Artificial Intelligence Medical Compendium

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

Showing 521 to 530 of 234,501 articles

Type 1 diabetes metabolomics and multi-omics: Current applications, limitations, and future directions - A narrative review.

Clinica chimica acta; international journal of clinical chemistry
Type 1 diabetes mellitus (T1DM) is an autoimmune disease characterized by immune-mediated destruction of pancreatic β-cells, resulting in absolute insulin deficiency and lifelong dependence on exogenous insulin. This disease, if not treated, leads to... read more 

Optical monitoring of inflammatory response in joint explant cultures using near infrared spectroscopy.

Osteoarthritis and cartilage
OBJECTIVE: To evaluate near-infrared (NIR) spectroscopy combined with machine learning (ML) for detecting and estimating inflammatory and matrix-related biomarkers released into the culture medium of an explant model. MATERIALS AND METHODS: Bovine os... read more 

Molecular characteristics and formation mechanisms of chlorinated disinfection byproducts from diatomaceous organic matter: Deciphering intracellular vs. extracellular contributions via FT-ICR MS and machine learning.

Environmental research
Diatom-derived algal organic matter (AOM), including intracellular organic matter (IOM) and extracellular organic matter (EOM), is an important precursor of disinfection byproducts (DBPs) in drinking water sources during diatom blooms. Clarifying the... read more 

Kinetics, radical chemistry and machine-learning-optimized abatement of capecitabine by UV/peroxymonosulfate: simultaneous micropollutant removal and attenuation of disinfection by-products formation potential.

Environmental research
As a widely used anticancer prodrug, capecitabine (CAPT) is poorly removed by conventional treatment and may generate toxic transformation products and disinfection by-products (DBPs). This study systematically evaluated CAPT abatement by UV/peroxymo... read more 

Revealing Spatiotemporal NOx Emission Dynamics in the Yangtze River Delta Using Physics-Informed Neural Networks and TROPOMI Observations.

Environmental research
Accurate estimation of nitrogen oxide (NOx) emissions is essential for characterizing their impacts on atmospheric environment and supporting air pollution management. This study introduces an innovative inversion framework based on a Physics-Informe... read more 

Spatiotemporal deep learning with visualization predicts fertilization for early rescue intracytoplasmic sperm injection.

F&S science
OBJECTIVE: To determine whether a deep learning (DL) model trained on time-lapse videos predicts fertilization following short-term insemination more accurately than senior embryologists, particularly in morphologically difficult cases, to visualize ... read more 

Uncovering Spatially Heterogeneous Predictors of Reported Malaria Cases in the Ashanti Region of Ghana: A Triangulated Framework Integrating Correlation Analysis, Random Forest, and Multiscale Geographically Weighted Regression.

Acta tropica
INTRODUCTION: Malaria remains a major cause of illness and death in sub-Saharan Africa. In 2023, WHO reported more than 263 million cases and 597,000 deaths globally. Ghana records a substantial malaria burden, and the Ashanti Region is among the reg... read more 

Oral squamous cell carcinoma: A Modern Review of Pathogenesis and Treatment.

The international journal of biochemistry & cell biology
Oral cancer (OC), predominantly oral squamous cell carcinoma (OSCC), is a serious global health burden with high mortality rates, especially in South and Southeast Asia. OC is a severe worldwide health issue that is mostly caused by a sedentary lifes... read more 

Feasibility of AI-based Automated Identification of Sacral Anatomy and Lead Features During Sacral Neuromodulation Procedures.

Urology
OBJECTIVE: To evaluate the ability of artificial intelligence-based modeling to identify pertinent sacral anatomy and lead characteristics on fluoroscopic imaging. METHODS: An expert practitioner manually reviewed and segmented image pairs from sacra... read more 

AI-integrated iPSC multi-omics: advancing phenotype discovery and drug target identification in rare diseases.

Drug discovery today
Rare diseases represent a major unmet medical need, yet the development of therapeutics for these diseases is limited by small patient cohorts, fragmented data, and uncertain disease mechanisms. Recent advances that combine patient-derived induced pl... read more