AIMC Topic: Fundus Oculi
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Enhancing Choroidal Nevus Position Identification through CNN-Based Segmentation of Eye Fundus Images.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
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 pa...
An Automatic Method for Locating Positions and their Colors Important for Classifying Genders in Retinal Fundus Images by Deep Learning Models.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2024
This paper proposes an automatic method to identify important positions and their color features in retinal fundus images for gender classification using deep learning. The proposed method consists of MALCC (Model Analysis by Local Color Characterist...
Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images.
Translational vision science & technology
Jul 1, 2024
PURPOSE: The purpose of this study was to establish and validate a deep learning model to screen vascular aging using retinal fundus images. Although vascular aging is considered a novel cardiovascular risk factor, the assessment methods are currentl...
Innovative utilization of ultra-wide field fundus images and deep learning algorithms for screening high-risk posterior polar cataract.
Journal of cataract and refractive surgery
Jun 1, 2024
PURPOSE: To test a cataract shadow projection theory and validate it by developing a deep learning algorithm that enables automatic and stable posterior polar cataract (PPC) screening using fundus images.
AUTOMATED DETECTION OF VITRITIS USING ULTRAWIDE-FIELD FUNDUS PHOTOGRAPHS AND DEEP LEARNING.
Retina (Philadelphia, Pa.)
Jun 1, 2024
BACKGROUND/PURPOSE: Evaluate the performance of a deep learning algorithm for the automated detection and grading of vitritis on ultrawide-field imaging.
Convolutional Neural Network-Based Prediction of Axial Length Using Color Fundus Photography.
Translational vision science & technology
May 1, 2024
PURPOSE: To develop convolutional neural network (CNN)-based models for predicting the axial length (AL) using color fundus photography (CFP) and explore associated clinical and structural characteristics.
PallorMetrics: Software for Automatically Quantifying Optic Disc Pallor in Fundus Photographs, and Associations With Peripapillary RNFL Thickness.
Translational vision science & technology
May 1, 2024
PURPOSE: We sough to develop an automatic method of quantifying optic disc pallor in fundus photographs and determine associations with peripapillary retinal nerve fiber layer (pRNFL) thickness.
[Preliminary study on automatic quantification and grading of leopard spots fundus based on deep learning technology].
[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
Mar 11, 2024
To achieve automatic segmentation, quantification, and grading of different regions of leopard spots fundus (FT) using deep learning technology. The analysis includes exploring the correlation between novel quantitative indicators, leopard spot fund...
A novel lightweight deep learning approach for simultaneous optic cup and optic disc segmentation in glaucoma detection.
Mathematical biosciences and engineering : MBE
Mar 4, 2024
Glaucoma is a chronic neurodegenerative disease that can result in irreversible vision loss if not treated in its early stages. The cup-to-disc ratio is a key criterion for glaucoma screening and diagnosis, and it is determined by dividing the area o...
Deep Learning Detection of Early Retinal Peripheral Degeneration From Ultra-Widefield Fundus Photographs of Asymptomatic Young Adult (17-19 Years) Candidates to Airforce Cadets.
Translational vision science & technology
Feb 1, 2024
PURPOSE: Artificial intelligence (AI)-assisted ultra-widefield (UWF) fundus photographic interpretation is beneficial to improve the screening of fundus abnormalities. Therefore we constructed an AI machine-learning approach and performed preliminary...
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