AIMC Topic: Diagnostic Techniques, Ophthalmological

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Deep Learning Approaches Predict Glaucomatous Visual Field Damage from OCT Optic Nerve Head En Face Images and Retinal Nerve Fiber Layer Thickness Maps.

Ophthalmology
PURPOSE: To develop and evaluate a deep learning system for differentiating between eyes with and without glaucomatous visual field damage (GVFD) and predicting the severity of GFVD from spectral domain OCT (SD OCT) optic nerve head images.

Force classification during robotic interventions through simulation-trained neural networks.

International journal of computer assisted radiology and surgery
PURPOSE: Intravitreal injection is among the most frequent treatment strategies for chronic ophthalmic diseases. The last decade has seen a serious increase in the number of intravitreal injections, and with it, adverse effects and drawbacks. To tack...

Direct Cup-to-Disc Ratio Estimation for Glaucoma Screening via Semi-Supervised Learning.

IEEE journal of biomedical and health informatics
Glaucoma is a chronic eye disease that leads to irreversible vision loss. The Cup-to-Disc Ratio (CDR) serves as the most important indicator for glaucoma screening and plays a significant role in clinical screening and early diagnosis of glaucoma. In...

Relative Afferent Pupillary Defect Screening Through Transfer Learning.

IEEE journal of biomedical and health informatics
Abnormalities in pupillary light reflex can indicate optic nerve disorders that may lead to permanent visual loss if not diagnosed in an early stage. In this study, we focus on relative afferent pupillary defect (RAPD), which is based on the differen...

Automatic Cataract Classification Using Deep Neural Network With Discrete State Transition.

IEEE transactions on medical imaging
Cataract is the clouding of lens, which affects vision and it is the leading cause of blindness in the world's population. Accurate and convenient cataract detection and cataract severity evaluation will improve the situation. Automatic cataract dete...

Promising Artificial Intelligence-Machine Learning-Deep Learning Algorithms in Ophthalmology.

Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
The lifestyle of modern society has changed significantly with the emergence of artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies in recent years. Artificial intelligence is a multidimensional technology with va...

Machine Learning in the Detection of the Glaucomatous Disc and Visual Field.

Seminars in ophthalmology
Glaucoma is the leading cause of irreversible blindness worldwide. Early detection is of utmost importance as there is abundant evidence that early treatment prevents disease progression, preserves vision, and improves patients' long-term quality of ...

Deep learning in ophthalmology: The technical and clinical considerations.

Progress in retinal and eye research
The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for ...

A Novel Weakly Supervised Multitask Architecture for Retinal Lesions Segmentation on Fundus Images.

IEEE transactions on medical imaging
Obtaining the complete segmentation map of retinal lesions is the first step toward an automated diagnosis tool for retinopathy that is interpretable in its decision-making. However, the limited availability of ground truth lesion detection maps at a...