AIMC Topic: Anterior Chamber
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Deep learning-based normative database of anterior chamber dimensions for angle closure assessment: the Singapore Chinese Eye Study.
The British journal of ophthalmology
Mar 20, 2025
BACKGROUND/ AIMS: The lack of context for anterior segment optical coherence tomography (ASOCT) measurements impedes its clinical utility. We established the normative distribution of anterior chamber depth (ACD), area (ACA) and width (ACW) and lens ...
Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis.
Journal of glaucoma
May 16, 2024
PRCIS: In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6...
Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy.
Ultrasound in medicine & biology
Sep 18, 2023
OBJECTIVE: The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure...
A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs.
Computer methods and programs in biomedicine
Mar 6, 2022
BACKGROUND AND OBJECTIVES: Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach t...
Reproducibility of deep learning based scleral spur localisation and anterior chamber angle measurements from anterior segment optical coherence tomography images.
The British journal of ophthalmology
Jan 28, 2022
AIMS: To apply a deep learning model for automatic localisation of the scleral spur (SS) in anterior segment optical coherence tomography (AS-OCT) images and compare the reproducibility of anterior chamber angle (ACA) width between deep learning loca...
Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning.
BMC ophthalmology
Sep 22, 2021
BACKGROUND: The purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview anterior segment photographs.
Machine Learning-Guided Prediction of Central Anterior Chamber Depth Using Slit Lamp Images from a Portable Smartphone Device.
Biosensors
Jun 5, 2021
There is currently no objective portable screening modality for narrow angles in the community. In this prospective, single-centre image validation study, we used machine learning on slit lamp images taken with a portable smartphone device (MIDAS) to...
Classification Criteria for Multifocal Choroiditis With Panuveitis.
American journal of ophthalmology
May 11, 2021
PURPOSE: To determine classification criteria for multifocal choroiditis with panuveitis (MFCPU).
Artificial Intelligence, Machine Learning and Calculation of Intraocular Lens Power.
Klinische Monatsblatter fur Augenheilkunde
Nov 23, 2020
BACKGROUND AND PURPOSE: In the last decade, artificial intelligence and machine learning algorithms have been more and more established for the screening and detection of diseases and pathologies, as well as for describing interactions between measur...
Novel Artificial Intelligence-Based Quantification of Anterior Chamber Inflammation Using Vision Transformers.
Translational vision science & technology
May 1, 2025
PURPOSE: Quantitative assessment of inflammation is critical for the accurate diagnosis and effective management of uveitis. This study aims to introduce a novel three-dimensional vision transformer approach using anterior segment optical coherence t...
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