AIMC Topic: Female

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A new method for internal urinary metabolite exposure and dietary exposure association assessment of 3-MCPD and glycidol and their esters based on machine learning.

Ecotoxicology and environmental safety
3-Monochloropropane-1,2-diol (3-MCPD) and glycidol along with their esters are commonly found in chemical production, wastewater treatment, food processing, and exhibit toxicity. Accurate exposure assessment is essential for evaluating the environmen...

Two-stage ensemble learning framework for automated classification of keratoconus severity.

Computers in biology and medicine
BACKGROUND AND OBJECTIVE: Accurate staging of keratoconus (KC) is crucial for timely intervention and improving patient quality of life. Unlike prior studies that relied on traditional base machine learning (ML) models, this paper proposes a more adv...

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,...

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...

High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.

Radiology. Artificial intelligence
Purpose To develop and evaluate an open-source deep learning model for detection and localization of breast cancer on MRI scans. Materials and Methods In this retrospective study, a deep learning model for breast cancer detection and localization was...

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.

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...