Ophthalmic emergencies are acute vision-threatening disorders, for which a delay in prompt emergency response could result in catastrophic vision loss. Triage is an effective process for ensuring that timely emergency care is provided despite limited resource by prioritizing patients to appropriate orders for visits. Historically, registered nurses classify emergency patients based on personal experiences with high variation. Additionally, primary healthcare providers have been conventionally at the forefront of providing first aid care. However, most of ocular emergencies are wrongly diagnosed or referred due to non-eye specialists' limited knowledge and training in the ophthalmology. Here, the investigators established and validated an artificial intelligence system, EE-Explorer, to triage eye emergencies and assist in primary diagnosis using metadata and ocular images. This system has been integrated into a website to be prospectively validated in the real world.
Study Type
OBSERVATIONAL
Enrollment
100
An intelligent triage and diagnostic system for ophthalmic emergencies has been developed. In the prospective test, patients with acute ocular symptoms can achieve remote self-triage and primary diagnosis after uploading metadata and ocular images.
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, China
RECRUITINGThe accuracy of the triage model
Use the triage model to classify patients with acute ocular symptoms, and count the proportion of correct classification.
Time frame: 2023.1
The accuracy of the primary diagnostic model
Use the primary diagnostic model to diagnose patients with ophthalmic emergencies, and count the proportion of correct diagnosis in all patients.
Time frame: 2023.1
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