Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis

Artificial Intelligence System for Assessing Image Quality of Slit-Lamp Images and Its Effects on Diagnosis: A Clinical Trial

Slit-lamp images are widely used in ophthalmology for the detection of cataract, keratopathy and other anterior segment disorders. In real-world practice, the quality of slit-lamp images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of slit-lamp images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

Study Overview

• Status: Unknown
• Conditions: Artificial Intelligence; Anterior Segment Disorders
• Intervention / Treatment: Device: Taking slit-lamp images

• Study Type: Observational
• Enrollment (Anticipated): 300

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510060
      • Zhongshan Ophthalmic Center, Sun Yat-sen University

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

Inclusion Criteria: - Patients should be aware of the contents and signed for the informed consent. Exclusion Criteria: - 1. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths. - 2. Patients who do not agree to sign informed consent

Description

Inclusion Criteria:

• Patients should be aware of the contents and signed for the informed consent.

Exclusion Criteria:

• 1. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths.
• 2. Patients who do not agree to sign informed consent.

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
Slit-lamp image quality assessment; Device: an artificial intelligence system for quality assessment of slit-lamp images. These patients are enrolled in primary healthcare units or the AI clinic at Zhongshan Ophthalmic Center	Device: Taking slit-lamp images; The participant only needs to take several slit-lamp images as usual.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Performance of artificial intelligence system for distinguish between good image quality and poor image quality	Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values,accuracy	3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The comparison of the performance for previous artificial intelligence diagnostic system with slit-lamp images of different image quality	Cohen's kappa coefficient, P value and other related statistic results	3 months

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2020
• Primary Completion (Anticipated): July 1, 2020
• Study Completion (Anticipated): July 1, 2020

Study Registration Dates

• First Submitted: March 16, 2020
• First Submitted That Met QC Criteria: March 16, 2020
• First Posted (Actual): March 19, 2020

Study Record Updates

• Last Update Posted (Actual): March 19, 2020
• Last Update Submitted That Met QC Criteria: March 16, 2020
• Last Verified: March 1, 2020

More Information

Terms related to this study

• Other Study ID Numbers: IMAQUA2020-China-02

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

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