A Prospective Study: Smart Phone Application for Measure Serum Bilirubin Through Sclera Images

Jaundice Clinical Trial

Official title:

A Prospective Study: A Computer Vision Model on a Smartphone Platform for Real-time Evaluation of Serum Bilirubin Through Sclera Images

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06421844
• Other study ID #: KY20242031-F-1
• Status: Recruiting
• Start date: April 20, 2024
• Est. completion date: July 10, 2024

Study information

• Verified date: May 2024
• Source: Air Force Military Medical University, China
• Contact: Yanglin Pan, MD
• Phone: 86-13991811225
• Email: yanglinpan[@]hotmail.com
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial Summary

The primary efficacy endpoints are the standard deviation and coefficient of determination (R2) between predicted and actual values for the bilirubin regression model, and the grading accuracy for the jaundice severity classification model. The secondary efficacy endpoint is the mean percentage error between predicted and actual bilirubin values. There are no relevant safety risks. Statistical differences for categorical variables (e.g., jaundice grading evaluation indicators) will be analyzed using the chi-square test or Fisher's exact probability test. For continuous variables (e.g., bilirubin prediction evaluation indicators), t-tests (normal distribution) or non-parametric tests (non-normal distribution) will be used. The 95% confidence interval for jaundice grading accuracy will be calculated using the Wilson method. The study duration is estimated to be 3 months.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 270
• Est. completion date: July 10, 2024
• Est. primary completion date: June 20, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: 14 Years and older

Eligibility

Inclusion Criteria:

Age 14 years or older. Subjects who are visiting the Gastroenterology Department of Xijing Hospital and will undergo liver function tests on the same day. The disease spectrum of the subjects mainly includes pancreatitis, pancreatic tumors, hepatobiliary stones, biliary tumors, and colonic polyps. Exclusion Criteria: Subjects with diseases that may cause abnormal changes in scleral color, such as glaucoma, Wilson's disease, pterygium, or scleritis. Subjects who have recently consumed a large amount of carotenoid-rich foods (such as oranges or carrots). Subjects who are unable to provide informed consent. Elimination Criteria: Subjects with incomplete scleral exposure due to limited eye movement or excessive tension during external eye examination. Subjects who are unable to understand the instructions for eye rotation during scleral examination or are unable to cooperate due to reasons such as poor hearing.

Related Conditions & MeSH terms

• Deep Learning
• Hyperbilirubinemia
• Jaundice

Locations

Country	Name	City	State
China	First Affiliated Hospital of Air Force Military Medical University	Xi'an	Shaanxi

Sponsors (1)

Lead Sponsor	Collaborator
Air Force Military Medical University, China

Country where clinical trial is conducted

China, 

References & Publications (6)

Bang JY, Navaneethan U, Hasan M, Hawes R, Varadarajulu S. Stent placement by EUS or ERCP for primary biliary decompression in pancreatic cancer: a randomized trial (with videos). Gastrointest Endosc. 2018 Jul;88(1):9-17. doi: 10.1016/j.gie.2018.03.012. Epub 2018 Mar 21. — View Citation

Bian Y, Zheng Z, Fang X, Jiang H, Zhu M, Yu J, Zhao H, Zhang L, Yao J, Lu L, Lu J, Shao C. Artificial Intelligence to Predict Lymph Node Metastasis at CT in Pancreatic Ductal Adenocarcinoma. Radiology. 2023 Jan;306(1):160-169. doi: 10.1148/radiol.220329. Epub 2022 Sep 6. — View Citation

Inamori G, Kamoto U, Nakamura F, Isoda Y, Uozumi A, Matsuda R, Shimamura M, Okubo Y, Ito S, Ota H. Neonatal wearable device for colorimetry-based real-time detection of jaundice with simultaneous sensing of vitals. Sci Adv. 2021 Mar 3;7(10):eabe3793. doi: 10.1126/sciadv.abe3793. Print 2021 Mar. — View Citation

Park JH, Yang MJ, Kim JS, Park B, Kim JH, Sunwoo MH. Deep-Learning-Based Smartphone Application for Self-Diagnosis of Scleral Jaundice in Patients with Hepatobiliary and Pancreatic Diseases. J Pers Med. 2021 Sep 18;11(9):928. doi: 10.3390/jpm11090928. — View Citation

Wu HL, Yao LW, Shi HY, Wu LL, Li X, Zhang CX, Chen BR, Zhang J, Tan W, Cui N, Zhou W, Zhang JX, Xiao B, Gong RR, Ding Z, Yu HG. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health. 2023 Nov;5(11):e812-e820. doi: 10.1016/S2589-7500(23)00160-7. Epub 2023 Sep 27. — View Citation

Xiao W, Huang X, Wang JH, Lin DR, Zhu Y, Chen C, Yang YH, Xiao J, Zhao LQ, Li JO, Cheung CY, Mise Y, Guo ZY, Du YF, Chen BB, Hu JX, Zhang K, Lin XS, Wen W, Liu YZ, Chen WR, Zhong YS, Lin HT. Screening and identifying hepatobiliary diseases through deep learning using ocular images: a prospective, multicentre study. Lancet Digit Health. 2021 Feb;3(2):e88-e97. doi: 10.1016/S2589-7500(20)30288-0. — View Citation

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Loss of predicted bilirubin levels	Standard deviation and mean average percentage error between predicted and actual bilirubin levels for the bilirubin regression model.	Immediately after test completion
Primary	Classification accuracy	Classification accuracy for the jaundice severity grading model	Immediately after test completion