Evaluation of Artificial Intelligence System in Diagnosis of Colorectal Tubular Adenoma Lesions

Renmin Hospital of Wuhan University4,200 enrolled

Overview

This study is a prospective,multi-center and observational clinical study.Investigators would like to innovatively construct a "trinity" database of colorectal tubular adenomas based on white light - magnifying chromo - pathological images.It simulates the decision - making logic of doctors, and based on the multimodal endoscopic LAFEQ method previously proposed, develop a multimodal deep - learning diagnostic model for colon adenomas and an interpretable risk prediction model for intestinal adenomas. While achieving high - precision auxiliary treatment decisions, clearly present the decision - making basis, and break through the limitation of poor interpretability of previous medical imaging AI models.

Study Type

OBSERVATIONAL

Enrollment

4,200

Conditions

Colorectal Adenoma Artificial Intelligence (AI) in Diagnosis

Interventions

AI models with NBIDEVICE

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: * Patients aged ≥ 18 years, who need to undergo colonoscopy, regardless of gender. * Voluntarily sign the informed consent form * Promise to abide by the research procedures and cooperate in the implementation of the entire research process. Exclusion Criteria: * Patients who has a history of abdominal or pelvic surgery or radiotherapy in the past; * Patients who has definite active lower gastrointestinal bleeding. * Existing or suspected hereditary colorectal polyposis, inflammatory bowel disease; * Uncontrolled hypertension (systolic blood pressure \> 160 mmHg or diastolic blood pressure \> 95 mmHg after standardized treatment) * There is a history of stroke, coronary artery disease, or vascular disease; * Pregnant; * Intestinal preparation cannot be carried out.

Outcomes

Primary Outcomes

The accuracy rate of diagnosing adenomas

The accuracy rate of the endoscopic AI model in diagnosing adenomas (presence or absence of adenomas, number of adenomas, advanced adenomas).

Time frame: during endoscopy

Secondary Outcomes

The prediction for the disease risk level

The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level.