AIMC Topic: Female

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Automated Breast Density Assessment for Full-Field Digital Mammography and Digital Breast Tomosynthesis.

Cancer prevention research (Philadelphia, Pa.)
Mammographic density is a strong risk factor for breast cancer and is reported clinically as part of Breast Imaging Reporting and Data System (BI-RADS) results issued by radiologists. Automated assessment of density is needed that can be used for bot...

A novel machine learning-based cancer-specific cardiovascular disease risk score among patients with breast, colorectal, or lung cancer.

JNCI cancer spectrum
BACKGROUND: Cancer patients have up to a 3-fold higher risk for cardiovascular disease (CVD) than the general population. Traditional CVD risk scores may be less accurate for them. We aimed to develop cancer-specific CVD risk scores and compare them ...

Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.

Investigative ophthalmology & visual science
PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) has emerged as a novel approach in corneal transplantation over the past two decades. This study aims to identify predisposing risk factors for post-DMEK ocular hypertension (OHT) and develop...

Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.

Explainable Deep Learning for Glaucomatous Visual Field Prediction: Artifact Correction Enhances Transformer Models.

Translational vision science & technology
PURPOSE: The purpose of this study was to develop a deep learning approach that restores artifact-laden optical coherence tomography (OCT) scans and predicts functional loss on the 24-2 Humphrey Visual Field (HVF) test.

Performance on Activities of Daily Living and User Experience When Using Artificial Intelligence by Individuals With Vision Impairment.

Translational vision science & technology
PURPOSE: This study assessed objective performance, usability, and acceptance of artificial intelligence (AI) by people with vision impairment. The goal was to provide evidence-based data to enhance technology selection for people with vision loss (P...

The Associations Between Myopia and Fundus Tessellation in School Children: A Comparative Analysis of Macular and Peripapillary Regions Using Deep Learning.

Translational vision science & technology
PURPOSE: To evaluate the refractive differences among school-aged children with macular or peripapillary fundus tessellation (FT) distribution patterns, using fundus tessellation density (FTD) quantified by deep learning (DL) technology.

Benchmarking the speed-accuracy tradeoff in object recognition by humans and neural networks.

Journal of vision
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational mod...

Deep Learning-Based SD-OCT Layer Segmentation Quantifies Outer Retina Changes in Patients With Biallelic RPE65 Mutations Undergoing Gene Therapy.

Investigative ophthalmology & visual science
PURPOSE: To quantify outer retina structural changes and define novel biomarkers of inherited retinal degeneration associated with biallelic mutations in RPE65 (RPE65-IRD) in patients before and after subretinal gene augmentation therapy with voretig...

Modeling of injury severity of distracted driving accident using statistical and machine learning models.

PloS one
Distracted Driving (DD) is one of the global causes of high mortality and fatality in road traffic accidents. The increase in the number of distracted driving accidents (DDAs) is one of the concerns among transportation communities. The present study...