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AI Classifies Multi-Retinal Diseases

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04592068
Recruitment Status : Unknown
Verified October 2020 by Beijing Tongren Hospital.
Recruitment status was:  Recruiting
First Posted : October 19, 2020
Last Update Posted : December 11, 2020
Sponsor:
Collaborator:
Beijing Tulip Partner Technology Co., Ltd, China
Information provided by (Responsible Party):
Beijing Tongren Hospital

Study Description
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Brief Summary:
The objective of this study is to establish deep learning (DL) algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. The effectiveness and accuracy of the established algorithm will be evaluated in community derived dataset.

Condition or disease Intervention/treatment
Deep Learning Retinal Diseases Device: Retinal multi-diseases diagnosed by DL algorithm Other: Retinal multi-diseases diagnosed by expert panel

Detailed Description:

Retinal diseases seriously threaten vision and quality of life, but they often develop insidiously. To date, deep learning (DL) algorithms have shown high prospects in biomedical science, particularly in the diagnosis of ocular diseases, such as diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, glaucoma, and papilledema. However, there is still a lack of a single algorithm that can classify multi-diseases from fundus photography.

This cross-sectional study will establish a DL algorithm to automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities. We will use the receiver operating characteristic (ROC) curve to examine the ability of recognition and classification of diseases. Taken the results of the expert panel as the gold standard, we will use the evaluation indexes, such as sensitivity, specificity, accuracy, positive predictive value, negative predictive value, etc, to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

Study Design
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Study Type : Observational
Estimated Enrollment : 10000 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Deep Learning-Based Automated Classification of Multi-Retinal Disease From Fundus Photography
Actual Study Start Date : November 1, 2020
Estimated Primary Completion Date : November 1, 2021
Estimated Study Completion Date : December 1, 2021

Resource links provided by the National Library of Medicine


Groups and Cohorts
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Group/Cohort Intervention/treatment
Retinal multi-diseases diagnosed by DL algorithm Device: Retinal multi-diseases diagnosed by DL algorithm
DL algorithm automatically classify multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.
Retinal multi-diseases diagnosed by expert panel Other: Retinal multi-diseases diagnosed by expert panel
Expert panel classifies multi-diseases from fundus photography and differentiate major vision-threatening conditions and other retinal abnormalities.


Outcome Measures
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Primary Outcome Measures :
  1. Area under curve [ Time Frame: 1 week ]
    We will use the receiver operating characteristic (ROC) curve to examine the ability of recognition and classification of diseases. Taken the results of the expert panel as the gold standard, we will use the area under curve to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

  2. Sensitivity and specificity [ Time Frame: 1 week ]
    Taken the results of the expert panel as the gold standard, we will use sensitivity and specificity to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

  3. Positive and negative predictive value [ Time Frame: 1 week ]
    Taken the results of the expert panel as the gold standard, we will use positive and negative predictive value to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.

  4. Accuracy [ Time Frame: 1 week ]
    Taken the results of the expert panel as the gold standard, we will use accuracy to compare the diagnostic capacity between the AI recognition system and human ophthalmologist.


Eligibility Criteria
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Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
community derived dataset
Criteria

Inclusion Criteria:

  • fundus photography around 45° field which covers optic disc and macula
  • complete patient identification information;

Exclusion Criteria:

  • incomplete patient identification information
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04592068


Locations
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China, Beijing
Wen-Bin Wei Recruiting
Beijing, Beijing, China, 100730
Contact: Wen-Bin Wei, MD       weiwenbintr@163.com   
Principal Investigator: Wen-Bin Wei, MD         
Sponsors and Collaborators
Beijing Tongren Hospital
Beijing Tulip Partner Technology Co., Ltd, China
More Information
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Responsible Party: Beijing Tongren Hospital
ClinicalTrials.gov Identifier: NCT04592068    
Other Study ID Numbers: Retinal multi diseases
First Posted: October 19, 2020    Key Record Dates
Last Update Posted: December 11, 2020
Last Verified: October 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: Yes
Studies a U.S. FDA-regulated Device Product: No
Product Manufactured in and Exported from the U.S.: No
Additional relevant MeSH terms:
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Retinal Diseases
Eye Diseases