Enhancing Choroidal Nevus Position Identification through CNN-Based Segmentation of Eye Fundus Images.

Diagnosing choroidal nevus in color fundus images is challenging for clinicians not regularly practicing it. Machine learning (ML) has proven effective in detecting and analyzing such abnormalities with high accuracy and efficiencyThis research is part of a larger project to develop a decision support system for choroidal nevus diagnosis, focusing on creating a segmentation algorithm to identify key areas in color fundus images. The study evaluates and compares the efficacy of various convolutional neural network (CNN) segmentation models, a crucial step for improved image analysis accuracyFundus images from the Alberta Ocular Brachytherapy Program, including healthy and choroidal nevus-affected eyes, were used.

Conceptual Framework for Community-Based Prevention of Brown Dog Tick-Associated Rocky Mountain Spotted Fever.

Rocky Mountain spotted fever (RMSF) is a severe tickborne disease that can reach epidemic proportions in communities with certain social and ecologic risk factors. In some areas, the case-fatality rate of brown dog tick-associated RMSF is up to 50%. Because of the spread of brown dog tick-associated RMSF in the southwestern United States and northern Mexico, the disease has the potential to emerge and become endemic in other communities that have large populations of free-roaming dogs, brown dog ticks, limited resources, and low provider awareness of the disease.

Artificial intelligence in the classification and segmentation of fundus images with choroidal nevi.

Objective: The purpose of this study is to summarize the results from 3 experimental studies into the use of artificial intelligence to classify and segment colour fundus images with choroidal nevi.

Study Design: This study is based on a secondary analysis of colour fundus images taken of patients receiving usual clinical care from the Alberta Ocular Brachytherapy Program.

Methods: High-resolution colour fundus images were labeled by experienced ocular oncologists.

Advancing ocular oncology care through teleophthalmology: a patient-centric perspective.

Objective: Assessing patient satisfaction is an important component of understanding a program's efficacy and quality. The purpose of this study is to evaluate patient satisfaction with a teleophthalmology program tailored for ocular oncology that provides screening, remote assessment, care planning, and follow-up.

Study Design: Retrospective survey.

15-Gene Expression Profile and as Integrated Prognostic Test for Uveal Melanoma: First Report of Collaborative Ocular Oncology Group Study No. 2 (COOG2.1).

PURPOSEValidated and accurate prognostic testing is critical for precision medicine in uveal melanoma (UM). Our aims were to (1) prospectively validate an integrated prognostic classifier combining a 15-gene expression profile (15-GEP) and RNA expression and (2) identify clinical variables that enhance the prognostic accuracy of the 15-GEP/ classifier.MATERIALS AND METHODSThis study included 1,577 patients with UM of the choroid and/or ciliary body who were enrolled in the Collaborative Ocular Oncology Group Study Number 2 (COOG2) and prospectively monitored across 26 North American centers.