AIMC Topic: Glaucoma

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Development of machine learning models for diagnosis of glaucoma.

PloS one
The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features fro...

A machine-learning graph-based approach for 3D segmentation of Bruch's membrane opening from glaucomatous SD-OCT volumes.

Medical image analysis
Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Meas...

Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
We present a novel method to segment retinal images using ensemble learning based convolutional neural network (CNN) architectures. An entropy sampling technique is used to select informative points thus reducing computational complexity while perfor...

A Hybrid Swarm Algorithm for optimizing glaucoma diagnosis.

Computers in biology and medicine
Glaucoma is among the most common causes of permanent blindness in human. Because the initial symptoms are not evident, mass screening would assist early diagnosis in the vast population. Such mass screening requires an automated diagnosis technique....

Deep learning-assisted 10-μL single droplet-based viscometry for human aqueous humor.

Biosensors & bioelectronics
Probing the viscosity of human aqueous humor is crucial for optimizing micro-tube shunts in glaucoma treatment. However, conventional viscometers are not suitable for aqueous humor due to the limited sample volume-only tens of microliters-that can be...

Robust Uncertainty-Informed Glaucoma Classification Under Data Shift.

Translational vision science & technology
PURPOSE: Standard deep learning (DL) models often suffer significant performance degradation on out-of-distribution (OOD) data, where test data differs from training data, a common challenge in medical imaging due to real-world variations.

A deep learning model integrating domain-specific features for enhanced glaucoma diagnosis.

BMC medical informatics and decision making
Glaucoma is a group of serious eye diseases that can cause incurable blindness. Despite the critical need for early detection, over 60% of cases remain undiagnosed, especially in less developed regions. Glaucoma diagnosis is a costly task and some mo...

Ethics of Artificial Intelligence in Medicine and Ophthalmology.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
BACKGROUND: This review explores the bioethical implementation of artificial intelligence (AI) in medicine and in ophthalmology. AI, which was first introduced in the 1950s, is defined as "the machine simulation of human mental reasoning, decision ma...

Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Deep learning algorithms as tools for automated image classification have recently experienced rapid growth in imaging-dependent medical specialties, including ophthalmology. However, only a few algorithms tailored to specific health conditions have ...

Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.

International journal of molecular sciences
Glaucoma is a leading cause of irreversible blindness, with challenges persisting in early diagnosis, disease progression, and surgical outcome prediction. Recent advances in artificial intelligence have enabled significant progress by extracting cli...