An automatic radiographic labeling algorithm called “LevelCheck” was analyzed as a means of decision support for target localization in spine surgery. The potential clinical utility and scenarios in which LevelCheck is likely to be the most beneficial were assessed in a retrospective clinical data set (398 cases) in terms of expert consensus from a multi-reader study (three spine surgeons).
The aim of this study was to evaluate the potential utility of the LevelCheck algorithm for vertebrae localization.
Three hundred ninety-eight intraoperative radiographs and 178 preoperative computed tomographic (CT) images for patients undergoing spine surgery in cervical, thoracic, lumbar regions.
Vertebral labels annotated in preoperative CT image were overlaid on intraoperative radiographs via 3D-2D registration. Three spine surgeons assessed the radiographs and LevelCheck labeling according to a questionnaire evaluating performance, utility, and suitability to surgical workflow. Geometric accuracy and registration run time were measured for each case.
LevelCheck was judged to be helpful in 42.2% of the cases (168/398), to improve confidence in 30.6% of the cases (122/398), and in no case diminished performance (0/398), supporting its potential as an independent check and assistant to decision support in spine surgery. The clinical contexts for which the method was judged most likely to be beneficial included the following scenarios: images with a lack of conspicuous anatomical landmarks; level counting across long spine segments; vertebrae obscured by other anatomy (e.g., shoulders); poor radiographic image quality; and anatomical variations/abnormalities. The method demonstrated 100% geometric accuracy (i.e., overlaid labels within the correct vertebral level in all cases) and did not introduce ambiguity in image interpretation.
LevelCheck is a potentially useful means of decision support in vertebral level localization in spine surgery.
Level of Evidence: N/A
∗Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
†Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland
‡Russell H. Morgan Department of Radiology, Johns Hopkins University, Baltimore, Maryland
§Department of Computer Science, Johns Hopkins University, Baltimore, Maryland
¶Siemens Healthcare, Erlangen, Germany.
Address correspondence and reprint requests to Jeffrey H. Siewerdsen, PhD, FAAPM, Department of Biomedical Engineering, Johns Hopkins University, Traylor Building, Room 622, 720 Rutland Avenue, Baltimore, MD 21205. E-mail: email@example.com
Received 20 January, 2016
Revised 5 March, 2016
Accepted 8 March, 2016
The manuscript submitted does not contain information about medical device(s)/drug(s).
The National Institutes of Health (R01 EB017226) and academic-industry partnership with Siemens Healthcare (XP Division, Erlangen Germany) funds were received in support of this work.
Relevant financial activities outside the submitted work: consultancy, grants, employment, stocks.