Validation of a Smartphone-based Intelligent Diagnosis and Measurement for Strabismus

Recruiting

• Conditions: Vertical Strabismus; Exotropia; Esotropia; Strabismus
• Interventions: Diagnostic Test: A new technology based on 3D reconstruction and deep learning algorithm to achieve an automatic diagnosis of strabismus based on patient-sourced videos of programmatic cover tests.

• Registration Number: NCT05615519
• Lead Sponsor: Sun Yat-sen University

Brief Summary

The current measurement methods of strabismus include the corneal light reflection method, prism alternate covering, etc., which especially rely on the subjective experience of doctors, and there is a large error between different measurers， leading to serious underestimation of strabismus prevalence and insufficient care for strabismus patients. Here, the investigators established and validated an artificial intelligence system to achieve an automatic diagnosis of strabismus based on patient-sourced videos of programmatic cover tests. Three-dimensional reconstruction methods are used to digitize the parameters of head and eye positions. This system has been integrated into a smartphone platform to be further validated through hospital-based and population-based clinical trials.

Detailed Description

Not available

Study Timeline

• Study First Submitted: 2022-11-07
• Study First Submitted QC: 2022-11-07
• Study First Posted: 2022-11-14
• Status Verified: 2022-12
• Study Start: 2022-12-02
• Last Update Submitted: 2022-12-02
• Last Update Posted: 2022-12-05
• Primary Completion: 2023-03-01
• Study Completion: 2023-04-01

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 300

Inclusion Criteria

The quality of facial videos should be clinically acceptable.

Exclusion Criteria

Not provided

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
Eligible participants for smartphone-based strabismus measurement and diagnosis	A new technology based on 3D reconstruction and deep learning algorithm to achieve an automatic diagnosis of strabismus based on patient-sourced videos of programmatic cover tests.	Facial videos dataset Facial videos were collected using smartphone and following the programmatic cover tests.

Primary Outcome Measures

Name	Time	Method
The effectiveness of smartphone-based diagnosis	Baseline	The accurate and the area under curve of the smartphone-based diagnosis.
The consistency between manual and smartphone measurement	Baseline	Agreement between the manual and automated tests was represented in Bland-Altman plots and concordance correlation coefficients.

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity; 🇨🇳 Guangzhou, Guangdong, China