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CLASSICA: Validating artificial intelligence in classifying cancer in real time during surgery.

A Moynihan1, N Hardy1, J Dalli1

  • 1University College Dublin, Dublin, Ireland.

Colorectal Disease : the Official Journal of the Association of Coloproctology of Great Britain and Ireland
|November 7, 2023
PubMed
Summary

Artificial intelligence (AI) analysis of indocyanine green (ICG) fluorescence shows promise for classifying rectal polyps during endoscopic evaluation. This study validates AI

Keywords:
artificial intelligencefluorescence guided surgerypolyp classificationrectal polyprectal tumour

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Area of Science:

  • Gastroenterology and Surgical Oncology
  • Medical Imaging and Artificial Intelligence

Background:

  • Accurate pre-excision profiling of rectal polyps is challenging, impacting treatment decisions.
  • Indocyanine green (ICG) fluorescence perfusion signals, analyzed mathematically and with artificial intelligence (AI), offer potential for endoscopic tissue characterization.
  • Current methods rely on biopsies, which have limitations in real-time intra-operative assessment.

Purpose of the Study:

  • To validate the generalizability, usability, and accuracy of AI-driven analysis of ICG fluorescence for classifying rectal polyps and tumors.
  • To develop clinical-grade software for real-time, AI-assisted endoscopic tissue characterization.
  • To compare AI-based classification and guided intervention with the current standard of care.

Main Methods:

  • Prospective, unblinded, multicentre European observational study (CLASSICA).
  • Enrollment of 600 patients undergoing transanal endoscopic evaluation for significant rectal polyps/tumors across at least five clinical sites.
  • Centralized analysis of ICG fluorescence video recordings with progressive development of local, automated AI classification software.

Main Results:

  • The study is designed to validate AI analysis of ICG fluorescence for endoscopic classification.
  • AI-based classification will be compared against standard care, including biopsies and final pathology.
  • The study will establish the performance of AI in differentiating benign from malignant rectal lesions.

Conclusions:

  • The CLASSICA study will confirm the utility of AI in analyzing ICG fluorescence for endoscopic polyp and tumor classification.
  • Future research will compare AI-guided interventions (biopsy/excision) with traditional methods, assessing outcomes like marginal clearance and recurrence.
  • AI-based endoscopic characterization holds potential to refine surgical strategies and improve patient outcomes.