Ophthalmology

Diagnosis of Glaucoma on Retinal Fundus Images Using Deep Learning: Detection of Nerve Fiber Layer Defect and Optic Disc Analysis.

Early detection of glaucoma is important to slow down progression of the disease and to prevent total vision loss. Retinal fundus photography is frequently obtained for various eye disease diagnosis and record and is a suitable screening exam for its simplicity and low cost. However, the number of ophthalmologists who are specialized in glaucoma diagnosis is limited. We have been studying automate...

Deep Learning Techniques for Diabetic Retinopathy Detection.

Diabetes occurs due to the excess of glucose in the blood that may affect many organs of the body. Elevated blood sugar in the body causes many problems including Diabetic Retinopathy (DR). DR occurs due to the mutilation of the blood vessels in the retina. The manual detection of DR by ophthalmologists is complicated and time-consuming. Therefore, automatic detection is required, and recently dif...

Ethical Issues Raised by the Introduction of Artificial Companions to Older Adults with Cognitive Impairment: A Call for Interdisciplinary Collaborations.

Due to the high costs of providing long-term care to older adults with cognitive impairment, artificial companions are increasingly considered as a co...

Detection of Disease-Specific Parent Cells Via Distinct Population of Nano-Vesicles by Machine Learning.

BACKGROUND: The diagnosis and prognosis of pathological conditions, such as age-related macular degeneration (AMD) and cancer still need improvement. ...

Application of Artificial Intelligence in Targeting Retinal Diseases.

Retinal diseases affect an increasing number of patients worldwide because of the aging population. Request for diagnostic imaging in ophthalmology is...

Iatrogenic Cushing's syndrome as a consequence of nasal use of Betamethasone spray during pregnancy.

INTRODUCTION: Glucocorticoids (GC) are largely used for their anti-inflammatory and immunosuppressive effects. Until recently "local" administration (...

[Assistant diagnose for subclinical keratoconus by artificial intelligence].

To investigate the diagnosis of normal cornea, subclinical keratoconus and keratoconus by artifical intelligence. Diagnostic study. From January 201...

OPTICAL COHERENCE TOMOGRAPHY BIOMARKERS TO DISTINGUISH DIABETIC MACULAR EDEMA FROM PSEUDOPHAKIC CYSTOID MACULAR EDEMA USING MACHINE LEARNING ALGORITHMS.

PURPOSE: In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, identifying the underlying pathology can be challengi...

Evaluation of a Deep Learning System For Identifying Glaucomatous Optic Neuropathy Based on Color Fundus Photographs.

PRECIS: Pegasus outperformed 5 of the 6 ophthalmologists in terms of diagnostic performance, and there was no statistically significant difference bet...

Finding Glaucoma in Color Fundus Photographs Using Deep Learning.

Accuracy of Kalman Filtering in Forecasting Visual Field and Intraocular Pressure Trajectory in Patients With Ocular Hypertension.

IMPORTANCE: Techniques that properly identify patients in whom ocular hypertension (OHTN) is likely to progress to open-angle glaucoma can assist clin...

Network physiology in insomnia patients: Assessment of relevant changes in network topology with interpretable machine learning models.

Network physiology describes the human body as a complex network of interacting organ systems. It has been applied successfully to determine topologic...

Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection.

INTRODUCTION: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic pat...

Can a Machine Learn from Radiologists' Visual Search Behaviour and Their Interpretation of Mammograms-a Deep-Learning Study.

Visual search behaviour and the interpretation of mammograms have been studied for errors in breast cancer detection. We aim to ascertain whether mach...

Artificial intelligence for pediatric ophthalmology.

PURPOSE OF REVIEW: Despite the impressive results of recent artificial intelligence applications to general ophthalmology, comparatively less progress...

The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network.

Recent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they rela...

Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.

Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to ha...

Lens Identification to Prevent Radiation-Induced Cataracts Using Convolutional Neural Networks.

Exposure of the lenses to direct ionizing radiation during computed tomography (CT) examinations predisposes patients to cataract formation and should...

Robotics and ophthalmology: Are we there yet?

Ophthalmology is a field that is now seeing the integration of robotics in its surgical procedures and interventions. Assistance facilitated by robots...

Artificial intelligence in diabetic retinopathy: A natural step to the future.

Use of artificial intelligence in medicine in an evolving technology which holds promise for mass screening and perhaps may even help in establishing ...