Neoplasms

Digestive System Diseases

All Conditions

Symptoms and General Pathology

Colorectal Neoplasms

Adenoma

Polyps

Intestinal Neoplasms

Gastrointestinal Neoplasms

Digestive System Neoplasms

Gastrointestinal Diseases

Colonic Diseases

Intestinal Diseases

Rectal Diseases

Neoplasms, Glandular and Epithelial

Neoplasms by Histologic Type

After the patient has passed the screening, a routine colonoscopy will be performed, and the target tissue with suspected inflammation or neoplasia will be biopsied. The clinical investigators use the hyperspectral microscope to collect image information of the biopsy tissue in the endoscopy room. After collecting information, biopsy specimens will be routinely processed and sent for pathological diagnosis.

Xiuli Zuo, MD,PhD

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.

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.

Biopsies from the colonic or rectal polyps will be prospectively collected hyperspectral images and conducted for histology examination and model validation.

Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy

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%

Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.

Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))\*100%

Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)\*100%

area under the receiver operating characteristic curve (AUC)

To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition

Study title	NCT ID	Conditions	Interventions	Eligibility	Organization	Link
Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy	NCT05576506	Colorectal Neop... Colorectal Poly... Colorectal Aden... Colorectal SSA		18 Years - 75 Years	Shandong University
Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy	NCT05576506	Colorectal Neop... Colorectal Poly... Colorectal Aden... Colorectal SSA		18 Years - 75 Years	Shandong University

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