Peritoneal fluid localization on FAST examination in the pediatric trauma patient

Brenkert, Timothy E.; Adams, Cynthia M.; Vieira, Rebecca L.; Rempell, Rachel

doi:10.1016/j.ajem.2017.04.025

Cited by 12 publications

(36 citation statements)

References 8 publications

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“…Notably, the model is accurate in separating abdominal FAST views, including the suprapubic view, which is the most sensitive abdominal view for intraabdominal hemorrhage detection in pediatric patients. (26) The inclusion of real-world clinician-performed FAST studies strengthens our model's generalizability by avoiding an idealized dataset. Further, this study's strength is that our pediatric FAST dataset is a relatively large expert-labeled set of pediatric FAST video clips and still frames from children with suspicion for abdominal hemorrhage.…”

Section: Discussionmentioning

confidence: 73%

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Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

Kornblith

Addo

Dong

et al. 2020

Preprint

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The pediatric Focused Assessment with Sonography for Trauma (FAST) is a sequence of ultrasound views rapidly performed by the clinician to diagnose hemorrhage. One limitation of FAST is inconsistent acquisition of required views. We sought to develop a deep learning model and classify FAST views using a heterogeneous dataset of pediatric FAST. This study of diagnostic test developed and tested a deep learning model for view classification of archived real-world pediatric FAST studies collected from two pediatric emergency departments. FAST frames were randomly distributed to training, validation, and test datasets in a 70:20:10 ratio; each patient was represented in only one dataset to maintain sample independence. The outcome was the prediction accuracy of the model in classifying FAST frames and video clips. FAST studies performed by 30 different clinicians from 699 injured children included 4,925 videos representing 1,062,612 frames from children who were a median of 9 years old. On test dataset, the overall view classification accuracy for the model was 93.4% (95% CI: 93.3-93.6) for frames and 97.8% (95% CI: 96.0-99.0) for video clips. Frames were correctly classified with an accuracy of 96.0% (95% CI: 95.9-96.1) for cardiac, 99.8% (95% CI: 99.8-99.8) for thoracic, 95.2% (95% CI: 95.0-95.3) for abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) for suprapubic. A deep learning model can be developed to accurately classify pediatric FAST views. Accurate view classification is the important first step to support developing a consistent and accurate multi-stage deep learning model for pediatric FAST interpretation.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

Kornblith

Addo

Dong

et al. 2020

Preprint

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“…Of the 39 studies on abdominal non-radiologist point-of-care US, we found 9 studies on the bladder (Table 1 ; [ 8 – 16 ]), 10 on the bowel (Table 2 ; [ 17 – 26 ]), 4 on the stomach (Table 3 ; [ 27 – 30 ]), 1 on the kidney (Table 4 ; [ 31 ]), 4 on fluid status (Table 5 ; [ 4 , 32 – 34 ]), 9 on non-radiologist point-of-care US for trauma screening (Table 6 ; [ 35 – 43 ]) and 1 “other” on umbilical artery line placement (Table 7; [ 44 ]). Next we present these studies per organ.…”

Section: Resultsmentioning

confidence: 99%

Non-radiologist-performed abdominal point-of-care ultrasonography in paediatrics — a scoping review

et al. 2021

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Background Historically, US in the paediatric setting has mostly been the domain of radiologists. However, in the last decade, there has been an uptake of non-radiologist point-of-care US. Objective To gain an overview of abdominal non-radiologist point-of-care US in paediatrics. Materials and methods We conducted a scoping review regarding the uses of abdominal non-radiologist point-of-care US, quality of examinations and training, patient perspective, financial costs and legal consequences following the use of non-radiologist point-of-care US. We conducted an advanced search of the following databases: Medline, Embase and Web of Science Conference Proceedings. We included published original research studies describing abdominal non-radiologist point-of-care US in children. We limited studies to English-language articles from Western countries. Results We found a total of 5,092 publications and selected 106 publications for inclusion: 39 studies and 51 case reports or case series on the state-of-art of abdominal non-radiologist point-of-care US, 14 on training of non-radiologists, and 1 each on possible harms following non-radiologist point-of-care US and patient satisfaction. According to included studies, non-radiologist point-of-care US is increasingly used, but no standardised training guidelines exist. We found no studies regarding the financial consequences of non-radiologist point-of-care US. Conclusion This scoping review supports the further development of non-radiologist point-of-care US and underlines the need for consensus on who can do which examination after which level of training among US performers. More research is needed on training non-radiologists and on the costs-to-benefits of non-radiologist point-of-care US.

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“…Recently published data in children show fluid is more likely to accumulate in the pelvis in FAST-positive children. 11,21 Without this additional descriptive data or archived images for qualitative review, we are unable to evaluate for an association with sonographic FF location that could affect the accuracy of the FAST exam.…”

Section: Limitationsmentioning

confidence: 99%

Focused assessment with sonography for trauma in predicting early surgical intervention in hemodynamically unstable children with blunt abdominal trauma

et al. 2022

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Peritoneal fluid localization on FAST examination in the pediatric trauma patient

Cited by 12 publications

References 8 publications

Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

Development and Validation of a Deep Learning Model for Automated View Classification of Pediatric Focused Assessment with Sonography for Trauma (FAST)

Non-radiologist-performed abdominal point-of-care ultrasonography in paediatrics — a scoping review

Focused assessment with sonography for trauma in predicting early surgical intervention in hemodynamically unstable children with blunt abdominal trauma

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