Detection of Jaundice From Ocular Images Via Deep Learning

NCT05682105 | Unknown DMC

• 1 other identifier: AEHD-2022
• Study Type: observational
• Target: 1,633
• Locations: 1 country
• Sites: 1

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.

Conditions

Ophthalmology; Artificial Intelligence; Hepatobiliary Disease

Keywords

Ophthalmology; Artificial Intelligence; ocualr image; Jaundice

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• sensitivity and specificity of the deep learning system

  baseline

Study Arms (2)

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).

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

This prospective, multicentre, observational study was led by Zhongshan Ophthalmic Centre (ZOC), Sun Yat-sen University, and conducted in three phases to collect data from participants from three surgery departments and three medical examination centres, including the Department of Hepatobiliary Surgery of the Third Affiliated Hospital of Sun Yat-sen University (HTH; Guangzhou, China), the Department of Infectious Diseases, Third Affiliated Hospital of Sun Yat-sen University (ITH; Guangzhou, China), the Department of Infectious Diseases, the Affiliated Huadu Hospital of Southern Medical University (HDH; Guangzhou, China), the Medical Centre of the Third Affiliated Hospital of Sun Yat-sen University(MCH; Guangzhou, China), Nantian Medical Centre of Aikang Health Care (NMC), and Huanshidong Medical Centre of Aikang Health Care (HMC).

You may qualify if:

• 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.

You may not qualify if:

• Images with light leakage (\>10% of the area), spots from lens flares or stains, and overexposure were excluded from further analysis

Sponsors & Collaborators

• Sun Yat-sen University: lead
• Third Affiliated Hospital, Sun Yat-Sen University: collaborator
• Affiliated Huadu Hospital of Southern Medical University: collaborator
• Aikang Health Care: collaborator

Study Sites (1)

Zhongshan Ophthalmic Center

Guangzhou, Guangdong, 510000, China

Location

MeSH Terms

Conditions

Digestive System Diseases; Jaundice

Condition Hierarchy (Ancestors)

Hyperbilirubinemia; Pathologic Processes; Pathological Conditions, Signs and Symptoms; Skin Manifestations; Signs and Symptoms

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: December 26, 2022
• First Posted: January 12, 2023
• Study Start: December 1, 2018
• Primary Completion: October 30, 2022
• Study Completion: June 30, 2023
• Last Updated: January 12, 2023

Record last verified: 2023-01

Locations

CN (1)