Artificial Intelligent System for Eye Emergency Triage and Primary Diagnosis

Prospective Validation of an Artificial Intelligent System for Eye Emergency Triage and Primary Diagnosis

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 Overview

• Status: Recruiting
• Conditions: Eye Diseases; Emergencies
• Intervention / Treatment: Diagnostic test: Artificial intelligent system for eye emergency triage and primary diagnosis

• Study Type: Observational
• Enrollment (Anticipated): 100

Contacts and Locations

• Study Contact: Name: Haotian Lin, M.D., Ph.D; Phone Number: 8613802793086; Email: haot.lin@hotmail.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510060
      • Recruiting
      • Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
      • Contact: Haotian Lin, M.D., Ph.D; Phone Number: 8613802793086; Email: linht5@mail.sysu.edu.cn
      • Contact: Xiaohang Wu, M.D., Ph.D; Phone Number: 8615913177657; Email: wxhang@mail2.sysu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• 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.

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Cohort
• Time Perspectives: Prospective

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Eligible participants for AI-based ophthalmic emergency triage and primary diagnosis	Diagnostic test: Artificial 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	2023.1

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Acceptance of the patients	Questionnaire scores	2023.1

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (Actual): December 10, 2022
• Primary Completion (Anticipated): January 13, 2023
• Study Completion (Anticipated): January 20, 2023

Study Registration Dates

• First Submitted: December 13, 2022
• First Submitted That Met QC Criteria: December 25, 2022
• First Posted (Estimate): January 11, 2023

Study Record Updates

• Last Update Posted (Estimate): January 11, 2023
• Last Update Submitted That Met QC Criteria: December 25, 2022
• Last Verified: December 1, 2022

More Information

Terms related to this study

• Keywords: Artificial intelligence; Primary diagnosis; Ophthalmic emergency triage; Multimodal data
• Additional Relevant MeSH Terms: Pathologic Processes; Disease Attributes; Emergencies; Eye Diseases
• Other Study ID Numbers: 2021KYPJ046

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No