Detection of Jaundice From Ocular Images Via Deep Learning

Detection of Jaundice From Ocular Images Via Deep Learning : a Prospective, Multicenter Cohort Study

Status

UNKNOWN

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05682105

Enrollment

1633

Registered

2023-01-12

Start date

2018-12-01

Completion date

2023-06-30

Last updated

2023-01-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Hepatobiliary Disease, Ophthalmology

Keywords

Ophthalmology, Artificial Intelligence, ocualr image, Jaundice

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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Yes

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

Design outcomes

Primary

Measure	Time frame	Description
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

Measure	Time frame	Description
sensitivity and specificity of the deep learning system	baseline	The investigators will calculate the sensitivity and specifity of deep learning system

Countries

China

Outcome results