AI Medical Compendium Journal:
Clinical & experimental ophthalmology

Showing 11 to 20 of 21 articles

Reporting guidelines for artificial intelligence in healthcare research.

Clinical & experimental ophthalmology
Reporting guidelines are structured tools developed using explicit methodology that specify the minimum information required by researchers when reporting a study. The use of artificial intelligence (AI) reporting guidelines that address potential so...

Triaging ophthalmology outpatient referrals with machine learning: A pilot study.

Clinical & experimental ophthalmology
IMPORTANCE: Triaging of outpatient referrals to ophthalmology services is required for the maintenance of patient care and appropriate resource allocation. Machine learning (ML), in particular natural language processing, may be able to assist with t...

New grading criterion for retinal haemorrhages in term newborns based on deep convolutional neural networks.

Clinical & experimental ophthalmology
BACKGROUND: To define a new quantitative grading criterion for retinal haemorrhages in term newborns based on the segmentation results of a deep convolutional neural network.

Development of an artificial intelligence system to classify pathology and clinical features on retinal fundus images.

Clinical & experimental ophthalmology
IMPORTANCE: Artificial intelligence (AI) algorithms are under development for use in diabetic retinopathy photo screening pathways. To be clinically acceptable, such systems must also be able to classify other fundus abnormalities and clinical featur...

Current state and future prospects of artificial intelligence in ophthalmology: a review.

Clinical & experimental ophthalmology
Artificial intelligence (AI) has emerged as a major frontier in computer science research. Although AI has broad application across many medical fields, it will have particular utility in ophthalmology and will dramatically change the diagnostic and ...

Using natural language processing for identification of herpes zoster ophthalmicus cases to support population-based study.

Clinical & experimental ophthalmology
IMPORTANCE: Diagnosis codes are inadequate for accurately identifying herpes zoster (HZ) ophthalmicus (HZO). There is significant lack of population-based studies on HZO due to the high expense of manual review of medical records.

Diabetic retinopathy screening using deep neural network.

Clinical & experimental ophthalmology
IMPORTANCE: There is a burgeoning interest in the use of deep neural network in diabetic retinal screening.