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Artificial intelligence in the detection of non-biological materials.
Eibschutz, Liesl; Lu, Max Yang; Abbassi, Mashya T; Gholamrezanezhad, Ali.
Afiliación
  • Eibschutz L; Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA.
  • Lu MY; Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA.
  • Abbassi MT; Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA.
  • Gholamrezanezhad A; Department of Radiology Division of Emergency Radiology, Keck School of Medicine, University of Southern California (USC), 1500 San Pablo Street, Los Angeles, CA, 90033, USA. ali.gholamrezanezhad@med.usc.edu.
Emerg Radiol ; 31(3): 391-403, 2024 Jun.
Article en En | MEDLINE | ID: mdl-38530436
ABSTRACT
Artificial Intelligence (AI) has emerged as a transformative force within medical imaging, making significant strides within emergency radiology. Presently, there is a strong reliance on radiologists to accurately diagnose and characterize foreign bodies in a timely fashion, a task that can be readily augmented with AI tools. This article will first explore the most common clinical scenarios involving foreign bodies, such as retained surgical instruments, open and penetrating injuries, catheter and tube malposition, and foreign body ingestion and aspiration. By initially exploring the existing imaging techniques employed for diagnosing these conditions, the potential role of AI in detecting non-biological materials can be better elucidated. Yet, the heterogeneous nature of foreign bodies and limited data availability complicates the development of computer-aided detection models. Despite these challenges, integrating AI can potentially decrease radiologist workload, enhance diagnostic accuracy, and improve patient outcomes.
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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Cuerpos Extraños Límite: Humans Idioma: En Revista: Emerg Radiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos

Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Asunto principal: Inteligencia Artificial / Cuerpos Extraños Límite: Humans Idioma: En Revista: Emerg Radiol Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos