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Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.
JMIR Med Inform
January 2025
Department of Systems Design Engineering, Faculty of Engineering, University of Waterloo, Waterloo, ON, Canada.
Background: While expert optometrists tend to rely on a deep understanding of the disease and intuitive pattern recognition, those with less experience may depend more on extensive data, comparisons, and external guidance. Understanding these variations is important for developing artificial intelligence (AI) systems that can effectively support optometrists with varying degrees of experience and minimize decision inconsistencies.
Objective: The main objective of this study is to identify and analyze the variations in diagnostic decision-making approaches between novice and expert optometrists.
Objectives: To analyse and compare the functionality of extraluminal and intraluminal artificial urinary sphincters (AUSs), an in silico procedure has been defined and applied. Design and reliability assessments of the AUS are typically performed using a clinical approach, which does not provide data on mechanical stimulation of urethral tissues. Mechanical stimulation may determine tissue degeneration, such as urethral atrophy or erosion, the main causes of AUS failure.
View Article and Find Full Text PDFGlaucoma Detection and Feature Identification via GPT-4V Fundus Image Analysis.
Ophthalmol Sci
November 2024
Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, California.
Purpose: The aim is to assess GPT-4V's (OpenAI) diagnostic accuracy and its capability to identify glaucoma-related features compared to expert evaluations.
Design: Evaluation of multimodal large language models for reviewing fundus images in glaucoma.
Subjects: A total of 300 fundus images from 3 public datasets (ACRIMA, ORIGA, and RIM-One v3) that included 139 glaucomatous and 161 nonglaucomatous cases were analyzed.
EyeLiner: A Deep Learning Pipeline for Longitudinal Image Registration Using Fundus Landmarks.
Ophthalmol Sci
November 2024
Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colorado.
Objective: Detecting and measuring changes in longitudinal fundus imaging is key to monitoring disease progression in chronic ophthalmic diseases, such as glaucoma and macular degeneration. Clinicians assess changes in disease status by either independently reviewing or manually juxtaposing longitudinally acquired color fundus photos (CFPs). Distinguishing variations in image acquisition due to camera orientation, zoom, and exposure from true disease-related changes can be challenging.
View Article and Find Full Text PDFDeep Learning-Based Precontrast CT Parcellation for MRI-Free Brain Amyloid PET Quantification.
Clin Nucl Med
January 2025
Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
Purpose: This study aimed to develop a deep learning (DL) model for brain region parcellation using CT data from PET/CT scans to enable accurate amyloid quantification in 18F-FBB PET/CT without relying on high-resolution MRI.
Patients And Methods: A retrospective dataset of PET/CT and T1-weighted MRI pairs from 226 individuals (157 with mild cognitive impairment or dementia and 69 healthy controls) was used. The dataset was split into training/validation (60%) and test (40%) sets.
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