Artificial Intelligence Medical Compendium

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

Showing 20,281 to 20,290 of 215,962 articles

Design and validation of renal stone detection using multi-architecture feature extraction with deep sequential learning model on axial computed tomography images.

Scientific reports May 12, 2026
Kidney stone disease is a significant public health threat, with its prevalence escalating due to evolving dietary habits, rising rates of obesity, other medical conditions, and the use of certain supplements. A kidney stone, otherwise known as a ren... read more 

MultiScaleKANNet: a hybrid CNN-KAN-transformer architecture for radiographic bone-loss risk stratification from knee X-rays.

Scientific reports May 12, 2026
Osteoporosis is underdiagnosed because dual-energy X-ray absorptiometry (DXA) is costly and scarce. We present MultiScaleKANNet, a hybrid deep-learning architecture for radiographic bone-loss risk stratification from routine knee X-rays, combining co... read more 

Human gloss perception reproduced by tiny neural networks.

Nature human behaviour May 12, 2026
A key goal of visual neuroscience is to explain how our brains infer object properties such as colour, curvature or gloss. Here we used machine learning to identify computations underlying human gloss judgements-traditionally considered a challenging... read more 

Deep convolutional models for robust multi-crop disease recognition in real-world conditions.

Scientific reports May 12, 2026
Crop diseases significantly reduce agricultural output and are a serious problem, especially in the parts of the world where diagnostic experts are not readily available. Deep learning has recently shown us that it is possible for a computer to ident... read more 

Systematic discovery of enzyme promiscuity in Escherichia coli using in vitro metabolomics.

Communications biology May 12, 2026
Metabolic enzymes have traditionally been regarded as highly specific catalysts; however, many can catalyze multiple reactions. To systematically investigate the prevalence of such enzyme promiscuity, we used nontargeted metabolomics to measure dynam... read more 

ULCYP: A Multitask Model for Predicting P450 Inducers Based on Positive-Unlabeled Learning.

Journal of chemical information and modeling May 12, 2026
Prediction of cytochrome P450 (CYP) induction is highly advantageous in early stage drug discovery, as it helps mitigate the risks of drug-drug interactions and toxicity. However, the development of specialized predictive models for CYP induction rem... read more 

Establishment of prognostic prediction model based on lipid metabolism related genes in esophageal squamous cell carcinoma by machine learning algorithms.

BMC gastroenterology May 12, 2026
BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a major cause of cancer-related mortality worldwide, with a high prevalence and poor prognosis in specific regions. Despite advancements in clinical care, the identification of reliable biomark... read more 

Multimodal predictions of end stage chronic kidney disease from asymptomatic individuals for discovery of genomic biomarkers.

BMC nephrology May 12, 2026
Chronic kidney disease (CKD) is a complex condition where the kidneys are damaged and progressively lose their ability to filter blood, 10% of the world population have the disease that often goes undetected until it is too late for intervention. Usi... read more 

A novel hybrid approach for microbial detection using deep ensemble learning.

BMC microbiology May 12, 2026
Microbes play a vital role in healthcare, clinical microbiology, biotechnology, and environmental health, making their accurate detection and classification essential in these fields. The accurate detection of bacteria enables investigators to distin... read more 

A comparative analysis in a clinical cohort: multiple imputation by chained equations and a novel super learner-based imputation approach.

BMC medical research methodology May 12, 2026
BACKGROUND: Missing data is a challenge in clinical research, especially in real-world data (RWD), where complete case analysis can bias results and reduce power. Ensemble learning approaches like Super Learner (SL) show strong numerical performance ... read more 
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