Artificial intelligence in emergency radiology: where are we going?
M Cellina, M Cè, G Irmici, V Ascenti, E Caloro… - Diagnostics, 2022 - mdpi.com
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 …
management of different pathologies is essential to saving patients' lives. Artificial …
Artificial intelligence CAD tools in trauma imaging: a scoping review from the American Society of Emergency Radiology (ASER) AI/ML Expert Panel
Abstract Background AI/ML CAD tools can potentially improve outcomes in the high-stakes,
high-volume model of trauma radiology. No prior scoping review has been undertaken to …
high-volume model of trauma radiology. No prior scoping review has been undertaken to …
A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations
Purpose There is a growing body of diagnostic performance studies for emergency
radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is …
radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is …
[HTML][HTML] Applications of deep learning in trauma radiology: a narrative review
CT Cheng, CH Ooyang, CH Liao, SC Kang - Biomedical Journal, 2025 - Elsevier
Diagnostic imaging is essential in modern trauma care for initial evaluation and identifying
injuries requiring intervention. Deep learning (DL) has become mainstream in medical …
injuries requiring intervention. Deep learning (DL) has become mainstream in medical …
Toward automated interpretable AAST grading for blunt splenic injury
Abstract Background The American Association for the Surgery of Trauma (AAST) splenic
organ injury scale (OIS) is the most frequently used CT-based grading system for blunt …
organ injury scale (OIS) is the most frequently used CT-based grading system for blunt …
A pilot study of deep learning-based CT volumetry for traumatic hemothorax
Abstract Purpose We employ nnU-Net, a state-of-the-art self-configuring deep learning-
based semantic segmentation method for quantitative visualization of hemothorax (HTX) in …
based semantic segmentation method for quantitative visualization of hemothorax (HTX) in …
A deep learning framework for automated detection and quantitative assessment of liver trauma
Background Both early detection and severity assessment of liver trauma are critical for
optimal triage and management of trauma patients. Current trauma protocols utilize …
optimal triage and management of trauma patients. Current trauma protocols utilize …
Pulmonary contusion: automated deep learning-based quantitative visualization
Purpose Rapid automated CT volumetry of pulmonary contusion may predict progression to
Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in …
Acute Respiratory Distress Syndrome (ARDS) and help guide early clinical management in …
The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential localization: an experimental study
Background: Splenic injury is the most common solid visceral injury in blunt abdominal
trauma, and high-resolution abdominal computed tomography (CT) can adequately detect …
trauma, and high-resolution abdominal computed tomography (CT) can adequately detect …
Deep Learning for automated detection and localization of traumatic abdominal solid organ injuries on CT scans
Computed tomography (CT) is the most commonly used diagnostic modality for blunt
abdominal trauma (BAT), significantly influencing management approaches. Deep learning …
abdominal trauma (BAT), significantly influencing management approaches. Deep learning …