Evaluating our progress with trauma transfer imaging: repeat CT scans, incomplete imaging, and delayed definitive care

Summers, Lauren; Harry, Melissa L.; Colling, Kristin P.

doi:10.1007/s10140-021-01938-x

Cited by 1 publication

(2 citation statements)

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“…Specifically in pediatric patients, FAST examinations are 59.5% sensitive and 84.3% specific for early surgical intervention, which may prompt expeditious transfer to a pediatric trauma center, of which there are fewer nationwide 32 . Utilization of the FAST examination during the initial trauma activation may provide useful radiologic information for expeditious disposition decisions and reduce delay to definitive trauma care created by comprehensive CT imaging before transfer 31,33,34 . Furthermore, while FAST examinations have not been integrated into transfer criteria for pediatric patients, they are frequently used as a screening test before obtaining CT imaging, which may reduce the long-term effects of excess radiation for young children 35–37 .…”

Section: Discussionmentioning

confidence: 99%

“…32 Utilization of the FAST examination during the initial trauma activation may provide useful radiologic information for expeditious disposition decisions and reduce delay to definitive trauma care created by comprehensive CT imaging before transfer. 31,33,34 Furthermore, while FAST examinations have not been integrated into transfer criteria for pediatric patients, they are frequently used as a screening test before obtaining CT imaging, which may reduce the long-term effects of excess radiation for young children. [35][36][37] In many instances, an AI algorithm may help inform patient's treatment and continued work up through accurate and efficient diagnostic imaging interpretation.…”

Section: Discussionmentioning

confidence: 99%

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Artificial intelligence evaluation of focused assessment with sonography in trauma

Bumgardner

2023

J Trauma Acute Care Surg

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BACKGROUND The focused assessment with sonography in trauma (FAST) is a widely used imaging modality to identify the location of life-threatening hemorrhage in a hemodynamically unstable trauma patient. This study evaluates the role of artificial intelligence in interpretation of the FAST examination abdominal views, as it pertains to adequacy of the view and accuracy of fluid survey positivity. METHODS Focused assessment with sonography for trauma examination images from 2015 to 2022, from trauma activations, were acquired from a quaternary care level 1 trauma center with more than 3,500 adult trauma evaluations, annually. Images pertaining to the right upper quadrant and left upper quadrant views were obtained and read by a surgeon or radiologist. Positivity was defined as fluid present in the hepatorenal or splenorenal fossa, while adequacy was defined by the presence of both the liver and kidney or the spleen and kidney for the right upper quadrant or left upper quadrant views, respectively. Four convolutional neural network architecture models (DenseNet121, InceptionV3, ResNet50, Vgg11bn) were evaluated. RESULTS A total of 6,608 images, representing 109 cases were included for analysis within the “adequate” and “positive” data sets. The models relayed 88.7% accuracy, 83.3% sensitivity, and 93.6% specificity for the adequate test cohort, while the positive cohort conferred 98.0% accuracy, 89.6% sensitivity, and 100.0% specificity against similar models. Augmentation improved the accuracy and sensitivity of the positive models to 95.1% accurate and 94.0% sensitive. DenseNet121 demonstrated the best accuracy across tasks. CONCLUSION Artificial intelligence can detect positivity and adequacy of FAST examinations with 94% and 97% accuracy, aiding in the standardization of care delivery with minimal expert clinician input. Artificial intelligence is a feasible modality to improve patient care imaging interpretation accuracy and should be pursued as a point-of-care clinical decision-making tool. LEVEL OF EVIDENCE Diagnostic Test/Criteria; Level III.

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Section: Discussionmentioning

confidence: 99%