Artificial Intelligence Medical Compendium

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

Showing 861 to 870 of 160,243 articles

Multi-transcriptomics predicts clinical outcome in systemically untreated breast cancer patients with extensive follow-up.

Breast cancer research : BCR
BACKGROUND: Prognostic tools for determining patients with indolent breast cancers (BCs) are far from optimal, leading to extensive overtreatment. Several studies have demonstrated mRNAs, lncRNAs and miRNAs to have prognostic potential in BC. Because... read more 

Machine learning techniques for lipid nanoparticle formulation.

Nano convergence
A significant amount of effort has been poured into optimizing the delivery system that is demanded by novel therapeutic modalities. Lipid nanoparticle presents as a solution to transfect cells safely and efficiently with nucleic acid-based therapeut... read more 

Combined Study of Behavior and Spike Discharges Associated with Negative Emotions in Mice.

Neuroscience bulletin
In modern society, people are increasingly exposed to chronic stress, leading to various mental disorders. However, the activities of brain regions, especially neural firing patterns related to specific behaviors, remain unclear. In this study, we in... read more 

BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction.

BMC biology
BACKGROUND: Diabetes is a global metabolic disease that urgently calls for the development of new and effective therapeutic agents. Anti-diabetic peptides (ADPs) have emerged as a research hotspot due to their therapeutic potential and natural safety... read more 

Artificial Intelligence Enhances Diagnostic Accuracy of Contrast Enemas in Hirschsprung Disease Compared to Clinical Experts.

European journal of pediatric surgery : official journal of Austrian Association of Pediatric Surgery ... [et al] = Zeitschrift fur Kinderchirurgie
Contrast enema (CE) is widely used in the evaluation of suspected Hirschsprung disease (HD). Deep learning is a promising tool to standardize image assessment and support clinical decision-making. This study assesses the diagnostic performance of a d... read more 

Evaluation of Artificial Intelligence-based diagnosis for facial fractures, advantages compared with conventional imaging diagnosis: a systematic review and meta-analysis.

BMC musculoskeletal disorders
BACKGROUND: Currently, the application of convolutional neural networks (CNNs) in artificial intelligence (AI) for medical imaging diagnosis has emerged as a highly promising tool. In particular, AI-assisted diagnosis holds significant potential for ... read more 

Predictive modeling for step II therapy response in periodontitis - model development and validation.

NPJ digital medicine
Steps I and II periodontal therapy is the first-line treatment for periodontal disease, but has varying success. This study aimed to develop machine learning models to predict changes in periodontal probing depth (PPD) after step II therapy using pat... read more 

A comparative study and simple baseline for travel time prediction.

Scientific reports
Accurate travel time prediction (TTP) is essential to freeway users, including drivers, administrators, and freight-related companies, for enabling them to plan trips effectively and mitigate traffic congestion. However, TTP is a complex challenge ev... 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 

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