AIMC Topic: Glaucoma

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An Artificial Intelligence Driven Approach for Classification of Ophthalmic Images using Convolutional Neural Network: An Experimental Study.

Current medical imaging
BACKGROUND: Early disease detection is emphasized within ophthalmology now more than ever, and as a result, clinicians and innovators turn to deep learning to expedite accurate diagnosis and mitigate treatment delay. Efforts concentrate on the creati...

Deep Learning-based Glaucoma Detection Using CNN and Digital Fundus Images: A Promising Approach for Precise Diagnosis.

Current medical imaging
BACKGROUND: Glaucoma is a significant cause of irreversible blindness worldwide, with symptoms often going undetected until the patient's visual field starts shrinking.

Identifying Hub Genes for Glaucoma based on Bulk RNA Sequencing Data and Multi-machine Learning Models.

Current medicinal chemistry
AIMS: The aims of this study were to determine hub genes in glaucoma through multiple machine learning algorithms.

Artifact Correction in Retinal Nerve Fiber Layer Thickness Maps Using Deep Learning and Its Clinical Utility in Glaucoma.

Translational vision science & technology
PURPOSE: Correcting retinal nerve fiber layer thickness (RNFLT) artifacts in glaucoma with deep learning and evaluate its clinical usefulness.

Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data.

Translational vision science & technology
PURPOSE: This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of real-world data (RWD) for glaucoma diagnosis.

Improving the Accuracy and Speed of Visual Field Testing in Glaucoma With Structural Information and Deep Learning.

Translational vision science & technology
PURPOSE: To assess the performance of a perimetric strategy using structure-function predictions from a deep learning (DL) model.

Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma.

Indian journal of ophthalmology
PURPOSE: To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore...

3-LbNets: Tri-Labeling Deep Convolutional Neural Network for the Automated Screening of Glaucoma, Glaucoma Suspect, and No Glaucoma in Fundus Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early detection of glaucoma, a widespread visual disease, can prevent vision loss. Unfortunately, ophthalmologists are scarce and clinical diagnosis requires much time and cost. Therefore, we developed a screening Tri-Labeling deep convolutional neur...

AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons.

Translational vision science & technology
PURPOSE: Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here...