Detection of Jaundice From Ocular Images Via Deep Learning

Active, not recruiting

• Conditions: Ophthalmology; Artificial Intelligence; Hepatobiliary Disease

• Registration Number: NCT05682105
• Lead Sponsor: Sun Yat-sen University

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.

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 Timeline

• Study Start: 2018-12-01
• Primary Completion: 2022-10-30
• Study First Submitted: 2022-12-26
• Status Verified: 2023-01
• Study First Submitted QC: 2023-01-10
• Last Update Submitted: 2023-01-10
• Study First Posted: 2023-01-12
• Last Update Posted: 2023-01-12
• Study Completion: 2023-06-30

Recruitment & Eligibility

• Status: ACTIVE_NOT_RECRUITING
• Sex: All
• Target Recruitment: 1633

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
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

Name	Time	Method
sensitivity and specificity of the deep learning system	baseline	The investigators will calculate the sensitivity and specifity of deep learning system

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center; 🇨🇳 Guangzhou, Guangdong, China