AIMC Topic: Male

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Alterations in the functional MRI-based temporal brain organisation in individuals with obesity.

Diabetes, obesity & metabolism
AIMS: Obesity is associated with functional alterations in the brain. Although spatial organisation changes in the brains of individuals with obesity have been widely studied, the temporal dynamics in their brains remain poorly understood. Therefore,...

Pose estimation for pickleball players' kinematic analysis through MediaPipe-based deep learning: A pilot study.

Journal of sports sciences
Pickleball has gained popularity across diverse age groups. This sport has particular balls that require different hitting styles, like hitting dinks. This study focuses on introducing pickleball players' kinematic analysis through a MediaPipe-based ...

From Faster Frames to Flawless Focus: Deep Learning HASTE in Postoperative Single Sequence MRI.

Academic radiology
BACKGROUND: This study evaluates the feasibility of a novel deep learning-accelerated half-fourier single-shot turbo spin-echo sequence (HASTE-DL) compared to the conventional HASTE sequence (HASTE) in postoperative single-sequence MRI for the detect...

Integrating multi-source data for skin burn classification using deep learning.

Computers in biology and medicine
BACKGROUND: Skin burns result from thermal or chemical damage to the skin, requiring timely and accurate assessment for effective treatment. Determining the degree of burns is crucial for appropriate clinical decisions, especially for interventions l...

Trends in Female Authorship at American Academy of Otolaryngology-HNS Annual Meetings From 2007 to 2022.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
OBJECTIVE: This study aims to trends in female authorship in poster and oral presentations at American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) annual meetings.

MRI Radiomics and Automated Habitat Analysis Enhance Machine Learning Prediction of Bone Metastasis and High-Grade Gleason Scores in Prostate Cancer.

Academic radiology
RATIONALE AND OBJECTIVES: To explore the value of machine learning models based on MRI radiomics and automated habitat analysis in predicting bone metastasis and high-grade pathological Gleason scores in prostate cancer.

Interpretable machine learning insights into the association between PFAS exposure and diabetes mellitus.

Ecotoxicology and environmental safety
BACKGROUND: Diabetes Mellitus (DM) is a global health concern with rising prevalence, and its link to PFAS exposure remains unclear. No machine learning (ML) models have yet been developed to predict DM based on PFAS exposure.

Ensemble-based Convolutional Neural Networks for brain tumor classification in MRI: Enhancing accuracy and interpretability using explainable AI.

Computers in biology and medicine
BACKGROUND: Accurate and efficient classification of brain tumors, including gliomas, meningiomas, and pituitary adenomas, is critical for early diagnosis and treatment planning. Magnetic resonance imaging (MRI) is a key diagnostic tool, and deep lea...

Refining cardiac segmentation from MRI volumes with CT labels for fine anatomy of the ascending aorta.

Radiological physics and technology
Magnetic resonance imaging (MRI) is time-consuming, posing challenges in capturing clear images of moving organs, such as cardiac structures, including complex structures such as the Valsalva sinus. This study evaluates a computed tomography (CT)-gui...