Artificial Intelligence in Emergency Radiology: Where Are We Going?
CAD
artificial intelligence
deep learning
emergency radiology
smart reporting
Journal
Diagnostics (Basel, Switzerland)
ISSN: 2075-4418
Titre abrégé: Diagnostics (Basel)
Pays: Switzerland
ID NLM: 101658402
Informations de publication
Date de publication:
19 Dec 2022
19 Dec 2022
Historique:
received:
29
10
2022
revised:
11
12
2022
accepted:
16
12
2022
entrez:
23
12
2022
pubmed:
24
12
2022
medline:
24
12
2022
Statut:
epublish
Résumé
Emergency Radiology is a unique branch of imaging, as rapidity in the diagnosis and management of different pathologies is essential to saving patients' lives. Artificial Intelligence (AI) has many potential applications in emergency radiology: firstly, image acquisition can be facilitated by reducing acquisition times through automatic positioning and minimizing artifacts with AI-based reconstruction systems to optimize image quality, even in critical patients; secondly, it enables an efficient workflow (AI algorithms integrated with RIS-PACS workflow), by analyzing the characteristics and images of patients, detecting high-priority examinations and patients with emergent critical findings. Different machine and deep learning algorithms have been trained for the automated detection of different types of emergency disorders (e.g., intracranial hemorrhage, bone fractures, pneumonia), to help radiologists to detect relevant findings. AI-based smart reporting, summarizing patients' clinical data, and analyzing the grading of the imaging abnormalities, can provide an objective indicator of the disease's severity, resulting in quick and optimized treatment planning. In this review, we provide an overview of the different AI tools available in emergency radiology, to keep radiologists up to date on the current technological evolution in this field.
Identifiants
pubmed: 36553230
pii: diagnostics12123223
doi: 10.3390/diagnostics12123223
pmc: PMC9777804
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
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