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Methods for improving colorectal cancer annotation efficiency for artificial intelligence-observer training.

Matthew Grudza1, Brandon Salinel2, Sarah Zeien3

  • 1School of Biological Health and Systems Engineering, Arizona State University, Tempe, AZ 85287, United States.

World Journal of Radiology
|January 5, 2024
PubMed
Summary

Sparse annotation significantly reduces colorectal cancer (CRC) detection AI model training time without compromising accuracy. This method efficiently establishes ground truth for AI development.

Keywords:
Artificial intelligenceColorectal cancerDetection

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Oncology

Background:

  • Missing occult cancer lesions are a primary cause of diagnostic errors in radiology.
  • Artificial intelligence (AI) as a second observer offers an economical solution to reduce these errors.
  • Large annotated datasets are crucial for effective AI model training in cancer detection.

Purpose of the Study:

  • To compare skip-slice annotation and AI-initiated annotation for decreasing AI model training time.
  • To evaluate the efficiency of different annotation methods in establishing ground truth for AI development.

Main Methods:

  • Developed a 2D U-Net AI model for colorectal cancer (CRC) detection.
  • Employed an ensemble of 2D U-Nets for enhanced performance.
  • Trained and tested models using The Cancer Imaging Archive dataset, comparing skip-slice and AI-initiated annotation techniques.

Main Results:

  • Sparse annotation, particularly skipping two slices, significantly reduced annotation time (P < 0.001).
  • Reduced annotation (up to 2/3) did not negatively impact AI model sensitivity or false positive rates.
  • AI-initiated annotation provided minimal time reduction, even with an ensemble AI approach.

Conclusions:

  • Sparse annotation is an efficient technique for reducing the time required to establish ground truth for AI models.
  • This method supports faster development of AI tools for improved cancer lesion detection.