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Diagnostic Techniques, Ophthalmological

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An Efficient Deep Learning Approach to Automatic Glaucoma Detection Using Optic Disc and Optic Cup Localization.

Sensors (Basel, Switzerland)
Glaucoma is an eye disease initiated due to excessive intraocular pressure inside it and caused complete sightlessness at its progressed stage. Whereas timely glaucoma screening-based treatment can save the patient from complete vision loss. Accurate...

Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression.

Ophthalmology. Glaucoma
On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmi...

Joint Optimization of CycleGAN and CNN Classifier for Detection and Localization of Retinal Pathologies on Color Fundus Photographs.

IEEE journal of biomedical and health informatics
Retinal related diseases are the leading cause of vision loss, and severe retinal lesion causes irreversible damage to vision. Therefore, the automatic methods for retinal diseases detection based on medical images is essential for timely treatment. ...

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...

Deep Learning Ensemble Method for Classifying Glaucoma Stages Using Fundus Photographs and Convolutional Neural Networks.

Current eye research
: This study developed and evaluated a deep learning ensemble method to automatically grade the stages of glaucoma depending on its severity.: After cross-validation of three glaucoma specialists, the final dataset comprised of 3,460 fundus photograp...

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...

Applications of deep learning in detection of glaucoma: A systematic review.

European journal of ophthalmology
Glaucoma is the leading cause of irreversible blindness and disability worldwide. Nevertheless, the majority of patients do not know they have the disease and detection of glaucoma progression using standard technology remains a challenge in clinical...

Clinically Verified Hybrid Deep Learning System for Retinal Ganglion Cells Aware Grading of Glaucomatous Progression.

IEEE transactions on bio-medical engineering
OBJECTIVE: Glaucoma is the second leading cause of blindness worldwide. Glaucomatous progression can be easily monitored by analyzing the degeneration of retinal ganglion cells (RGCs). Many researchers have screened glaucoma by measuring cup-to-disc ...

Joint optic disc and optic cup segmentation based on boundary prior and adversarial learning.

International journal of computer assisted radiology and surgery
PURPOSE: The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise segmentation of the optic cup (OC) and optic disc (OD). In recent years, convolution neural network...