UCSF

Objective

Pediatric focused assessment with sonography for trauma (FAST) is a sequence of ultrasound views rapidly performed by clinicians to diagnose hemorrhage. A technical limitation of FAST is the lack of expertise to consistently acquire all required views. We sought to develop an accurate deep learning view classifier using a large heterogeneous dataset of clinician-performed pediatric FAST.

Methods

We developed and conducted a retrospective cohort analysis of a deep learning view classifier on real-world FAST studies performed on injured children less than 18 years old in two pediatric emergency departments by 30 different clinicians. FAST was randomly distributed to training, validation, and test datasets, 70:20:10; each child was represented in only one dataset. The primary outcome was view classifier accuracy for video clips and still frames.

Results

There were 699 FAST studies, representing 4925 video clips and 1,062,612 still frames, performed by 30 different clinicians. The overall classification accuracy was 97.8% (95% confidence interval [CI]: 96.0-99.0) for video clips and 93.4% (95% CI: 93.3-93.6) for still frames. Per view still frames were classified with an accuracy: 96.0% (95% CI: 95.9-96.1) cardiac, 99.8% (95% CI: 99.8-99.8) pleural, 95.2% (95% CI: 95.0-95.3) abdominal upper quadrants, and 95.9% (95% CI: 95.8-96.0) suprapubic.

Conclusion

A deep learning classifier can accurately predict pediatric FAST views. Accurate view classification is important for quality assurance and feasibility of a multi-stage deep learning FAST model to enhance the evaluation of injured children.