AI in Predicting Polyp Pathology and Endoscopic Classification

Recruiting

• Conditions: Colorectal Polyps

• Registration Number: NCT06773832
• Lead Sponsor: Peking Union Medical College Hospital

Brief Summary

Background: Colonoscopy with optical diagnosis based on the appearance of polyps can guide the selection of endoscopic treatment methods, reduce unnecessary polypectomy procedures and the need for tissue pathological diagnosis, and formulate follow-up strategies in a timely manner \[1\]. This approach significantly alleviates the economic burden on patients and the healthcare system and can effectively ease the tension on clinical resources \[2\]. Various endoscopic polyp classification methods, including Pit Pattern \[3\], NICE \[4\], WASP \[5\], and MS \[6\], are used to determine pathological types. However, mastering these classification methods requires endoscopists to undergo extensive training, and due to the inherent flaws in each method, no single endoscopic classification method can accurately diagnose all types of polyps to meet the requirements of optical diagnosis. This limitation has hindered the widespread application of optical diagnosis in clinical practice \[7\]. The application of artificial intelligence technology in this field, known as computer-aided diagnosis (CADx), has seen rapid development in recent years. Numerous large-scale, prospective studies have demonstrated that the accuracy of CADx technology for optical diagnosis of minute lesions (\<5mm) has essentially met the threshold set by European and American endoscopy societies for optical diagnosis \[8,9\]. However, the diagnostic efficacy of CADx for polyps ≥5mm remains unclear. Moreover, current research is mostly limited to distinguishing between common adenomas and hyperplastic polyps, with little attention given to serrated lesions, which are also precancerous lesions and progress even more rapidly, and are more challenging for endoscopists to assess. These reasons prevent CADx from being widely applied in clinical practice for real-time accurate judgment of polyp pathological types.

Detailed Description

Not available

Study Timeline

• Status Verified: 2024-12
• Study Start: 2025-01
• Study First Submitted: 2025-01-03
• Study First Submitted QC: 2025-01-12
• Last Update Submitted: 2025-01-12
• Study First Posted: 2025-01-14
• Last Update Posted: 2025-01-14
• Primary Completion: 2026-12
• Study Completion: 2026-12

Recruitment & Eligibility

• Status: RECRUITING
• Sex: All
• Target Recruitment: 400

Inclusion Criteria

1. Outpatients or inpatients undergoing routine colonoscopy screening at the endoscopy centers of multicenter hospitals;
2. Aged 18 years or older;
3. Have understanding of the study content and have signed the informed consent form.

Exclusion Criteria

1. Gastroparesis or gastric outlet obstruction;
2. Known or suspected intestinal obstruction or perforation;
3. Severe chronic renal failure (creatinine clearance less than 30 mL/minute);
4. Severe congestive heart failure (New York Heart Association Class III or IV);
5. Currently pregnant or breastfeeding;
6. Toxic colitis or megacolon;
7. Poorly controlled hypertension (systolic blood pressure greater than 180 mmHg and/or diastolic blood pressure greater than 100 mmHg);
8. Moderate or massive active gastrointestinal bleeding (>100 mL/day);
9. Significant psychiatric or psychological illness;
10. Allergy to medications used for bowel preparation;
11. Patients who have undergone colorectal surgery.

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy of Optical Diagnosis for Colorectal Polyps	2 years	The accuracy of the AI model's optical diagnosis is compared with that of endoscopists, with pathological diagnosis serving as the gold standard.

Secondary Outcome Measures

Name	Time	Method
Other Assessment Parameters of Optical Diagnosis	2 years	Including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Optical Diagnosis
Accuracy in Determining Endoscopic Classification of Colorectal Polyps	2 years	Using the endoscopic classification judgment of experienced endoscopists as the gold standard, the study investigates the accuracy of the AI model in determining the endoscopic classification of lesions. The endoscopic classifications include Pit Pattern, CP, NICE, JNET, WASP, and MS.
Other Assessment Parameters in Determining Endoscopic Classification	2 years	The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the AI Model in determining endoscopic classification of colorectal polyps

Trial Locations

Locations (1)

Peking Union Medical College Hospital; 🇨🇳 Beijing, China