Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Artificial Intelligence Clinical Trial

Official title:

Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT04213183
• Other study ID #: AEHD-2019
• Status: Completed
• Start date: December 1, 2018
• Est. completion date: January 31, 2020

Study information

• Verified date: August 2020
• Source: Sun Yat-sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 1789
• Est. completion date: January 31, 2020
• Est. primary completion date: January 31, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Hepatobiliary Disease
• Liver Diseases
• Ophthalmology

Intervention

Diagnostic Test:
• Hepatobiliary Disorders
The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.

Locations

Country	Name	City	State
China	Zhongshan Ophthalmic Center, Sun Yat-sen Univerisity	Guangzhou	Guangdong

Sponsors (4)

Lead Sponsor	Collaborator
Sun Yat-sen University	Affiliated Huadu Hospital of Southern Medical University, Aikang Health Care, Third Affiliated Hospital, Sun Yat-Sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	sensitivity and specificity of the deep learning system	The investigators will calculate the sensitivity and specifity of deep learning system and compare this index between deep learning system and human doctors	baseline