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Image Segmentation and Machine Learning for Detection of Abdominal Free Fluid in Focused Assessment With Sonography for Trauma Examinations
Author(s) -
Sjogren Anna R.,
Leo Megan M.,
Feldman James,
Gwin Joseph T.
Publication year - 2016
Publication title -
journal of ultrasound in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.574
H-Index - 91
eISSN - 1550-9613
pISSN - 0278-4297
DOI - 10.7863/ultra.15.11017
Subject(s) - medicine , focused assessment with sonography for trauma , radiology , confidence interval , ultrasound , abdominal ultrasound , segmentation , medical physics , artificial intelligence , abdominal trauma , computer science , blunt
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.