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

NCT05615519 | Unknown DMC

• 1 other identifier: DRStrabismus2022
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

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.

Conditions

Strabismus; Exotropia; Esotropia; Vertical Strabismus

Keywords

Mobile health; Artificaial intelligence medicine; Pediatric ophthalmology

Outcome Measures

Primary Outcomes (2)

• The effectiveness of smartphone-based diagnosis

  The accurate and the area under curve of the smartphone-based diagnosis.

  Baseline

• The consistency between manual and smartphone measurement

  Agreement between the manual and automated tests was represented in Bland-Altman plots and concordance correlation coefficients.

  Baseline

Study Arms (1)

Eligible participants for smartphone-based strabismus measurement and diagnosis

Facial videos dataset Facial videos were collected using smartphone and following the programmatic cover tests.

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.

Interventions

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. DIAGNOSTIC_TEST

Digital ruler of strabismus

Eligible participants for smartphone-based strabismus measurement and diagnosis

Eligibility Criteria

• Age: 3 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Outpatients from strabismus \& optometry departments or participants from the internet.

You may qualify if:

• The quality of facial videos should be clinically acceptable.

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

Strabismus; Exotropia; Esotropia

Condition Hierarchy (Ancestors)

Ocular Motility Disorders; Cranial Nerve Diseases; Nervous System Diseases; Eye Diseases

Central Study Contacts

Haotian Lin, M.D., Ph.D

CONTACT

8613802793086; haot.lin@hotmail.com

Ruixin Wang, M.D., Ph.D

CONTACT

8615360458084; ruiruiw413@aliyun.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: November 7, 2022
• First Posted: November 14, 2022
• Study Start: December 2, 2022
• Primary Completion: March 1, 2023
• Study Completion: April 1, 2023
• Last Updated: December 5, 2022

Record last verified: 2022-12

Locations

CN (1)