Artificial Intelligent System for Eye Emergency Triage and Primary Diagnosis

NCT05680090 | Unknown DMC

• 1 other identifier: 2021KYPJ046
• Study Type: observational
• Target: 100
• Locations: 1 country
• Sites: 1

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.

Conditions

Emergencies; Eye Diseases

Keywords

Artificial intelligence; Ophthalmic emergency triage; Primary diagnosis; Multimodal data

Outcome Measures

Primary Outcomes (1)

• The accuracy of the triage model

  Use the triage model to classify patients with acute ocular symptoms, and count the proportion of correct classification.

  2023.1

Secondary Outcomes (1)

• The accuracy of the primary diagnostic model

  2023.1

Other Outcomes (1)

• Acceptance of the patients

  2023.1

Study Arms (1)

Eligible participants for AI-based ophthalmic emergency triage and primary diagnosis

Diagnostic Test: Artificial intelligent system for eye emergency triage and primary diagnosis

Interventions

Artificial intelligent system for eye emergency triage and primary diagnosis DIAGNOSTIC_TEST

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.

Eligible participants for AI-based ophthalmic emergency triage and primary diagnosis

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Through the online popular science, news reports, and other channels, we will promote and inform patients about the relevant knowledge of ophthalmic emergencies, so that they can judge by themselves and freely decide whether to participate in this study or not.

You may qualify if:

• Suffering acute ophthalmic symptoms within one month
• Visiting the ocular emergency department for the first time
• Must be able to complete the triage form for ophthalmic emergency
• Must be able to cooperate either by submitting smartphone photographs or receiving slit-lamp examination

You may not qualify if:

• The image quality does not meet the clinical requirements.

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity

Guangzhou, Guangdong, 510060, China

RECRUITING

MeSH Terms

Conditions

Emergencies; Eye Diseases

Condition Hierarchy (Ancestors)

Disease Attributes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Haotian Lin, M.D., Ph.D

CONTACT

8613802793086; haot.lin@hotmail.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: December 13, 2022
• First Posted: January 11, 2023
• Study Start: December 10, 2022
• Primary Completion: January 13, 2023
• Study Completion: January 20, 2023
• Last Updated: January 11, 2023

Record last verified: 2022-12

Locations

CN (1)