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

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT05576506

Enrollment

Registered

2022-10-12

Start date

2022-10-08

Completion date

2022-12-31

Last updated

2024-07-31

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Colorectal Adenoma, Colorectal Neoplasms, Colorectal Polyp, Colorectal SSA

Keywords

Hyperspectral imaging, Artifitial intelligence, colorectal cancer

Brief 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.

Detailed 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to 75 Years

Healthy volunteers

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

Design outcomes

Primary

Measure	Time frame	Description
Negative predictive values(NPV)	1 year	Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)\*100%
Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer	1 year	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%
AUC (95% CI)	1 year	area under the receiver operating characteristic curve (AUC)
Sensitivity	1 year	Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) \* 100%.
Specificity	1 year	Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))\*100%

Secondary

Measure	Time frame	Description
To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition	1 year	To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition

Countries

China

Outcome results