Diagnostic accuracy of a novel software technology for detecting pneumothorax in a porcine model

Abstract Introduction Our objective was to measure the diagnostic accuracy of a novel software technology to detect pneumothorax on Brightness (B) mode and Motion (M) mode ultrasonography. Methods Ultrasonography fellowship-trained emergency physicians performed thoracic ultrasonography at baseline...

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Bibliographic Details
Published in:The American journal of emergency medicine 2017-09, Vol.35 (9), p.1285-1290
Main Authors: Summers, Shane M., MD, Chin, Eric J., MD, April, Michael D., MD, DPhil, Grisell, Ronald D., PhD, Lospinoso, Joshua A., PhD, Kheirabadi, Bijan S., PhD, Salinas, Jose, PhD, Blackbourne, Lorne H., MD
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Anesthesia
Animals
Automation
Breathing
Cancer
Cardiovascular disease
Chest Tubes
Computer programs
Coronary vessels
Diabetic retinopathy
Diagnosis
Diagnostic software
Digital imaging
Emergency
Emergency medical care
Engineers
Female
Heart attacks
Image Interpretation, Computer-Assisted - methods
Laboratory animals
Likelihood ratio
Medical diagnosis
Medical personnel
Pneumothorax
Pneumothorax - diagnostic imaging
Sensitivity
Sensitivity and Specificity
Software
Swine
Technology
Thoracic Wall - diagnostic imaging
Thorax
Ultrasonic imaging
Ultrasonography
Ultrasound
Ventilation
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Summary:Abstract Introduction Our objective was to measure the diagnostic accuracy of a novel software technology to detect pneumothorax on Brightness (B) mode and Motion (M) mode ultrasonography. Methods Ultrasonography fellowship-trained emergency physicians performed thoracic ultrasonography at baseline and after surgically creating a pneumothorax in eight intubated, spontaneously breathing porcine subjects. Prior to pneumothorax induction, we captured sagittal M-mode still images and B-mode videos of each intercostal space with a linear array transducer at 4 cm of depth. After collection of baseline images, we placed a chest tube, injected air into the pleural space in 250 mL increments, and repeated the ultrasonography for pneumothorax volumes of 250 mL, 500 mL, 750 mL, and 1000 mL. We confirmed pneumothorax with intrapleural digital manometry and ultrasound by expert sonographers. We exported collected images for interpretation by the software. We treated each individual scan as a single test for interpretation by the software. Results Excluding indeterminate results, we collected 338 M-mode images for which the software demonstrated a sensitivity of 98% (95% confidence interval [CI] 92–99%), specificity of 95% (95% CI 86–99), positive likelihood ratio (LR +) of 21.6 (95% CI 7.1–65), and negative likelihood ratio (LR −) of 0.02 (95% CI 0.008–0.046). Among 364 B-mode videos, the software demonstrated a sensitivity of 86% (95% CI 81–90%), specificity of 85% (81–91%), LR + of 5.7 (95% CI 3.2–10.2), and LR − of 0.17 (95% CI 0.12–0.22). Conclusions This novel technology has potential as a useful adjunct to diagnose pneumothorax on thoracic ultrasonography.
ISSN:0735-6757
1532-8171
DOI:10.1016/j.ajem.2017.03.073