Emergencies, Eye Diseases
Conditions
Keywords
Artificial intelligence, Ophthalmic emergency triage, Primary diagnosis, Multimodal data
Brief summary
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.
Interventions
DIAGNOSTIC_TESTArtificial intelligent system for eye emergency triage and primary diagnosis
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.
Sponsors
Sun Yat-sen University
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
1. Suffering acute ophthalmic symptoms within one month 2. Visiting the ocular emergency department for the first time 3. Must be able to complete the triage form for ophthalmic emergency 4. Must be able to cooperate either by submitting smartphone photographs or receiving slit-lamp examination
Exclusion criteria
The image quality does not meet the clinical requirements.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy of the triage model | 2023.1 | Use the triage model to classify patients with acute ocular symptoms, and count the proportion of correct classification. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy of the primary diagnostic model | 2023.1 | Use the primary diagnostic model to diagnose patients with ophthalmic emergencies, and count the proportion of correct diagnosis in all patients. |
Other
| Measure | Time frame | Description |
|---|---|---|
| Acceptance of the patients | 2023.1 | Questionnaire scores |
Countries
China
Contacts
Primary ContactHaotian Lin, M.D., Ph.D
Outcome results
None listed