Detection of Jaundice From Ocular Images Via Deep Learning

Sponsor

Sun Yat-sen University (Other)

Overall Status

Active, not recruiting

CT.gov ID

NCT05682105

Collaborator

Third Affiliated Hospital, Sun Yat-Sen University (Other), Affiliated Huadu Hospital of Southern Medical University (Other), Aikang Health Care (Other)

1,633

Enrollment

Location

54.9

Anticipated Duration (Months)

29.7

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

Our study presents a detection model predicting a diagnosis of jaundice (clinical jaundice and occult jaundice) trained on prospective cohort data from slit-lamp photos and smartphone photos, demonstrating the model's validity and assisting clinical workers in identifying patient underlying hepatobiliary diseases.

Condition or Disease	Intervention/Treatment	Phase
Ophthalmology Artificial Intelligence Hepatobiliary Disease

Detailed Description

This study demonstrated that deep learning models could detect jaundice using ocular images in blood levels with reasonable accuracy, providing a non-invasive method for jaundice detection and recognition. This algorithm can assist clinical surgeons with daily follow-up visits and provide referral advice. It also highlights the algorithm's potential smartphone application in sizeable real-world population-based disease-detecting or telemedicine programs.

Study Design

Study Type:

Observational

Actual Enrollment :

1633 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

Detection of Jaundice From Ocular Images Via Deep Learning : a Prospective, Multicenter Cohort Study

Actual Study Start Date :

Dec 1, 2018

Actual Primary Completion Date :

Oct 30, 2022

Anticipated Study Completion Date :

Jun 30, 2023

Arms and Interventions

Arm	Intervention/Treatment
Development dataset Slit-lamp images collected from the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University(HTH)， Affiliated Huadu Hospital of Southern Medical University(HDH)， and Nantian Medical Centre of Aikang Health Care (NMC).
Testing dataset Slit-lamp and smartphone images collected from the Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University（ITH）， Huanshidong Medical Centre of Aikang Health Care， the Medical Centre of the Third Affiliated Hospital of Sun Yat-sen University(MCH).

Outcome Measures

Primary Outcome Measures

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

Secondary Outcome Measures

sensitivity and specificity of the deep learning system [baseline]
The investigators will calculate the sensitivity and specifity of deep learning system

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years and Older

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Yes

Inclusion Criteria:

The quality of slit-lamp images should be clinical acceptable. More than 90% of the slit-lamp image area, including three central regions (sclera, pupil, and lens) are easy to read and discriminate.