Image Segmentation and Machine Learning for Detection of Abdominal Free Fluid in Focused Assessment With Sonography for Trauma Examinations: A Pilot Study. (13th October 2016)

• Record Type: Journal Article
• Authors: Sjogren, Anna R.; Leo, Megan M.; Feldman, James; Gwin, Joseph T.
• Is Part Of: Journal of ultrasound in medicine. Volume 35:Number 11(2016)
• Page Start: 2501
• Page End: 2509
• Publication Date: 2016-10-13
• Subjects: abdominal free fluid -- emergency medicine -- focused assessment with sonography for trauma examination -- image segmentation -- point-of-care ultrasound -- support vector machine -- ultrasound; Ultrasonics in medicine -- Periodicals; Ultrasonics; Ultrasonography; Ultrasonics in medicine; Electronic journals; Periodicals; Periodicals; 616.07543
• DOI: 10.7863/ultra.15.11017
• Languages: English
• ISSNs: 0278-4297

Abstract

Abstract : The objective of this pilot study was to test the feasibility of automating the detection of abdominal free fluid in focused assessment with sonography for trauma (FAST) examinations. Perihepatic views from 10 FAST examinations with positive results and 10 FAST examinations with negative results were used. The sensitivity and specificity compared to manual classification by trained physicians was evaluated. The sensitivity and specificity (95% confidence interval) were 100% (69.2%–100%) and 90.0% (55.5%–99.8%), respectively. These findings suggest that computerized detection of free fluid on abdominal ultrasound images may be sensitive and specific enough to aid clinicians in their interpretation of a FAST examination.