## Protocol Section
### Identification Module
**NCT ID:** NCT06421844

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

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

#### Organization Study ID Info
**ID:** KY20242031-F-1
#### Organization
**Class:** OTHER
**Full Name:** Air Force Military Medical University, China
### Status Module
#### Completion Date
**Date:** 2024-07-10

**Type:** ESTIMATED

#### Expanded Access Info
#### Last Update Post Date
**Date:** 2024-05-20

**Type:** ACTUAL

**Last Update Submit Date:** 2024-05-15

**Overall Status:** RECRUITING

#### Primary Completion Date
**Date:** 2024-06-20

**Type:** ESTIMATED

#### Start Date
**Date:** 2024-04-20

**Type:** ACTUAL

**Status Verified Date:** 2024-05

#### Study First Post Date
**Date:** 2024-05-20
**Type:** ACTUAL
**Study First Submit Date:** 2024-05-15
**Study First Submit QC Date:** 2024-05-15
### Sponsor Collaborators Module

#### Lead Sponsor

**Class:** OTHER

**Name:** Air Force Military Medical University, China

#### Responsible Party

**Investigator Affiliation:** Air Force Military Medical University, China

**Investigator Full Name:** Yanglin Pan

**Investigator Title:** Professor

**Type:** PRINCIPAL_INVESTIGATOR

### Oversight Module

**Is FDA Regulated Device:** False

**Is FDA Regulated Drug:** False

**Oversight Has DMC:** False

### Description Module

**Brief 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.

### Conditions Module

**Conditions:**

- Jaundice

- Deep Learning

- Hyperbilirubinemia

- Sclera

**Keywords:**

- Smart phone

- Hyperbilirubinemia

- Real-time

- Deep learning

- Sclera

### Design Module

#### Design Info

**Observational Model:** COHORT

**Time Perspective:** PROSPECTIVE

#### Enrollment Info

**Count:** 270

**Type:** ESTIMATED

**Patient Registry:** True

**Study Type:** OBSERVATIONAL

**Target Duration:** 2 Weeks

### Arms Interventions Module

#### Arm Group 1

**Description:** The cohort consists of at least 270 subjects consecutively enrolled from the Department of Gastroenterology at Xijing Hospital. The subjects will be followed prospectively over a period of time to collect data on their age, medical information, scleral images, and liver function test results. The cohort includes patients with various liver diseases and varying degrees of jaundice severity.

**Label:** Real-Time Scleral Jaundice Evaluation in Gastroenterology Cohort

### Outcomes Module

#### Primary Outcomes

**Description:** Standard deviation and mean average percentage error between predicted and actual bilirubin levels for the bilirubin regression model.

**Measure:** Loss of predicted bilirubin levels

**Time Frame:** Immediately after test completion

**Description:** Classification accuracy for the jaundice severity grading model

**Measure:** Classification accuracy

**Time Frame:** Immediately after test completion

### Eligibility Module

**Eligibility Criteria:** 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.

**Healthy Volunteers:** True

**Minimum Age:** 14 Years

**Sampling Method:** NON_PROBABILITY_SAMPLE

**Sex:** ALL

**Standard Ages:**

- CHILD

- ADULT

- OLDER_ADULT

**Study Population:** Study Population Description:

The study will include a total of 270 patients, both male and female, aged 14 years and above. The participants should not have any pre-existing conditions that may affect the color of their sclera. They should be able to follow instructions and complete the scleral imaging procedure. The patient population will primarily consist of individuals undergoing treatment for colorectal polyps, pancreatitis, pancreatic tumors, hepatobiliary stones, or biliary tract tumors.

Screening Procedure:

Potential participants will be screened using oral queries to assess their eligibility based on the inclusion and exclusion criteria. A brief ocular examination will be performed to rule out any apparent ocular surface disorders. Eligible participants will be provided with a detailed explanation of the study objectives, procedures, and potential risks and benefits. Written informed consent will be obtained from all participants prior to enrollment in the study.

### Contacts Locations Module

#### Central Contacts

**Contact 1:**

**Email:** yanglinpan@hotmail.com

**Name:** Yanglin Pan, MD

**Phone:** 86-13991811225

**Role:** CONTACT

#### Locations

**Location 1:**

**City:** Xi'an

**Contacts:**

***Contact 1:***

- **Email:** yanglinpan@hotmail.com

- **Name:** Yanglin Pan

- **Phone:** 13991811225

- **Role:** CONTACT

***Contact 2:***

- **Name:** Xintain Yang

- **Role:** SUB_INVESTIGATOR

**Country:** China

**Facility:** First Affiliated Hospital of Air Force Military Medical University

**State:** Shaanxi

**Status:** RECRUITING

**Zip:** 710032

### IPD Sharing Statement Module

**IPD Sharing:** NO

### References Module

#### References

**Citation:** 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.

**PMID:** 29574126

**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.

**PMID:** 33658197

**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.

**PMID:** 36066369

**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.

**PMID:** 37775472

**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.

**PMID:** 34575705

**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.

**PMID:** 33509389

## Derived Section

### Condition Browse Module - Ancestors

- ID: D000010335

- Term: Pathologic Processes

- ID: D000012877

- Term: Skin Manifestations

### Condition Browse Module - Browse Branches

- Abbrev: BC23

- Name: Symptoms and General Pathology

- Abbrev: All

- Name: All Conditions

### Condition Browse Module - Browse Leaves

- ID: M10595

- Name: Jaundice

- Relevance: HIGH

- As Found: Jaundice

- ID: M9983

- Name: Hyperbilirubinemia

- Relevance: HIGH

- As Found: Hyperbilirubinemia

- ID: M15680

- Name: Skin Manifestations

- Relevance: LOW

- As Found: Unknown

### Condition Browse Module - Meshes

- ID: D000007565

- Term: Jaundice

- ID: D000006932

- Term: Hyperbilirubinemia

### Intervention Browse Module - Browse Branches

- Abbrev: Hemat

- Name: Hematinics

- Abbrev: All

- Name: All Drugs and Chemicals

### Intervention Browse Module - Browse Leaves

- ID: M11110

- Name: Liver Extracts

- Relevance: LOW

- As Found: Unknown

- ID: M4949

- Name: Bilirubin

- Relevance: LOW

- As Found: Unknown

### Misc Info Module

- Version Holder: 2024-05-31
