Colorectal Neoplasms Clinical Trial
Official title:
Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy
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.
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 |
Country | Name | City | State |
---|---|---|---|
China | Qilu hosipital | Jinan | Shandong |
Lead Sponsor | Collaborator |
---|---|
Shandong University |
China,
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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