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Recruiting NCT06982872

Comparison of the Diagnostic Performance of Different Artificial Intelligence Assisted Endocytoscopy for Colorectal Lesions

— Observational Endocytoscopy

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: artificial intelligence.
Who it may be relevant to
Registry conditions: Endocytoscopy. Basic parameters: No limits · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Overview

Colorectal cancer (colorectal cancer, CRC) is the third most common malignant tumor globally and the second leading cause of cancer-related deaths. Colonoscopy is considered the preferred method for screening colorectal cancer; early detection and removal of colorectal neoplasms can significantly reduce the incidence and mortality of colorectal cancer. To improve the diagnostic accuracy of endoscopy in colorectal lesions, many endoscopic techniques have been applied clinically, such as image-enhanced endoscopy, including narrow band imaging (narrow-band imaging, NBI), magnifying endoscopy, chromoendoscopy, confocal laser endoscopy, and endocytoscopy (EC). However, with the increasing number of endoscopic resections, the costs associated with the pathological diagnosis of resected specimens have risen year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to differentiate the nature of lesions during colonoscopy. Endocytoscopy is an ultra-high magnification endoscope that, when combined with chemical staining and narrowband imaging techniques, allows endoscopists to observe the nuclear morphology of colorectal lesions, the shape of glands, and the morphology of microvessels with the naked eye, thus avoiding pathological examination and achieving the goal of real-time biopsy in vivo. However, the accuracy of endocytoscopy images requires extensive experience accumulation to improve judgment, and there is a certain degree of subjectivity and error in the process of endoscopists making judgments. Therefore, to address this issue, clinical applications have proposed using artificial intelligence (AI) for computer-aided diagnosis. Currently, Japan has developed an endoscopic cytology auxiliary diagnostic system-EndoBRAIN, based on the Japanese population, which uses support vector machines to build model. The investigator's center has developed a deep learning-based endoscopic cytology AI auxiliary diagnostic system for Chinese populations to assist in determining the nature of colorectal lesions. There is currently a lack of comparative studies on the diagnostic performance of these two systems, so the investigator aim to conduct a clinical study to compare and analyze the differences between the two AI auxiliary diagnostic systems.

Interventions

  • Diagnostic test artificial intelligence
    Different AI assisted diagnostic systems are used to diagnose lesions.

Primary outcome measures

  • the sensitivity of two AI assisted diagnostic systems for diagnosing colorectal neoplasms [Time frame: 2025-12-31]
Secondary outcome measures (8)
  • the accuracy of two AI assisted diagnostic systems for diagnosing colorectal neoplasms [Time frame: 2025-12-31]
  • specificity of two AI assisted diagnostic systems for diagnosing colorectal neoplasms [Time frame: 2025-12-31]
  • positive predictive value of two AI assisted diagnostic systems for diagnosing colorectal neoplasms [Time frame: 2025-12-31]
  • negative predictive value of two AI assisted diagnostic systems for diagnosing colorectal neoplasms [Time frame: 2025-12-31]
  • the accuracy of two AI assisted diagnostic systems for diagnosing colorectal invasive cancer [Time frame: 2025-12-31]
  • The accuracy of two AI assisted diagnostic systems in diagnosing lesions of the rectoileal colon ≤5 mm [Time frame: 2025-12-31]
  • the high confidence diagnosis rate of two AI assisted diagnostic systems for diagnosing colorectal lesions [Time frame: 2025-12-31]
  • the diagnostic time of two artificial intelligence assisted diagnosis systems [Time frame: 2025-12-31]

Eligibility criteria

Inclusion criteria

  • colorectal lesions

Exclusion criteria

  • lesions lacking high-quality images;
  • Inflammatory bowel disease, familial adenomatous polyposis and other special diseases;
  • submucosal tumors;
  • Pathological diagnosis of Peutz-Jeghers polyps, juvenile polyps, lymphoma and other pathological types.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Study design

Observational model
Case-only

Study locations

China · 1 center
  • First Hospital of Jilin University — Changchun

Identifiers

NCT: NCT06982872 · 25K189-001

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗