Validation of the Utility of an Intelligent Visual Acuity Diagnostic System for Children

Validation of the Utility of an Intelligent Visual Acuity Diagnostic System for Children: Using a Human-in-the-loop Artificial Intelligence Paradigm

Status

Completed

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT03766737

Enrollment

Registered

2018-12-06

Start date

2018-05-20

Completion date

2018-07-20

Last updated

2018-12-06

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Ophthalmopathy

Keywords

Vision Disorders

Brief summary

Visual development during early childhood is a vital process. Examining the visual acuity of children is essential for the early detection of visual abnormality, but performing such an assessment in children is challenging. Here, the investigators developed a human-in-the-loop artificial intelligence (AI) paradigm that combines traditional vision examination and AI with integrated software and hardware, thus making the vision examination easy to perform. The investigator also establish a entity intelligent visual acuity diagnostic system based on the paradigm, and conduct clinical trial to validate if the diagnostic system can offsetting the shortcomings of human doctors.

Interventions

DEVICEAn intelligent visual acuity diagnostic system for children

An artificial intelligence to make evaluation and of children's vision.

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Eligibility

Sex/Gender

ALL

Age

1 Months to 14 Years

Healthy volunteers

Yes

Inclusion criteria

* Paediatric patients from eye clinic written informed consents provided

Design outcomes

Primary

Measure	Time frame
The proportion of accurate, mistaken and miss detection of the intelligent visual acuity diagnostic system.	Up to 5 years

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026