Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Overview

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.

Study Type

  • Study Type: Observational [Patient Registry]
  • Study Design
    • Time Perspective: Prospective
  • Study Primary Completion Date: January 31, 2020

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.

Arms, Groups and Cohorts

  • 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.

Clinical Trial Outcome Measures

Primary Measures

  • area under the receiver operating characteristic curve of the deep learning system
    • Time Frame: 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 Measures

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

Participating in This Clinical Trial

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.

Gender Eligibility: All

Minimum Age: N/A

Maximum Age: N/A

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • Sun Yat-sen University
  • Collaborator
    • Third Affiliated Hospital, Sun Yat-Sen University
  • Provider of Information About this Clinical Study
    • Principal Investigator: Haotian Lin, Principal Investigator – Sun Yat-sen University

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