Using Deep Learning to Detect the Presence and Location of Hemoperitoneum on the Focused Assessment with Sonography in Trauma (FAST) Examination in Adults.
Artificial intelligence
Deep learning
Emergency ultrasound
Focused Assessment with Sonography in Trauma
Point-of-care ultrasound
Trauma
Journal
Journal of digital imaging
ISSN: 1618-727X
Titre abrégé: J Digit Imaging
Pays: United States
ID NLM: 9100529
Informations de publication
Date de publication:
10 2023
10 2023
Historique:
received:
14
02
2023
accepted:
04
05
2023
revised:
13
04
2023
pmc-release:
01
10
2024
medline:
18
9
2023
pubmed:
8
6
2023
entrez:
7
6
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37286904
doi: 10.1007/s10278-023-00845-6
pii: 10.1007/s10278-023-00845-6
pmc: PMC10501965
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
2035-2050Subventions
Organisme : NIGMS NIH HHS
ID : R44 GM123821
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001430
Pays : United States
Informations de copyright
© 2023. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.
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