Clinical trial · Interventional
CAD EYE Detection of Remaining Lesions After EMR
Accuracy of CAD Eye in the Detection of Colonic Remaining Lesions After Endoscopic Mucosal Resection: a Pilot Study
NCT05542030CI-TRIAL-00070052unknownN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
In the last decade, many innovative systems have been developed to support and improve the diagnosis accuracy during endoscopic studies. CAD-Eye™ (Fujifilm, Tokyo, Japan) is a computer-assisted diagnostic (CADx) system that uses artificial intelligence for the detection and characterization of polyps during colonoscopy. However, the accuracy of CAD-Eye™ in the recognition of remaining lesions after endoscopic mucosal resection (EMR) has not been broadly evaluated. Finally, based on the importance of complete resection of the colonic mucosal lesions, namely suspicious high-grade dysplasia or early invasive cancer, the investigators aimed to assess the accuracy of CAD-Eye™ in the detection of remaining lesions after the procedure.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Colorectal Dysplasia | Colorectal Intraepithelial Neoplasia | ALIAS | 0.90 |
| Colorectal Neoplasms | Colorectal Neoplasm | ONTOLOGY_EXACT | 0.98 |
Interventions
Interventions (3)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| EMR with CAD-Eye™ | Diagnostic Test | — | UNRESOLVED |
| EMR without CAD-Eye™ | Diagnostic Test | — | UNRESOLVED |
| Follow-up colonoscopy with CAD-Eye™ | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- EXPERIMENTAL
- label
- Endoscopic mucosal resection + CAD-Eye™
- description
- This group constitutes patients with lesions suggestive of high-grade dysplasia or early invasive cancer approached with endoscopic mucosal resection, subjected to colonoscopy + CAD-Eye™ system evaluation for the detection of remaining malignant tissue. For this group, the investigators used as a complement tool an AI system (CAD-Eye™) for the detection of remaining lesions immediately after EMR and in a three-month follow-up.
- interventionNames
- Diagnostic Test: EMR with CAD-Eye™
- Diagnostic Test: Follow-up colonoscopy with CAD-Eye™
- type
- ACTIVE_COMPARATOR
- label
- Endoscopic mucosal resection without CAD Eye
- description
- This group constitutes patients with lesions suggestive of high-grade dysplasia or early invasive cancer approached with endoscopic mucosal resection and subjected to colonoscopy. The detection of remaining lesions immediately after EMR is based on the visual impression of the expert. For this group, the investigators used as a complement tool an AI system (CAD-Eye™) only for the evaluation of the post-procedure scar to detect remaining lesions in the three-month follow-up.
- interventionNames
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
- Maximum age
- 90 Years
Show eligibility criteria text
Inclusion Criteria: * Patients referred to our center with an indication of colonoscopy and EMR for the treatment of lesions suspicious of high-grade dysplasia and early invasive cancer. * Patients who authorize EMR and colonoscopy. * Signed informed consent Exclusion Criteria: * Any clinical condition which makes EMR inviable. * Poor bowel preparation score defined as the total Boston bowel preparation score (BBPS) \<6 and the right-segment score \<2 * Patients with more than one previous EMR * Lost on a three-month follow-up after EMR * Pregnancy or nursing
References
Publications (6)
- BACKGROUNDKliegis L, Obst W, Bruns J, Weigt J. Can a Polyp Detection and Characterization System Predict Complete Resection? Dig Dis. 2022;40(1):115-118. doi: 10.1159/000516974. Epub 2021 May 6. PMID 33940578
- BACKGROUNDYoshida N, Inoue K, Tomita Y, Kobayashi R, Hashimoto H, Sugino S, Hirose R, Dohi O, Yasuda H, Morinaga Y, Inada Y, Murakami T, Zhu X, Itoh Y. An analysis about the function of a new artificial intelligence, CAD EYE with the lesion recognition and diagnosis for colorectal polyps in clinical practice. Int J Colorectal Dis. 2021 Oct;36(10):2237-2245. doi: 10.1007/s00384-021-04006-5. Epub 2021 Aug 18. PMID 34406437
- BACKGROUNDDumoulin FL, Hildenbrand R. Endoscopic resection techniques for colorectal neoplasia: Current developments. World J Gastroenterol. 2019 Jan 21;25(3):300-307. doi: 10.3748/wjg.v25.i3.300. PMID 30686899
- BACKGROUNDNeumann H, Kreft A, Sivanathan V, Rahman F, Galle PR. Evaluation of novel LCI CAD EYE system for real time detection of colon polyps. PLoS One. 2021 Aug 26;16(8):e0255955. doi: 10.1371/journal.pone.0255955. eCollection 2021. PMID 34437563
- BACKGROUNDMin M, Deng P, Zhang W, Sun X, Liu Y, Nong B. Comparison of linked color imaging and white-light colonoscopy for detection of colorectal polyps: a multicenter, randomized, crossover trial. Gastrointest Endosc. 2017 Oct;86(4):724-730. doi: 10.1016/j.gie.2017.02.035. Epub 2017 Mar 9. PMID 28286095
- BACKGROUNDTate DJ, Desomer L, Klein A, Brown G, Hourigan LF, Lee EY, Moss A, Ormonde D, Raftopoulos S, Singh R, Williams SJ, Zanati S, Byth K, Bourke MJ. Adenoma recurrence after piecemeal colonic EMR is predictable: the Sydney EMR recurrence tool. Gastrointest Endosc. 2017 Mar;85(3):647-656.e6. doi: 10.1016/j.gie.2016.11.027. Epub 2016 Nov 28. PMID 27908600