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

• Published in: The American journal of emergency medicine Vol. 35; no. 9; pp. 1285 - 1290
• Main Authors: Summers, Shane M., Chin, Eric J., April, Michael D., Grisell, Ronald D., Lospinoso, Joshua A., Kheirabadi, Bijan S., Salinas, Jose, Blackbourne, Lorne H.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2017; Elsevier Limited
• Subjects: Accuracy; Algorithms; Anesthesia; Animals; Automation; Cancer; Cardiovascular disease; Chest Tubes; Computer programs; Coronary vessels; Diabetic retinopathy; Diagnosis; Emergency; Emergency medical care; Engineers; Female; Heart attacks; Image Interpretation, Computer-Assisted - methods; Laboratory animals; Medical diagnosis; Pneumothorax; Pneumothorax - diagnostic imaging; Sensitivity and Specificity; Software; Swine; Thoracic Wall - diagnostic imaging; Thorax; Ultrasonic imaging; Ultrasonography; Ultrasound; Ventilation; Pneumothorax; Software; Diagnosis; Swine; Ultrasonography
• ISSN: 0735-6757; 1532-8171; 1532-8171
• DOI: 10.1016/j.ajem.2017.03.073

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. 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 4cm of depth. After collection of baseline images, we placed a chest tube, injected air into the pleural space in 250mL increments, and repeated the ultrasonography for pneumothorax volumes of 250mL, 500mL, 750mL, and 1000mL. 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. Excluding indeterminate results, we collected 338M-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). This novel technology has potential as a useful adjunct to diagnose pneumothorax on thoracic ultrasonography.