AIMC Topic: Diagnostic Techniques, Ophthalmological

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Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning.

IEEE transactions on bio-medical engineering
GOAL: Cataracts are a clouding of the lens and the leading cause of blindness worldwide. Assessing the presence and severity of cataracts is essential for diagnosis and progression monitoring, as well as to facilitate clinical research and management...

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 ...

AI for Corneal Imaging: How Will This Help Us Take Care of Our Patients?

Cornea
As artificial intelligence continues to evolve at a rapid pace, there is growing enthusiasm surrounding the potential for novel applications in corneal imaging. This article provides an overview of the potential for such applications, as well as the ...

[Challenges and prospects in the application of artificial intelligence for ocular disease screening and diagnosis].

[Zhonghua yan ke za zhi] Chinese journal of ophthalmology
In recent years, artificial intelligence (AI) technologies have experienced substantial growth across various sectors, with significant strides made particularly in medical AI through advancements such as large models. The application of AI within th...

Enhancing Meibography Image Analysis Through Artificial Intelligence-Driven Quantification and Standardization for Dry Eye Research.

Translational vision science & technology
PURPOSE: This study enhances Meibomian gland (MG) infrared image analysis in dry eye (DE) research through artificial intelligence (AI). It is comprised of two main stages: automated eyelid detection and tarsal plate segmentation to standardize meibo...

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

Translational vision science & technology
PURPOSE: To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations.

[Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis].

Vestnik oftalmologii
This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screenin...

AutoMorph: Automated Retinal Vascular Morphology Quantification Via a Deep Learning Pipeline.

Translational vision science & technology
PURPOSE: To externally validate a deep learning pipeline (AutoMorph) for automated analysis of retinal vascular morphology on fundus photographs. AutoMorph has been made publicly available, facilitating widespread research in ophthalmic and systemic ...

Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic dis...

Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management.

Current opinion in ophthalmology
PURPOSE OF REVIEW: The field of artificial intelligence has grown exponentially in recent years with new technology, methods, and applications emerging at a rapid rate. Many of these advancements have been used to improve the diagnosis and management...