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Artificial Intelligence in Pancreatic Imaging: A Systematic Review.

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Artificial intelligence (AI) is revolutionizing pancreatic imaging for better disease detection and diagnosis. AI, including machine learning and deep learning, shows promise in improving patient outcomes for pancreatic conditions.

Keywords:
artificial intelligencedeep learningendoscopic ultrasoundmachine learningpancreatic ductal adenocarcinoma

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Pancreatic diseases, including cancer, present a significant global health challenge with high mortality rates.
  • Current diagnostic methods for pancreatic conditions often require invasive biopsies, even with advanced imaging.
  • Artificial intelligence (AI) offers potential solutions for enhancing diagnostic precision and personalizing treatment in pancreatic diseases.

Purpose of the Study:

  • To review the role, advancements, and applications of AI in pancreatic imaging.
  • To explore the use of machine learning, deep learning, and radiomics in diagnosing pancreatic diseases.
  • To identify challenges and future directions for AI in pancreatic imaging.

Main Methods:

  • A narrative review following PRISMA-DTA guidelines.
  • Comprehensive literature search of PubMed, Scopus, and Cochrane Library up to March 31, 2024.
  • Inclusion of English articles on AI, machine learning, deep learning, and radiomics in pancreatic imaging involving human subjects.

Main Results:

  • AI, particularly deep learning (e.g., CNNs), shows effectiveness in detecting, segmenting, and differentiating pancreatic lesions.
  • AI algorithms aid in predicting survival, recurrence risk, and therapy response in pancreatic cancer.
  • Radiomics, combined with deep learning, improves diagnostic accuracy using quantitative imaging features.

Conclusions:

  • AI holds transformative potential for improving the diagnosis and treatment of pancreatic diseases.
  • Advancements in AI can lead to enhanced patient outcomes and survival rates.
  • Addressing challenges like ethical considerations and algorithm transparency is crucial for AI implementation.