Department of Radiology, McMaster University, Hamilton, ON, Canada.
Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Can Assoc Radiol J. 2024 Nov;75(4):761-770. doi: 10.1177/08465371241250197. Epub 2024 May 7.
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.
人工智能（AI）是一个快速发展的领域，对放射学有重大影响。急性腹痛是一种常见的临床表现，可从良性疾病到危及生命的紧急情况。这些情况的紧迫性使得急诊腹部成像成为 AI 应用的理想选择。CT、X 线和超声是这些患者影像学评估最常用的方式。对于每种方式，都有大量研究评估了 AI 模型在检测常见病理方面的性能，如阑尾炎、肠梗阻和胆囊炎。这些模型的功能范围从简单的分类到详细的严重程度评估。本叙述性综述探讨了 AI 在评估急性腹部病变中的应用的发展、趋势和挑战。我们回顾了 AI 在非创伤性和创伤性腹部病变中的应用，并讨论了该技术的潜在临床影响、挑战和未来方向。
