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Research of Automated Maculopathy Screening Based on AI Techniques Using OCT Images

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: NCT03476291
Recruitment Status : Unknown
Verified February 2018 by The First Affiliated Hospital with Nanjing Medical University.
Recruitment status was:  Active, not recruiting
First Posted : March 26, 2018
Last Update Posted : March 26, 2018
Sponsor:
Information provided by (Responsible Party):
The First Affiliated Hospital with Nanjing Medical University

Study Description
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Brief Summary:
The investigators expect to develop an algorithm that can interpret OCT images and automated determine whether the macula is normal or not by using OCT image-based deep learning techniques. And investigators wish to develop software applications that will help better screen and diagnose macular diseases in resource-limited areas.

Condition or disease
Maculopathy

Detailed Description:
The investigators will apply deep learning convolutional neural network by using ImageNet for an automated detection of multiple retinal diseases with OCT horizontal B-scans with a high-quality labeled database. Datasets, including training dataset, testing dataset and validation datasets, will be built by ophthalmologists of the First affiliated hospital of Nanjing Medical University according to the standardized annotation guidelines.
Study Design
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Study Type : Observational
Estimated Enrollment : 20000 participants
Observational Model: Other
Time Perspective: Cross-Sectional
Official Title: Research of Automated Maculopathy Screening by Optical Coherent Tomography Image-based Deep Learning Techniques
Actual Study Start Date : June 30, 2017
Estimated Primary Completion Date : June 1, 2018
Estimated Study Completion Date : December 31, 2020

Resource links provided by the National Library of Medicine


Groups and Cohorts
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Group/Cohort
Normal
normal macular structure of horizontal OCT B-scans
Abnormal
abnormal macular structure of horizontal OCT B-scans, including many sub-categories of pathological features, like epiretinal membrane, pigment epithelium detachment, ect.


Outcome Measures
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Primary Outcome Measures :
  1. receiver operating characteristic(ROC) curve of the algorithm [ Time Frame: approximately 1 year ]
    It is also called sensitivity curve. The ROC curve shows how sensitive the algorithm model is to automatically detect the desired output.

  2. Area under the ROC curve(AUC) [ Time Frame: approximately 1 year ]
    It shows the operating value of the algorithm model, which can represent the effect of the model.


Eligibility Criteria
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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Sampling Method:   Non-Probability Sample
Study Population
no age limited, no gender-based
Criteria

Inclusion Criteria:

  • All patients attending the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University within 5 years and who received known, clear diagnoses with digital retinal imaging (including OCT, fundus digital photographs and fundus fluorescein angiography, at least with OCT images) as part of their routine clinical care, will be eligible for inclusion in this study.

Exclusion Criteria:

  • Hardcopy examinations (i.e., photos of paper reports of OCT imaging performed at other hospitals) will be ineligible.
  • Data from patients who have previously manually requested that their data should not be shared, even for research purposes in anonymised form, and have informed the Ophthalmology Department of the First Affiliated Hospital of Nanjing Medical University of this desire (even in previously conducted studies or other on-going studies in this hospital), will be excluded, and their data will not be upload to the cloud platform before research begins.
  • Data from eyes tamponed with silicone oil or gas (i.e., C3F8) will be ineligible.
  • Data with poor image quality, such as incomplete images, inverted images, blurred or cracked images and images with a very weak signal (i.e., vitreous haemorrhage), will be ineligible.
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): NCT03476291


Locations
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China, Jiangsu
The First Affiliated Hospital with Nanjing Medical University
Nanjing, Jiangsu, China, 210029
Sponsors and Collaborators
The First Affiliated Hospital with Nanjing Medical University
Investigators
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Principal Investigator: Songtao Yuan, doctor The First Affiliated Hospital with Nanjing Medical University
More Information
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Responsible Party: The First Affiliated Hospital with Nanjing Medical University
ClinicalTrials.gov Identifier: NCT03476291    
Other Study ID Numbers: JSPH-AIOCT-001
First Posted: March 26, 2018    Key Record Dates
Last Update Posted: March 26, 2018
Last Verified: February 2018
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by The First Affiliated Hospital with Nanjing Medical University:
macular diseases
screening
OCT
Deep learning
AI
Additional relevant MeSH terms:
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Macular Degeneration
Retinal Degeneration
Retinal Diseases
Eye Diseases