Artificial Intelligence (AI), Colorectal Adenoma
Conditions
Brief summary
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.
Interventions
DEVICEAI models with NBI
AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.
Sponsors
Renmin Hospital of Wuhan University
Beijing Friendship Hospital, Captial Medical University
Air Force Military Medical University, China
The Sixth Affiliated Hospital, Sun Yat-sen University
Army Medical University, China
Guizhou Provincial People's Hospital
Shengjing Hospital
The Second Medical Center, Chinese PLA General Hospital
Zhejiang University
Shandong University
Study design
Observational model
CASE_CROSSOVER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The accuracy rate of diagnosing adenomas | during endoscopy | The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The prediction for the disease risk level | during endoscopy | The prediction rate of the interpretable artificial intelligence-assisted diagnosis model for the disease risk level. |
Countries
China
Contacts
CONTACTMingkai Chen
Outcome results
None listed