Detection of Jaundice From Ocular Images Via Deep Learning

Artificial Intelligence Clinical Trial

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT05682105
• Other study ID #: AEHD-2022
• Status: Active, not recruiting
• Start date: December 1, 2018
• Est. completion date: June 30, 2023

Study information

• Verified date: January 2023
• Source: Sun Yat-sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 1633
• Est. completion date: June 30, 2023
• Est. primary completion date: October 30, 2022
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 18 Years and older

Eligibility

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.

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
• Digestive System Diseases
• Hepatobiliary Disease
• Jaundice
• Liver Diseases
• Ophthalmology

Locations

Country	Name	City	State
China	Zhongshan Ophthalmic Center	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	baseline
Secondary	sensitivity and specificity of the deep learning system	The investigators will calculate the sensitivity and specifity of deep learning system	baseline