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A primer on artificial intelligence in pancreatic imaging.

Taha M Ahmed1, Satomi Kawamoto1, Ralph H Hruban2

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Artificial intelligence (AI) in medical imaging, including deep learning and radiomics, offers improved disease detection and characterization. This review assesses AI

Keywords:
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Area of Science:

  • Radiology and medical imaging.
  • Application of artificial intelligence in healthcare.

Background:

  • Artificial intelligence (AI) is poised to revolutionize medical imaging analysis.
  • Deep learning and radiomics are key AI methodologies currently employed in radiology.
  • These AI techniques leverage extensive data within medical images for enhanced insights.

Purpose of the Study:

  • To review the current applications of artificial intelligence in pancreatic imaging.
  • To critically evaluate the quality of existing evidence in AI for pancreatic imaging.
  • To utilize the radiomics quality score for appraising study methodologies.

Main Methods:

  • Deep learning: Utilizes layered, self-correcting algorithms to build data-fitting mathematical models.
  • Radiomics: Extracts and analyzes quantitative features (e.g., intensity, shape, texture) from medical images.
  • Radiomics Quality Score (RQS): Employed to assess the methodological rigor of included studies.

Main Results:

  • Both deep learning and radiomics demonstrate potential in enhancing disease detection, characterization, and prognostication in medical imaging.
  • The review critically appraises the existing body of evidence on AI in pancreatic imaging.
  • Quality assessment using the radiomics quality score provides insights into the reliability of current research.

Conclusions:

  • Artificial intelligence, particularly deep learning and radiomics, holds significant promise for advancing pancreatic imaging.
  • Further high-quality research is needed to fully realize the potential of AI in this field.
  • Methodological standardization and robust validation are crucial for clinical translation.