Clinical Translation of the LevelCheck Decision Support Algorithm for Target Localization in Spine Surgery - Manbachi, Amir; De Silva, Tharindu; Uneri, Ali; Jacobson, Matthew; Goerres, Joseph; Ketcha, Michael; Han, Runze; Aygun, Nafi; Thompson, David; Ye, Xiaobu; Vogt, Sebastian; Kleinszig, Gerhard; et al. (Annals of biomedical engineering, 2018)
Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck , previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon’s decision) and (b) Active Assistant (labels presented before the surgeon’s decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes ( p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery.