AIMC Topic: Humans

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Screening core genes for minimal change disease based on bioinformatics and machine learning approaches.

International urology and nephrology
Based on bioinformatics and machine learning methods, we conducted a study to screen the core genes of minimal change disease (MCD) and further explore its pathogenesis. First, we obtained the chip data sets GSE108113 and GSE200828 from the Gene Expr...

Use of a Novel Artificial Intelligence Approach for a Faster and More Precise Computerized Facial Evaluation in Aesthetic Dentistry.

Journal of esthetic and restorative dentistry : official publication of the American Academy of Esthetic Dentistry ... [et al.]
INTRODUCTION: AI is based on automated learning algorithms that use large bodies of information (big data). In the field of dentistry, AI allows the analysis of radiographs, intraoral images and other clinical recordings with unprecedented precision ...

Federated learning-based prediction of depression among adolescents across multiple districts in China.

Journal of affective disorders
Depression in adolescents is a serious mental health condition that can affect their emotional and social well-being. Detailed understanding of depression patterns and status of depressive symptoms in adolescents could help identify early interventio...

Context-aware feature reconstruction for class-incremental anomaly detection and localization.

Neural networks : the official journal of the International Neural Network Society
With the development of deep learning, the unsupervised visual anomaly detection and localization task has gained significant attention in both academia and industry, where only normal data are used for training. Existing methods for this task typica...

Introducing high correlation and high quality instances for few-shot entity linking.

Neural networks : the official journal of the International Neural Network Society
Entity linking, the process of connecting textual mentions in documents to canonical entities within a knowledge base, plays an integral role in a myriad of natural language processing tasks. A significant challenge prevalent within the field is the ...

Knowledge-driven multi-graph convolutional network for brain network analysis and potential biomarker discovery.

Medical image analysis
In brain network analysis, individual-level data can provide biological features of individuals, while population-level data can provide demographic information of populations. However, existing methods mostly utilize either individual- or population...

Analysis of nailfold capillaroscopy images with artificial intelligence: Data from literature and performance of machine learning and deep learning from images acquired in the SCLEROCAP study.

Microvascular research
OBJECTIVE: To evaluate the performance of machine learning and then deep learning to detect a systemic scleroderma (SSc) landscape from the same set of nailfold capillaroscopy (NC) images from the French prospective multicenter observational study SC...

Acceptability of artificial intelligence in dental radiology among patients in India: are we ready for this revolution?

Oral radiology
OBJECTIVE: In recent times, artificial Intelligence (AI) has gained popularity in medical as well as dental radiology. Studies have been conducted among medical and dental students and professionals about the knowledge and understanding towards AI. T...

Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic Review.

Aesthetic plastic surgery
PURPOSE: This systematic review aims to assess the use of machine learning, deep learning, and artificial intelligence in aesthetic plastic surgery.

Rapid diagnosis and recurrence prediction of choledocholithiasis disease using raw bile with machine learning assisted SERS.

Talanta
Surface-enhanced Raman spectroscopy (SERS) analysis based on body fluids has been widely applied in disease diagnose. Choledocholithiasis is a widespread and often recurrent digestive system disease, with limited data on factors predicting its format...