Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Study Overview
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Brief Summary
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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.

Official Title
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Screening and Identifying Hepatobiliary Diseases Via Deep Learning Using Ocular Images

Conditions
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Ophthalmology, Artificial Intelligence, Hepatobiliary Disease

Intervention / Treatment
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* Diagnostic Test: Hepatobiliary Disorders


Participation Criteria
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Eligibility Criteria
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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.
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| development dataset 01<br>Slit-lamp and retinal fundus images collected from Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University. | Diagnostic Test: Hepatobiliary Disorders<br>* The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.<br>|
| development dataset 02<br>Slit-lamp and retinal fundus images collected from Affiliated Huadu Hospital of Southern Medical University. | Diagnostic Test: Hepatobiliary Disorders<br>* The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.<br>|
| development dataset 03<br>Slit-lamp and retinal fundus images collected from Nantian Medical Centre of Aikang Health Care. | Diagnostic Test: Hepatobiliary Disorders<br>* The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.<br>|
| test dataset 01<br>Slit-lamp and retinal fundus images collected from Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University. | Diagnostic Test: Hepatobiliary Disorders<br>* The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.<br>|
| test dataset 02<br>Slit-lamp and retinal fundus images collected from Huanshidong Medical Centre of Aikang Health Care. | Diagnostic Test: Hepatobiliary Disorders<br>* The training dataset was used to train the deep learning model, which was validated and tested by the other two datasets.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Eye Images, Hepatobiliary Disease, Artificial Intelligence