Detection of Jaundice From Ocular Images Via Deep Learning

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

1633 participants

Study Classification

OBSERVATIONAL

Study Start Date

2018-12-01

Study Completion Date

2023-06-30

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Ophthalmology Artificial Intelligence Hepatobiliary Disease

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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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).

No interventions assigned to this group

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).

No interventions assigned to this group

Eligibility Criteria

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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.