In computer-assisted surgery, it is typically required to detect when the tool comes into contact with the patient. In activated electrosurgery, this is known as the energy event. By continuously tracking the electrosurgical tools’ location using a navigation system, energy events can help determine locations of sensor-classified tissues. Our objective was to detect the energy event and determine the settings of electrosurgical cautery—robustly and automatically based on sensor data. This study aims to demonstrate the feasibility of using the cautery state to detect surgical incisions, without disrupting the surgical workflow. We detected current changes in the wires of the cautery device and grounding pad using non-invasive current sensors and an oscilloscope. An open-source software was implemented to apply machine learning on sensor data to detect energy events and cautery settings. Our methods classified each cautery state at an average accuracy of 95.56% across different tissue types and energy level parameters altered by surgeons during an operation. Our results demonstrate the feasibility of automatically identifying energy events during surgical incisions, which could be an important safety feature in robotic and computer-integrated surgery. This study provides a key step towards locating tissue classifications during breast cancer operations and reducing the rate of positive margins.

Background To improve implant survival after reverse shoulder arthroplasty (RSA), surgeons need to maximize screw fixation. However, bone density variation and distribution within the scapula are not well understood as they relate to RSA. The three columns of bone in the scapula surrounding the glenoid fossa are the lateral border, the base of the coracoid process, and the spine of the scapula. In our previous study by Daalder et al on cadaveric specimens, the coracoid column was significantly less dense than the lateral border and spine. This study’s objective was to verify whether these results are consistent with computer tomography (CT) scan information from patients undergoing RSA. Methods Two-dimensional axial CT images from twelve patients were segmented, and a three-dimensional digital model of the scapula was subsequently created using Mimics 17.0 Materialise Software (Leuven, Belgium). Hounsfield unit (HU) values representing cortical bone were filtered out to determine the distributions of trabecular bone density. An analysis of variance with post hoc Bonferroni tests determined the differences in bone density between the columns of bone in the scapula. Results The coracoid superolateral (270 ± 45.6 HU) to the suprascapular notch was significantly less dense than the inferior (356 ± 63.6 HU, P = .03, d s = 1.54) and anterosuperior portion of the lateral border (353 ± 68.9 HU, P = .04, d s = 1.42) and the posterior (368 ± 70 HU, P = .007, d s = 1.65) and anterior spine (370 ± 78.9 HU, P = .006, d s = 1.54). Discussion/Conclusion The higher-density bone in the spine and lateral border compared with the coracoid region may provide better bone purchase for screws when fixing the glenoid baseplate in RSA. This is in agreement with our previous study and indicates that the previous cadaveric results are applicable to clinical CT scan data. When these studies are taken together, they provide robust evidence for clinical applications, including having surgeons aim screws for higher-density regions to increase screw fixation, which may decrease micromotion and improve implant longevity.