AIMC Topic: Neural Networks, Computer

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Utilizing 12-lead electrocardiogram and machine learning to retrospectively estimate and prospectively predict atrial fibrillation and stroke risk.

Computers in biology and medicine
BACKGROUND: The stroke risk in patients with subclinical atrial fibrillation (AF) is underestimated. By identifying patients at high risk of embolic stroke, health-care professionals can make more informed decisions regarding anticoagulation treatmen...

Compact Assessment of Molecular Surface Complementarities Enhances Neural Network-Aided Prediction of Key Binding Residues.

Journal of chemical information and modeling
Predicting interactions between proteins is fundamental for understanding the mechanisms underlying cellular processes, since protein-protein complexes are crucial in physiological conditions but also in many diseases, for example by seeding aggregat...

Genetic association studies using disease liabilities from deep neural networks.

American journal of human genetics
The case-control study is a widely used method for investigating the genetic underpinnings of binary traits. However, long-term, prospective cohort studies often grapple with absent or evolving health-related outcomes. Here, we propose two methods, l...

Explainable paroxysmal atrial fibrillation diagnosis using an artificial intelligence-enabled electrocardiogram.

The Korean journal of internal medicine
BACKGROUND/AIMS: Atrial fibrillation (AF) significantly contributes to global morbidity and mortality. Paroxysmal atrial fibrillation (PAF) is particularly common among patients with cryptogenic strokes or transient ischemic attacks and has a silent ...

Deep learning-based surrogates for multi-objective optimization of the groundwater abstraction schemes to manage seawater intrusion into coastal aquifers.

Journal of environmental management
Efficient optimization of pumping systems is crucial for managing salinity intrusion and ensuring groundwater sustainability in coastal aquifers. Surrogate models (SMs) are widely used in aquifer management as efficient alternatives to complex ground...

Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case-control study.

BMC medicine
BACKGROUND: Early detection of cancer can help patients with more effective treatments and result in better prognosis. Unfortunately, established cancer screening technologies are limited for use, especially for multi-cancer early detection. In this ...

(DA-U)Net: double attention UNet for retinal vessel segmentation.

BMC ophthalmology
BACKGROUND: Morphological changes in the retina are crucial and serve as valuable references in the clinical diagnosis of ophthalmic and cardiovascular diseases. However, the retinal vascular structure is complex, making manual segmentation time-cons...

Predicting sleep quality among college students during COVID-19 lockdown using a LASSO-based neural network model.

BMC public health
BACKGROUND: In March 2022, a new outbreak of COVID-19 emerged in Quanzhou, leading to the implementation of strict lockdown management measures in colleges. While existing research has indicated that the pandemic has had a significant impact on sleep...

Generalizable deep neural networks for image quality classification of cervical images.

Scientific reports
Successful translation of artificial intelligence (AI) models into clinical practice, across clinical domains, is frequently hindered by the lack of image quality control. Diagnostic models are often trained on images with no denotation of image qual...

Explainable label guided lightweight network with axial transformer encoder for early detection of oral cancer.

Scientific reports
Oral cavity cancer exhibits high morbidity and mortality rates. Therefore, it is essential to diagnose the disease at an early stage. Machine learning and convolution neural networks (CNN) are powerful tools for diagnosing mouth and oral cancer. In t...