AIMC Topic: Diagnostic Techniques, Ophthalmological

Clear Filters Showing 11 to 20 of 165 articles

Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage challenging. We studied a smartphone-based quantitative pupillometer for differentiation of acute ischemic and hemorrhagic stroke.

Artificial intelligence in ophthalmology.

Current opinion in ophthalmology
PURPOSE OF REVIEW: To review role of artificial intelligence in medicine.

Leveraging Artificial Intelligence for Diabetic Retinopathy Screening and Management: History and Current Advances.

Seminars in ophthalmology
AIM: Regular screening of large number of people with diabetes for diabetic retinopathy (DR) with the support of available human resources alone is a global challenge. Digital health innovation is a boon in screening for DR. Multiple artificial intel...

Applications of artificial intelligence to inherited retinal diseases: A systematic review.

Survey of ophthalmology
Artificial intelligence(AI)-based methods have been extensively used for the detection and management of various common retinal conditions, but their targeted development for inherited retinal diseases (IRD) is still nascent. In the context of limite...

Application of Artificial Intelligence in the Diagnosis, Follow-Up and Prediction of Treatment of Ophthalmic Diseases.

Seminars in ophthalmology
PURPOSE: To describe the application of artificial intelligence (AI) in ophthalmic diseases and its possible future directions.

Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images.

The British journal of ophthalmology
BACKGROUND: Artificial intelligence (AI) in medical imaging diagnostics has huge potential, but human judgement is still indispensable. We propose an AI-aided teaching method that leverages generative AI to train students on many images while preserv...

Advances in artificial intelligence for meibomian gland evaluation: A comprehensive review.

Survey of ophthalmology
Meibomian gland dysfunction (MGD) is increasingly recognized as a critical contributor to evaporative dry eye, significantly impacting visual quality. With a global prevalence estimated at 35.8 %, it presents substantial challenges for clinicians. Co...

Applications of Artificial Intelligence in Diagnosis of Dry Eye Disease: A Systematic Review and Meta-Analysis.

Cornea
PURPOSE: Clinical diagnosis of dry eye disease is based on a subjective Ocular Surface Disease Index questionnaire or various objective tests, however, these diagnostic methods have several limitations.

AI in Neuro-Ophthalmology: Current Practice and Future Opportunities.

Journal of neuro-ophthalmology : the official journal of the North American Neuro-Ophthalmology Society
BACKGROUND: Neuro-ophthalmology frequently requires a complex and multi-faceted clinical assessment supported by sophisticated imaging techniques in order to assess disease status. The current approach to diagnosis requires substantial expertise and ...

Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review.

Survey of ophthalmology
Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integ...