An observational study in Ophthalmology, Artificial Intelligence and Hepatobiliary Disease, sponsored by Sun Yat-sen University. Status unknown at 1 site in China. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2023-01-12.
Sponsored by Sun Yat-sen University · Observational
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.
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.
613 studies on the registry are indexed under Digestive System Diseases; 117 are open to participants now.
This study's enrollment of 1,633 is above the median of 272 across 193 observational studies indexed under Digestive System Diseases.
Browse Digestive System Diseases studies →Sun Yat-sen University is the lead sponsor of 1,644 studies on the registry; 602 are open to participants now.
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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).
Exclusion Criteria:
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).
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).
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
Time frame: baseline
sensitivity and specificity of the deep learning system
The investigators will calculate the sensitivity and specifity of deep learning system
Time frame: baseline
This study is status unknown, as verified in Jan 2023. You cannot join it, but the record below documents what was studied.
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Sun Yat-sen University