Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults

Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical appli...

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Vydáno v:Journal of digital imaging Ročník 36; číslo 5; s. 2035 - 2050
Hlavní autoři: Leo, Megan M, Potter, Ilkay Yildiz, Zahiri, Mohsen, Vaziri, Ashkan, Jung, Christine F, Feldman, James A
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Springer Nature B.V 01.10.2023
Témata:
Adult
Algorithms
Deep Learning
Focused Assessment with Sonography for Trauma - methods
Hemoperitoneum
Hemoperitoneum - diagnostic imaging
Hemorrhage
Humans
Image processing
Latency
Localization
Machine learning
Object recognition
Point of care testing
Sensitivity
Sensitivity and Specificity
Trauma
Ultrasonic imaging
Ultrasonography
Ultrasound
Deep learning
Point-of-care ultrasound
Emergency ultrasound
Artificial intelligence
Trauma
Focused Assessment with Sonography in Trauma
ISSN:0897-1889, 1618-727X, 1618-727X
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Shrnutí:Abdominal ultrasonography has become an integral component of the evaluation of trauma patients. Internal hemorrhage can be rapidly diagnosed by finding free fluid with point-of-care ultrasound (POCUS) and expedite decisions to perform lifesaving interventions. However, the widespread clinical application of ultrasound is limited by the expertise required for image interpretation. This study aimed to develop a deep learning algorithm to identify the presence and location of hemoperitoneum on POCUS to assist novice clinicians in accurate interpretation of the Focused Assessment with Sonography in Trauma (FAST) exam. We analyzed right upper quadrant (RUQ) FAST exams obtained from 94 adult patients (44 confirmed hemoperitoneum) using the YoloV3 object detection algorithm. Exams were partitioned via fivefold stratified sampling for training, validation, and hold-out testing. We assessed each exam image-by-image using YoloV3 and determined hemoperitoneum presence for the exam using the detection with highest confidence score. We determined the detection threshold as the score that maximizes the geometric mean of sensitivity and specificity over the validation set. The algorithm had 95% sensitivity, 94% specificity, 95% accuracy, and 97% AUC over the test set, significantly outperforming three recent methods. The algorithm also exhibited strength in localization, while the detected box sizes varied with a 56% IOU averaged over positive cases. Image processing demonstrated only 57-ms latency, which is adequate for real-time use at the bedside. These results suggest that a deep learning algorithm can rapidly and accurately identify the presence and location of free fluid in the RUQ of the FAST exam in adult patients with hemoperitoneum.
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ISSN:0897-1889
1618-727X
1618-727X
DOI:10.1007/s10278-023-00845-6