Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images
1 other identifier
observational
1,789
1 country
1
Brief Summary
Artificial Intelligence may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in individuals with hepatobiliary disorders. We conducted a pioneer work to explore the association between the eye and liver via deep learning, to develop and evaluate different deep learning models to predict the hepatobiliary disease by using ocular images.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Dec 2018
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
December 1, 2018
CompletedFirst Submitted
Initial submission to the registry
December 25, 2019
CompletedFirst Posted
Study publicly available on registry
December 30, 2019
CompletedPrimary Completion
Last participant's last visit for primary outcome
January 31, 2020
CompletedStudy Completion
Last participant's last visit for all outcomes
January 31, 2020
CompletedAugust 18, 2020
August 1, 2020
1.2 years
December 25, 2019
August 16, 2020
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
area under the receiver operating characteristic curve of the deep learning system
The investigators will calculate the area under the receiver operating characteristic curve of deep learning system and compare this index between deep learning system and human doctors
baseline
Secondary Outcomes (1)
sensitivity and specificity of the deep learning system
baseline
Study Arms (5)
development dataset 01
Slit-lamp and retinal fundus images collected from Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University.
development dataset 02
Slit-lamp and retinal fundus images collected from Affiliated Huadu Hospital of Southern Medical University.
development dataset 03
Slit-lamp and retinal fundus images collected from Nantian Medical Centre of Aikang Health Care.
test dataset 01
Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University.
test dataset 02
Slit-lamp and retinal fundus images collected from Huanshidong Medical Centre of Aikang Health Care.
Interventions
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.
Eligibility Criteria
Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University, Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University, Affiliated Huadu Hospital of Southern Medical University, Nantian Medical Centre of Aikang Health Care, and Huanshidong Medical Centre of Aikang Health Care.
You may qualify if:
- The quality of fundus and slit-lamp images should clinical acceptable.
- More than 90% of the fundus image area including four main regions (optic disk, macular, upper and lower retinal vessel archs) are easy to read and discriminate.
- More than 90% of the slit-lamp image area including three main regions (sclera, pupil, and lens) are easy to read and discriminate.
You may not qualify if:
- Images with light leakage (\>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Sun Yat-sen Universitylead
- Third Affiliated Hospital, Sun Yat-Sen Universitycollaborator
- Affiliated Huadu Hospital of Southern Medical Universitycollaborator
- Aikang Health Carecollaborator
Study Sites (1)
Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity
Guangzhou, Guangdong, 510000, China
MeSH Terms
Conditions
Study Design
- Study Type
- observational
- Observational Model
- OTHER
- Time Perspective
- PROSPECTIVE
- Target Duration
- 1 Month
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Principal Investigator
Study Record Dates
First Submitted
December 25, 2019
First Posted
December 30, 2019
Study Start
December 1, 2018
Primary Completion
January 31, 2020
Study Completion
January 31, 2020
Last Updated
August 18, 2020
Record last verified: 2020-08
Data Sharing
- IPD Sharing
- Will not share