AIMC Topic: Humans

Clear Filters Showing 18631 to 18640 of 95995 articles

Ki-67 evaluation using deep-learning model-assisted digital image analysis in breast cancer.

Histopathology
AIMS: To test the efficacy of artificial intelligence (AI)-assisted Ki-67 digital image analysis in invasive breast carcinoma (IBC) with quantitative assessment of AI model performance.

Optimization of grinding parameters in robotic-assisted preparation of cracked teeth based on fracture mechanics: FEA and experiment.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: If left untreated, cracked teeth can lead to tooth loss, of which the incidence is 70%. Dental preparation is an effective treatment, but it is difficult to meet the clinical requirements when traditionally prepared by dent...

A general adaptive unsupervised feature selection with auto-weighting.

Neural networks : the official journal of the International Neural Network Society
Feature selection (FS) is essential in machine learning and data mining as it makes handling high-dimensional data more efficient and reliable. More attention has been paid to unsupervised feature selection (UFS) due to the extra resources required t...

Negative-Free Self-Supervised Gaussian Embedding of Graphs.

Neural networks : the official journal of the International Neural Network Society
Graph Contrastive Learning (GCL) has recently emerged as a promising graph self-supervised learning framework for learning discriminative node representations without labels. The widely adopted objective function of GCL benefits from two key properti...

Physiological model-based machine learning for classifying patients with binge-eating disorder (BED) from the Oral Glucose Tolerance Test (OGTT) curve.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Binge eating disorder (BED) is the most frequent eating disorder, often confused with obesity, with which it shares several characteristics. Early identification could enable targeted therapeutic interventions. In this study...

Expert opinion elicitation for assisting deep learning based Lyme disease classifier with patient data.

International journal of medical informatics
BACKGROUND: Diagnosing erythema migrans (EM) skin lesion, the most common early symptom of Lyme disease, using deep learning techniques can be effective to prevent long-term complications. Existing works on deep learning based EM recognition only uti...

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

BioFactors (Oxford, England)
Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heightened risk of metastasis. Consequently, the timely identification of biomarkers associated with lymph n...

Active Machine Learning for Pre-procedural Prediction of Time-Varying Boundary Condition After Fontan Procedure Using Generative Adversarial Networks.

Annals of biomedical engineering
The Fontan procedure is the definitive palliation for pediatric patients born with single ventricles. Surgical planning for the Fontan procedure has emerged as a promising vehicle toward optimizing outcomes, where pre-operative measurements are used ...

AlzyFinder: A Machine-Learning-Driven Platform for Ligand-Based Virtual Screening and Network Pharmacology.

Journal of chemical information and modeling
Alzheimer's disease (AD), a prevalent neurodegenerative disorder, presents significant challenges in drug development due to its multifactorial nature and complex pathophysiology. The AlzyFinder Platform, introduced in this study, addresses these cha...

AI-empowered visualization of nucleic acid testing.

Life sciences
AIMS: The visualization of nucleic acid testing (NAT) results plays a critical role in diagnosing and monitoring infectious and genetic diseases. The review aims to review the current status of AI-based NAT result visualization. It systematically int...