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Artificial intelligence (AI) shows promise in detecting colorectal neoplasia, but its effectiveness for subtle flat lesions, like non-granular laterally spreading tumors, requires further investigation due to limited training data.

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Flat advanced colorectal neoplasia has a high miss rate, despite quality improvement initiatives.
  • Flat lesions are associated with increased miss rates and potentially more aggressive behavior.
  • Artificial intelligence (AI) has improved general adenoma detection rates.

Purpose of the Study:

  • To review the evidence on AI's role in detecting flat colorectal neoplasia.
  • To assess AI accuracy for flat lesions, considering varying morphologies like laterally spreading tumors (LSTs).
  • To evaluate the impact of training set composition on AI performance for flat neoplasia.

Main Methods:

  • Review of published trials and AI systems for colorectal neoplasia detection.
  • Analysis of AI performance specifically for flat colorectal lesions.
  • Focus on the inclusion of laterally spreading tumors (LSTs) in AI training and validation datasets.

Main Results:

  • AI consistently increases adenoma detection rates across various lesion types.
  • Uncertainty remains regarding AI's ability to reduce the miss rate of flat advanced neoplasia.
  • Limited data on flat lesions, especially non-granular LSTs, in AI training sets may impact accuracy.

Conclusions:

  • AI shows potential for improving colorectal neoplasia detection, but specific challenges exist for flat lesions.
  • Further research is needed to validate AI performance on diverse flat neoplasia morphologies.
  • The composition of training datasets is crucial for AI's accuracy in identifying challenging flat colorectal lesions.