AI in Predicting Polyp Pathology and Endoscopic Classification

January 12, 2025 updated by: Peking Union Medical College Hospital

Artificial Intelligence Predicts the Pathology and Endoscopic Classification of Colorectal Polyps During Colonoscopy

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Study Type

Observational

Enrollment (Estimated)

400

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
      • Beijing, China, 100730
        • Recruiting
        • Peking Union Medical College Hospital
        • Contact:
        • Contact:
          • Wenmo Hu, MD

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

Yes

Sampling Method

Probability Sample

Study Population

Patients aged 18 years or older undergoing routine colonoscopy screening

Description

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 Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients aged 18 years or older undergoing routine colonoscopy screening
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Accuracy of Optical Diagnosis for Colorectal Polyps
Time Frame: 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.
2 years

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Other Assessment Parameters of Optical Diagnosis
Time Frame: 2 years
Including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Optical Diagnosis
2 years
Accuracy in Determining Endoscopic Classification of Colorectal Polyps
Time Frame: 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.
2 years
Other Assessment Parameters in Determining Endoscopic Classification
Time Frame: 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
2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Peking Union Medical College Hospital

Investigators

  • Principal Investigator: Dong Wu, MD, Peking Union Medical College Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

January 1, 2025

Primary Completion (Estimated)

December 1, 2026

Study Completion (Estimated)

December 1, 2026

Study Registration Dates

First Submitted

January 3, 2025

First Submitted That Met QC Criteria

January 12, 2025

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

January 12, 2025

Last Verified

December 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • K7281
  • BRWEP2024W034010100 (Other Grant/Funding Number: Excellence in Clinical Research Program for Research Wards of Beijing)

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

YES

IPD Plan Description

All IPD collected throughout the trial

IPD Sharing Time Frame

Beginning 3 months after publication with no end date

IPD Sharing Access Criteria

Any investigators who wish to utilize the data for pertinent research with an appropriate request

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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