Detection of Jaundice From Ocular Images Via Deep Learning

Sun Yat-sen University

Status: Active, not recruiting

Conditions: Artificial Intelligence; Ophthalmology; Hepatobiliary Disease

Study type: Observational

Funder types: Other

Identifiers

NCT05682105

AEHD-2022

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,633 patients

Sex: All

Ages: 18+ years old

Volunteers: Accepts Healthy Volunteers

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

Trial design

1,633 participants in 2 patient groups

Development dataset

Description:

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

Description:

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).