme class imbalance in observational data. This study addresses a critical methodological gap: traditional eval... read more 

NerveAI- a machine learning algorithm for detection of nerve pain in the head and neck.

Scientific reports May 12, 2026
Nerve pain screening in patients with headache disorders requires specialized clinical knowledge that is frequently not readily accessible at the initial point-of-care. This results in restricted access to early treatment, thereby increasing the risk... read more 

Development and Validation of a SHAP-Interpretable Machine Learning Model for Stroke Risk Prediction Using Circulating MicroRNA Biomarkers.

Journal of molecular neuroscience : MN May 12, 2026
BACKGROUND/OBJECTIVE: Stroke remains a leading cause of morbidity and mortality worldwide. Circulating microRNAs (miRNAs) have emerged as promising non-invasive biomarkers for cardiovascular disease diagnosis and risk stratification. However, their i... read more 

Performance and generalization analysis of machine learning, deep learning, and transformer models for histopathology image classification.

Scientific reports May 12, 2026
Histopathology image classification plays a critical role in computer-aided diagnosis by supporting pathologists in disease detection and grading. With the rapid advancement of artificial intelligence, a wide range of machine learning, deep learning,... read more 

Comprehensive annotation and analysis of human microproteins by human microprotein atlas platform.

Communications chemistry May 12, 2026
Small open reading frames (sORFs) and their encoded microproteins are increasingly recognized as a widespread but poorly characterized components of the human proteome. Here we show a Human Microprotein Atlas (HMPA) platform that integrates 617,462 h... read more 

Intelligent underwater mine detection using multi-backbone feature fusion with temporal attention-gated unit on side-scan sonar imaging.

Scientific reports May 12, 2026
Underwater mines pose a high risk to submarines and ships. As a result, navies across the globe employ mine countermeasure units to counter them. Among the measurements employed by MCM units is mine hunting, which involves examining every mine in a s... read more 

NeuroNetFusion: enhanced EEG abnormality classification via multi-network TF-IDF feature selection.

Scientific reports May 12, 2026
To address the inherent complexity and nonlinearity of electroencephalogram (EEG) signals, this study proposes a refined classification framework, NeuroNetFusion, which strategically integrates and selects multi-context network-based features for imp... read more 
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