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

Research of Automated Maculopathy Screening by Optical Coherent Tomography Image-based Deep Learning Techniques

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.

Study Overview

• Status: Unknown
• Conditions: 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 Type: Observational
• Enrollment (Anticipated): 20000

Contacts and Locations

Study Locations

• China
  • Jiangsu
    • Nanjing, Jiangsu, China, 210029
      • The First Affiliated Hospital with Nanjing Medical University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: N/A
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

no age limited, no gender-based

Description

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.

Study Plan

How is the study designed?

Design Details

• Observational Models: Other
• Time Perspectives: Cross-Sectional

Number of groups / cohorts

2

Cohorts and Interventions

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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
receiver operating characteristic(ROC) curve of the algorithm	It is also called sensitivity curve. The ROC curve shows how sensitive the algorithm model is to automatically detect the desired output.	approximately 1 year
Area under the ROC curve(AUC)	It shows the operating value of the algorithm model, which can represent the effect of the model.	approximately 1 year

Collaborators and Investigators

• Sponsor: The First Affiliated Hospital with Nanjing Medical University
• Investigators: Principal Investigator: Songtao Yuan, doctor, The First Affiliated Hospital with Nanjing Medical University

Study record dates

Study Major Dates

• Study Start (Actual): June 30, 2017
• Primary Completion (Anticipated): June 1, 2018
• Study Completion (Anticipated): December 31, 2020

Study Registration Dates

• First Submitted: February 26, 2018
• First Submitted That Met QC Criteria: March 22, 2018
• First Posted (Actual): March 26, 2018

Study Record Updates

• Last Update Posted (Actual): March 26, 2018
• Last Update Submitted That Met QC Criteria: March 22, 2018
• Last Verified: February 1, 2018

More Information

Terms related to this study

• Keywords: OCT; screening; AI; Deep learning; macular diseases
• Additional Relevant MeSH Terms: Eye Diseases; Retinal Degeneration; Retinal Diseases; Macular Degeneration
• Other Study ID Numbers: JSPH-AIOCT-001

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