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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT05576506
Other study ID # 2022-SDU-QILU-G003
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date October 8, 2022
Est. completion date December 31, 2023

Study information

Verified date October 2022
Source Shandong University
Contact Xiuli Zuo, MD,PhD
Phone 15588818685
Email zuoxiuli@sina.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.


Description:

Prospectively collect the hyperspectral image information of ordinary colonoscopic biopsy tissue. The colonoscopic biopsy tissue is from the Endoscopy Center of Qilu Hospital of Shandong University. The hyperspectral images are marked based on the biopsy pathological results, and the deep convolutional neural network (DCNN) model is used. With training and verification, develop the Hyperspectral Imaging Artificial Intelligence Diagnostic System (HSIAIDS) .A portion of colonoscopic biopsy tissue will be collected as a prospective test set to prospectively test the diagnostic performance of the HSIAIDS algorithm.


Recruitment information / eligibility

Status Recruiting
Enrollment 50
Est. completion date December 31, 2023
Est. primary completion date December 31, 2023
Accepts healthy volunteers No
Gender All
Age group 18 Years to 75 Years
Eligibility Inclusion Criteria: - patients aged 18-75 years who undergo the colonoscopy examination and biopsy Exclusion Criteria: - patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in colonoscopy - patients with previous surgical procedures on the gastrointestinal tract. - patients with contraindications to biopsy - patients who refuse to sign the informed consent form

Study Design


Locations

Country Name City State
China Qilu hosipital Jinan Shandong

Sponsors (1)

Lead Sponsor Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer Accuracy of hyperspectral imaging (HSI) artificial intelligence model to identify colorectal hyperplastic polyp, adenoma, SSL and colorectal cancer. Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100% 1 year
Primary Sensitivity Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%. 1 year
Primary Specificity Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))*100% 1 year
Primary Negative predictive values(NPV) Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)*100% 1 year
Primary AUC (95% CI) area under the receiver operating characteristic curve (AUC) 1 year
Secondary To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition 1 year
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