Applications of Artificial Intelligence in Acute Abdominal Imaging

Jason Yao, Linda C. Chu, Michael Patlas

Research output: Contribution to journalReview articlepeer-review

Abstract

Artificial intelligence (AI) is a rapidly growing field with significant implications for radiology. Acute abdominal pain is a common clinical presentation that can range from benign conditions to life-threatening emergencies. The critical nature of these situations renders emergent abdominal imaging an ideal candidate for AI applications. CT, radiographs, and ultrasound are the most common modalities for imaging evaluation of these patients. For each modality, numerous studies have assessed the performance of AI models for detecting common pathologies, such as appendicitis, bowel obstruction, and cholecystitis. The capabilities of these models range from simple classification to detailed severity assessment. This narrative review explores the evolution, trends, and challenges in AI applications for evaluating acute abdominal pathologies. We review implementations of AI for non-traumatic and traumatic abdominal pathologies, with discussion of potential clinical impact, challenges, and future directions for the technology.

Original languageEnglish (US)
Pages (from-to)761-770
Number of pages10
JournalCanadian Association of Radiologists Journal
Volume75
Issue number4
DOIs
StatePublished - Nov 2024

Keywords

  • abdomen
  • artificial intelligence
  • computed tomography
  • emergency
  • radiography
  • radiology
  • trauma
  • ultrasound

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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