The focused assessment with sonography for 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 (AI) in interpretation of the FAST exam abdominal views, as it pertains to adequacy of the view and accuracy of fluid survey positivity. FAST exam images from 2015-2022, from trauma activations, were acquired from a quaternary care level 1 trauma center with over 3500 adult trauma evaluations, annually. Images pertaining to the right upper quadrant (RUQ) and left upper quadrant (LUQ) 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 RUQ or LUQ views, respectively. Four convolutional neural network architecture models (DenseNet121, InceptionV3, ResNet50, Vgg11bn) were evaluated. 6608 images, representing 109 cases were included for analysis within the "Adequate" and "Positive" datasets. 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. AI can detect positivity and adequacy of FAST exams with 94% and 97% accuracy, aiding in the standardization of care delivery with minimal expert clinician input. AI 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 III, Diagnostic test/criteria.
4,519 publications found
Sort by