Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis

Overview

Fundus images are widely used in ophthalmology for the detection of diabetic retinopathy, glaucoma and other diseases. In real-world practice, the quality of fundus 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 fundus images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

Full Title of Study: “Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis: A Clinical Trial”

Study Type

  • Study Type: Observational
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: July 1, 2020

Interventions

  • Device: Taking a fundus image
    • The participant only needs to take a fundus image as usual.

Arms, Groups and Cohorts

  • Fundus image quality assessment
    • Device: an artificial intelligence system for quality assessment of fundus images. These patients are enrolled in primary healthcare units or the AI clinic at Zhongshan Ophthalmic Center.

Clinical Trial Outcome Measures

Primary Measures

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

Secondary Measures

  • The comparison of the performance for previous artificial intelligence diagnostic system with fundus images of different image quality
    • Time Frame: 3 months
    • Cohen’s kappa coefficient, P value and other related statistic results

Participating in This Clinical Trial

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.

Gender Eligibility: All

Minimum Age: N/A

Maximum Age: N/A

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • Sun Yat-sen University
  • Provider of Information About this Clinical Study
    • Principal Investigator: Haotian Lin, Clinical Professor – Sun Yat-sen University

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