AIMC Topic: Neural Networks, Computer

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Predicting Bioconcentration Factors of Per- and Polyfluoroalkyl Substances Using a Directed Message Passing Neural Network with Multimodal Feature Fusion.

Environmental science & technology
Amid growing concerns regarding the ecological risks posed by emerging contaminants, per- and polyfluoroalkyl substances (PFASs) present significant challenges for risk assessment due to their structural diversity and the paucity of experimental data...

Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence.

Brain : a journal of neurology
Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i.e. MRI negative) based on visual assessment by human experts. MRI-negative patients face diagnostic uncertaint...

A Comprehensive Exploration of Groundwater Quality of Ambagarh Chowki Region, Chhattisgarh, India: Water Quality Index, Health Risk, and ANN Predictive Modeling.

Water environment research : a research publication of the Water Environment Federation
Access to safe and clean drinking water remains a critical global challenge, with groundwater as a primary source for billions of people. Further, toxic contaminants increasingly threaten groundwater quality, posing significant health risks. This stu...

Predicting Pneumoconiosis Risk in Coal Workers using Artificial Neural Networks.

Puerto Rico health sciences journal
OBJECTIVE: This study aimed to create a model to predict pneumoconiosis risk in coal workers using artificial neural networks (ANNs).

A prognostic model of immunoglobulin A nephropathy using artificial neural network: a retrospective study based on integrated Chinese and Western Medicine.

Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan
OBJECTIVE: To establish and evaluate a prognostic model of immunoglobulin A nephropathy (IgAN) based on integrated Chinese and Western Medicine.

Development of Machine-Learning-Based Models for Detection of Cognitive Impairment in Patients Receiving Maintenance Hemodialysis.

European journal of neurology
BACKGROUND: Cognitive impairment is common but frequently undiagnosed in the dialysis population. We aimed to develop and validate a quick and accurate screening tool using machine-learning-based approaches in them.

Do Transformers and CNNs Learn Different Concepts of Brain Age?

Human brain mapping
"Predicted brain age" refers to a biomarker of structural brain health derived from machine learning analysis of T1-weighted brain magnetic resonance (MR) images. A range of machine learning methods have been used to predict brain age, with convoluti...

Brain Aging in Patients With Cardiovascular Disease From the UK Biobank.

Human brain mapping
The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the developm...

Multi-Objective Evolutionary Optimization Boosted Deep Neural Networks for Few-Shot Medical Segmentation With Noisy Labels.

IEEE journal of biomedical and health informatics
Fully-supervised deep neural networks have achieved remarkable progress in medical image segmentation, yet they heavily rely on extensive manually labeled data and exhibit inflexibility for unseen tasks. Few-shot segmentation (FSS) addresses these is...

CRT: A Convolutional Recurrent Transformer for Automatic Sleep State Detection.

IEEE journal of biomedical and health informatics
Sleep is a crucial period of rest necessary for optimal cognitive function, psychological well-being, and execution of everyday tasks. In the field of sleep healthcare, the primary objective is to identify and classify the various sleep states. Imple...