AIMC Topic: Corneal Opacity

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Artificial intelligence derived grading of mustard gas induced corneal injury and opacity.

Scientific reports
Artificial intelligence (AI) has emerged as a transformative tool in ophthalmology for disease diagnosis and prognosis. However, use of AI for assessing corneal damage due to chemical injury in live rabbits remains lacking. This study aimed to develo...

LASIK Versus PRK Based on Increased Risk of Corneal Haze: Assessing Current Decision-Making Capabilities of Six Artificial Intelligence Models in Refractive Surgery.

Journal of refractive surgery (Thorofare, N.J. : 1995)
PURPOSE: To investigate the current decision-making capabilities of 6 different artificial intelligence (AI) models by assessing their refractive surgery recommendations (laser in-situ keratomileusis [LASIK] or photorefractive keratectomy [PRK]) for ...

Determination of probability of causative pathogen in infectious keratitis using deep learning algorithm of slit-lamp images.

Scientific reports
Corneal opacities are important causes of blindness, and their major etiology is infectious keratitis. Slit-lamp examinations are commonly used to determine the causative pathogen; however, their diagnostic accuracy is low even for experienced ophtha...

MCOA: A Comprehensive Multimodal Dataset for Advancing Deep Learning in Corneal Opacity Assessment.

Scientific data
Corneal opacity remains a major global cause of vision impairment. Its severity is typically assessed subjectively by clinicians using slit lamp examinations of the anterior segment. While anterior segment optical coherence tomography (AS-OCT) provid...

[Classification Model of Corneal Opacity Based on Digital Image Features].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: According to the digital image features of corneal opacity, a multi classification model of support vector machine (SVM) was established to explore the objective quantification method of corneal opacity.