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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

20000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2017-06-30

Study Completion Date

2020-12-31

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Maculopathy

Study Design

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Observational Model Type

OTHER

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Normal

normal macular structure of horizontal OCT B-scans

No interventions assigned to this group

Abnormal

abnormal macular structure of horizontal OCT B-scans, including many sub-categories of pathological features, like epiretinal membrane, pigment epithelium detachment, ect.

No interventions assigned to this group

Eligibility Criteria

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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.

Eligible Sex

ALL

Accepts Healthy Volunteers

No