Mohamed Tiouririne1, Adam J Dixon, F William Mauldin, David Scalzo, Arun Krishnaraj. 1. From the *Department of Anesthesiology, University of Virginia Health System; †Department of Biomedical Engineering, University of Virginia; ‡Rivanna Medical; and §Department of Radiology, University of Virginia Health System, Charlottesville, VA.
Abstract
OBJECTIVES: The aim of this study was to evaluate the imaging performance of a handheld ultrasound system and the accuracy of an automated lumbar spine computer-aided detection (CAD) algorithm in the spines of human subjects. MATERIALS AND METHODS: This study was approved by the institutional review board of the University of Virginia. The authors designed a handheld ultrasound system with enhanced bone image quality and fully automated CAD of lumbar spine anatomy. The imaging performance was evaluated by imaging the lumbar spines of 68 volunteers with body mass index between 18.5 and 48 kg/m. The accuracy, sensitivity, and specificity of the lumbar spine CAD algorithm were assessed by comparing the algorithm's results to ground-truth segmentations of neuraxial anatomy provided by radiologists. RESULTS: The lumbar spine CAD algorithm detected the epidural space with a sensitivity of 94.2% (95% confidence interval [CI], 85.1%-98.1%) and a specificity of 85.5% (95% CI, 81.7%-88.6%) and measured its depth with an error of approximately ±0.5 cm compared with measurements obtained manually from the 2-dimensional ultrasound images. The spine midline was detected with a sensitivity of 93.9% (95% CI, 85.8%-97.7%) and specificity of 91.3% (95% CI, 83.6%-96.9%), and its lateral position within the ultrasound image was measured with an error of approximately ±0.3 cm. The bone enhancement imaging mode produced images with 5.1- to 10-fold enhanced bone contrast when compared with a comparable handheld ultrasound imaging system. CONCLUSIONS: The results of this study demonstrate the feasibility of CAD for assisting with real-time interpretation of ultrasound images of the lumbar spine at the bedside.
OBJECTIVES: The aim of this study was to evaluate the imaging performance of a handheld ultrasound system and the accuracy of an automated lumbar spine computer-aided detection (CAD) algorithm in the spines of human subjects. MATERIALS AND METHODS: This study was approved by the institutional review board of the University of Virginia. The authors designed a handheld ultrasound system with enhanced bone image quality and fully automated CAD of lumbar spine anatomy. The imaging performance was evaluated by imaging the lumbar spines of 68 volunteers with body mass index between 18.5 and 48 kg/m. The accuracy, sensitivity, and specificity of the lumbar spine CAD algorithm were assessed by comparing the algorithm's results to ground-truth segmentations of neuraxial anatomy provided by radiologists. RESULTS: The lumbar spine CAD algorithm detected the epidural space with a sensitivity of 94.2% (95% confidence interval [CI], 85.1%-98.1%) and a specificity of 85.5% (95% CI, 81.7%-88.6%) and measured its depth with an error of approximately ±0.5 cm compared with measurements obtained manually from the 2-dimensional ultrasound images. The spine midline was detected with a sensitivity of 93.9% (95% CI, 85.8%-97.7%) and specificity of 91.3% (95% CI, 83.6%-96.9%), and its lateral position within the ultrasound image was measured with an error of approximately ±0.3 cm. The bone enhancement imaging mode produced images with 5.1- to 10-fold enhanced bone contrast when compared with a comparable handheld ultrasound imaging system. CONCLUSIONS: The results of this study demonstrate the feasibility of CAD for assisting with real-time interpretation of ultrasound images of the lumbar spine at the bedside.
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