Menu
Recruiting NCT07073430

Application Evaluation Research on the Artificial Intelligence-assisted Support System for the Diagnosis of Colorectal Tubular Adenoma Lesions

— Observational Colorectal Adenoma Artificial Intelligence (AI)

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: AI models with NBI.
Who it may be relevant to
Registry conditions: Colorectal Adenoma, Artificial Intelligence (AI). Basic parameters: from 18 years · 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 →

Unsure about the terms? Read our patient guide →

Overview

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

  • Device AI models with NBI
    AI models for detecting intestinal adenoma in magnifying endoscopy with NBI.

Primary outcome measures

  • The accuracy rate of diagnosing adenomas [Time frame: during endoscopy]
Secondary outcome measures (1)
  • The prediction for the disease risk level [Time frame: during endoscopy]

Eligibility criteria

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.

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

Healthy volunteers: No

Study design

Observational model
Case-crossover

Study locations

China · 1 center
  • Renmin Hospital of Wuhan University — Wuhan

Publications

  • Li J, Zhu Y, Dong Z, He X, Xu M, Liu J, Zhang M, Tao X, Du H, Chen D, Huang L, Shang R, Zhang L, Luo R, Zhou W, Deng Y, Huang X, Li Y, Chen B, Gong R, Zhang C, Li X, Wu L, Yu H. Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: A case-control study. EClinicalMedicine. 2022 Mar 30;46:101366. doi: 10.1016/j.eclinm.2022.101366 PMID 35521066
  • Dekker E, Rex DK. Advances in CRC Prevention: Screening and Surveillance. Gastroenterology. 2018 May;154(7):1970-1984. doi: 10.1053/j.gastro.2018.01.069. Epub 2018 Feb 15. PMID 29454795
  • Zhou T, Cheng Q, Lu H, Li Q, Zhang X, Qiu S. Deep learning methods for medical image fusion: A review. Comput Biol Med. 2023 Jun;160:106959. doi: 10.1016/j.compbiomed.2023.106959. Epub 2023 Apr 20. PMID 37141652
  • Tempany CM, Jayender J, Kapur T, Bueno R, Golby A, Agar N, Jolesz FA. Multimodal imaging for improved diagnosis and treatment of cancers. Cancer. 2015 Mar 15;121(6):817-27. doi: 10.1002/cncr.29012. Epub 2014 Sep 9. PMID 25204551
  • Wang Y, Zhen L, Tan TE, Fu H, Feng Y, Wang Z, Xu X, Goh RSM, Ng Y, Calhoun C, Tan GSW, Sun JK, Liu Y, Ting DSW. Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis. IEEE Trans Med Imaging. 2024 May;43(5):1945-1957. doi: 10.1109/TMI.2024.3352602. Epub 2024 May 2. PMID 38206778
  • van der Velden BHM, Kuijf HJ, Gilhuijs KGA, Viergever MA. Explainable artificial intelligence (XAI) in deep learning-based medical image analysis. Med Image Anal. 2022 Jul;79:102470. doi: 10.1016/j.media.2022.102470. Epub 2022 May 4. PMID 35576821
  • Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform. 2022 Mar 10;23(2):bbab569. doi: 10.1093/bib/bbab569. PMID 35089332
  • Haight TJ, Eshaghi A. Deep Learning Algorithms for Brain Imaging: From Black Box to Clinical Toolbox? Neurology. 2023 Mar 21;100(12):549-550. doi: 10.1212/WNL.0000000000206808. Epub 2023 Jan 13. No abstract available. PMID 36639238

Identifiers

NCT: NCT07073430 · WDRY2024-K153

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗