AI Medical Compendium Journal:
Computers in biology and medicine

Showing 91 to 100 of 1779 articles

Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

Computers in biology and medicine
The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting in inconsistent and imprecise evaluations. To address this issue and facilitate early intervention, t...

FedSynthCT-Brain: A federated learning framework for multi-institutional brain MRI-to-CT synthesis.

Computers in biology and medicine
The generation of Synthetic Computed Tomography (sCT) images has become a pivotal methodology in modern clinical practice, particularly in the context of Radiotherapy (RT) treatment planning. The use of sCT enables the calculation of doses, pushing t...

Prognostication of zooplankton-driven cholera pathoepidemiological Dynamics: Novel Bayesian-regularized deep NARX neuroarchitecture.

Computers in biology and medicine
BACKGROUND: Cholera outbreaks pose significant health concerns, particularly through freshwater contamination through zooplankton serving as reservoirs for Vibrio Cholerae. Understanding these complex interactions within the aquatic ecosystem through...

Deep transfer learning-based decoder calibration for intracortical brain-machine interfaces.

Computers in biology and medicine
Intracortical brain-machine interfaces (iBMIs) aim to establish a communication path between the brain and external devices. However, in the daily use of iBMIs, the non-stationarity of recorded neural signals necessitates frequent recalibration of th...

Enhancing robustness and generalization in microbiological few-shot detection through synthetic data generation and contrastive learning.

Computers in biology and medicine
In many medical and pharmaceutical processes, continuous hygiene monitoring is crucial, often involving the manual detection of microorganisms in agar dishes by qualified personnel. Although deep learning methods hold promise for automating this task...

Joint high-resolution feature learning and vessel-shape aware convolutions for efficient vessel segmentation.

Computers in biology and medicine
Clear imagery of retinal vessels is one of the critical shreds of evidence in specific disease diagnosis and evaluation, including sophisticated hierarchical topology and plentiful-and-intensive capillaries. In this work, we propose a new topology- a...

Automated engineered-stone silicosis screening and staging using Deep Learning with X-rays.

Computers in biology and medicine
Silicosis, a debilitating occupational lung disease caused by inhaling crystalline silica, continues to be a significant global health issue, especially with the increasing use of engineered stone (ES) surfaces containing high silica content. Traditi...

Towards fast and reliable estimations of 3D pressure, velocity and wall shear stress in aortic blood flow: CFD-based machine learning approach.

Computers in biology and medicine
In this work, we developed deep neural networks for the fast and comprehensive estimation of the most salient features of aortic blood flow. These features include velocity magnitude and direction, 3D pressure, and wall shear stress. Starting from 40...

EffiViT: Hybrid CNN-Transformer for Retinal Imaging.

Computers in biology and medicine
The human eye is a vital sensory organ that is crucial for visual perception. The retina is the main component of the eye and is responsible for visual signals. Due to its characteristics, the retina can reveal the occurrence of ocular diseases. So, ...

Attention in surgical phase recognition for endoscopic pituitary surgery: Insights from real-world data.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Surgical Phase Recognition systems are used to support the automated documentation of a procedure and to provide the surgical team with real-time feedback, potentially improving surgical outcome and reducing adverse events. ...