Research of Automated Maculopathy Screening Based on AI Techniques Using OCT Images
1 other identifier
observational
20,000
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Jun 2017
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
June 30, 2017
CompletedFirst Submitted
Initial submission to the registry
February 26, 2018
CompletedFirst Posted
Study publicly available on registry
March 26, 2018
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 1, 2018
CompletedStudy Completion
Last participant's last visit for all outcomes
December 31, 2020
CompletedMarch 26, 2018
February 1, 2018
11 months
February 26, 2018
March 22, 2018
Conditions
Keywords
Outcome Measures
Primary Outcomes (2)
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
Study Arms (2)
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.
Eligibility Criteria
no age limited, no gender-based
You may not qualify if:
- 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.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
The First Affiliated Hospital with Nanjing Medical University
Nanjing, Jiangsu, 210029, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Officials
- PRINCIPAL INVESTIGATOR
Songtao Yuan, doctor
The First Affiliated Hospital with Nanjing Medical University
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- CROSS SECTIONAL
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
February 26, 2018
First Posted
March 26, 2018
Study Start
June 30, 2017
Primary Completion
June 1, 2018
Study Completion
December 31, 2020
Last Updated
March 26, 2018
Record last verified: 2018-02
Data Sharing
- IPD Sharing
- Will not share