AIMC Topic: Male

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Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Expert review of anticancer therapy
BACKGROUND: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue...

Unraveling the neural dynamics of mathematical interference in english reading: A novel approach with deep learning and fNIRS data.

Brain research bulletin
English has emerged as the predominant global language, driving efforts to optimize its acquisition through interdisciplinary cognitive research. While behavioral studies suggest a link between English learning and mathematical cognition, the neural ...

Transforming the management of radiotherapy-induced hypothyroidism in nasopharyngeal carcinoma through an Innovative individualized radiation dosage model: A multicenter retrospective analysis.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: Current guidelines for thyroid radiation dose prescription lack uniformity and fail to consider the unique characteristics of individual patients. This study aimed to develop an individualized thyroid dosing regimen to enhance thyroid protec...

Augmenting Common Spatial Patterns to deep learning networks for improved alcoholism detection using EEG signals.

Computers in biology and medicine
One of the main risk factors for numerous health problems is excessive drinking. Alcoholism is a severe disorder that can affect a person's thinking and cognitive abilities. Early detection of alcoholism can help the subject regain control over their...

Development and validation of an interpretable machine learning model for predicting hyperuricemia risk: Based on environmental chemical exposure.

Ecotoxicology and environmental safety
Hyperuricemia is a global health concern, with environmental chemicals as risk factors. This study used data of multiple environmental chemical exposures from the 2011-2012 cycle of the National Health and Nutrition Examination Survey (NHANES) to dev...

Myocardial Infarction Detection using Variational Mode Decomposition with Fuzzy Weight Particle Swarm Optimization and Depthwise Separable Convolutional Network.

Computers in biology and medicine
The challenge of precisely recognizing myocardial infarction (MI) from electrocardiographic (ECG) readings stems from the complex nature of these signals.ECG data exhibit both nonlinear and non-stationary properties, making interpretation difficult. ...

Automatic transformer-based grading of multiple retinal inflammatory signs in uveitis on fluorescein angiography.

Computers in biology and medicine
BACKGROUND: Grading fluorescein angiography (FA) for uveitis is complex, often leading to the oversight of retinal inflammation in clinical studies. This study aims to develop an automated method for grading retinal inflammation.

Large medical image database impact on generalizability of synthetic CT scan generation.

Computers in biology and medicine
This study systematically examines the impact of training database size and the generalizability of deep learning models for synthetic medical image generation. Specifically, we employ a Cycle-Consistency Generative Adversarial Network (CycleGAN) wit...

A machine learning framework for cross-institute standardized analysis of flow cytometry in differentiating acute myeloid leukemia from non-neoplastic conditions.

Computers in biology and medicine
Flow cytometry (FC) remains a cornerstone diagnostic tool for acute myeloid leukemia (AML), yet standardizing panels across laboratories presents persistent challenges. Our study introduces a validated machine learning framework enabling cross-panel ...