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Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

NCT04213183 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 1789

Last updated 2020-08-18

No results posted yet for this study

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.

Conditions

  • Ophthalmology
  • Artificial Intelligence
  • Hepatobiliary Disease

Interventions

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.

Sponsors & Collaborators

  • Third Affiliated Hospital, Sun Yat-Sen University

    collaborator OTHER

  • Affiliated Huadu Hospital of Southern Medical University

    collaborator UNKNOWN

  • Aikang Health Care

    collaborator UNKNOWN

  • Sun Yat-sen University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2018-12-01
Primary Completion
2020-01-31
Completion
2020-01-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT04213183 on ClinicalTrials.gov