AIMC Topic: Glaucoma

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The role of saliency maps in enhancing ophthalmologists' trust in artificial intelligence models.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
PURPOSE: Saliency maps (SM) allow clinicians to better understand the opaque decision-making process in artificial intelligence (AI) models by visualising the important features responsible for predictions. This ultimately improves interpretability a...

Developing a privacy-preserving deep learning model for glaucoma detection: a multicentre study with federated learning.

The British journal of ophthalmology
BACKGROUND: Deep learning (DL) is promising to detect glaucoma. However, patients' privacy and data security are major concerns when pooling all data for model development. We developed a privacy-preserving DL model using the federated learning (FL) ...

Prediction of visual field progression with serial optic disc photographs using deep learning.

The British journal of ophthalmology
AIM: We tested the hypothesis that visual field (VF) progression can be predicted with a deep learning model based on longitudinal pairs of optic disc photographs (ODP) acquired at earlier time points during follow-up.

Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization.

IEEE transactions on medical imaging
Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are ava...

Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma.

BMC ophthalmology
BACKGROUND: Ahmed valve implantation demonstrated an increasing proportion in glaucoma surgery, but predicting the successful maintenance of target intraocular pressure remains a challenging task. This study aimed to evaluate the performance of machi...

Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection.

Scientific reports
Glaucoma is a progressive neurodegenerative disease characterized by the gradual degeneration of retinal ganglion cells, leading to irreversible blindness worldwide. Therefore, timely and accurate diagnosis of glaucoma is crucial, enabling early inte...

A hybrid framework for glaucoma detection through federated machine learning and deep learning models.

BMC medical informatics and decision making
BACKGROUND: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (...

Breaking Barriers in Behavioral Change: The Potential of Artificial Intelligence-Driven Motivational Interviewing.

Journal of glaucoma
Patient outcomes in ophthalmology are greatly influenced by adherence and patient participation, which can be particularly challenging in diseases like glaucoma, where medication regimens can be complex. A well-studied and evidence-based intervention...

Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network.

F1000Research
BACKGROUND: Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus ...