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AI in Predicting Polyp Pathology and Endoscopic Classification

NCT06773832 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 400

Last updated 2025-01-14

No results posted yet for this study

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.

Conditions

  • Colorectal Polyps

Interventions

DIAGNOSTIC_TEST

Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic Classification

During the AI model development phase, the aim is to include as many samples as possible. Given the focus on the diagnostic accuracy of serrated lesions, we retrospectively collected approximately 400 cases serrated lesions with pathological diagnosis by the department of pathology at Peking Union Medical College Hospital to date. Additionally, we matched with 400 cases each of hyperplastic polyps, conventional adenomas, and early-stage colorectal cancer, totaling approximately 1600 cases. The model employs mainstream AI classification algorithms to construct the model and compare the predictive performance of different models. Utilizing the dataset established in the first phase, which contains static images of polyp lesions along with their corresponding pathological diagnosis and endoscopic classifications, we developed and optimized the AI model. Then the model will be be compared with endoscopists in a prospective cohort to investigate the efficacy.

Sponsors & Collaborators

  • Peking Union Medical College Hospital

    lead OTHER

Principal Investigators

  • Dong Wu, MD · Peking Union Medical College Hospital

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-01-31
Primary Completion
2026-12-31
Completion
2026-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06773832 on ClinicalTrials.gov