Detection of Jaundice From Ocular Images Via Deep Learning

Status Active, not recruiting

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.

Clinical Trial 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

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT05682105
Study type	Observational
Source	Sun Yat-sen University
Contact
Status	Active, not recruiting
Phase
Start date	December 1, 2018
Completion date	June 30, 2023

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