Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Completed

• Conditions: Artificial Intelligence; Hepatobiliary Disease; Ophthalmology
• Interventions: Diagnostic Test: Hepatobiliary Disorders

• Registration Number: NCT04213183
• Lead Sponsor: Sun Yat-sen University

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.

Detailed Description

Not available

Study Timeline

• Study Start: 2018-12-01
• Study First Submitted: 2019-12-25
• Study First Submitted QC: 2019-12-25
• Study First Posted: 2019-12-30
• Primary Completion: 2020-01-31
• Study Completion: 2020-01-31
• Status Verified: 2020-08
• Last Update Submitted: 2020-08-16
• Last Update Posted: 2020-08-18

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1789

Inclusion Criteria

• 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.

Exclusion Criteria

• Images with light leakage (>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
development dataset 03	Hepatobiliary Disorders	Slit-lamp and retinal fundus images collected from Nantian Medical Centre of Aikang Health Care.
development dataset 02	Hepatobiliary Disorders	Slit-lamp and retinal fundus images collected from Affiliated Huadu Hospital of Southern Medical University.
test dataset 01	Hepatobiliary Disorders	Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University.
test dataset 02	Hepatobiliary Disorders	Slit-lamp and retinal fundus images collected from Huanshidong Medical Centre of Aikang Health Care.
development dataset 01	Hepatobiliary Disorders	Slit-lamp and retinal fundus images collected from Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University.

Primary Outcome Measures

Name	Time	Method
area under the receiver operating characteristic curve of the deep learning system	baseline	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

Secondary Outcome Measures

Name	Time	Method
sensitivity and specificity of the deep learning system	baseline	The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity; 🇨🇳 Guangzhou, Guangdong, China