Sensor-Based Automated Detection of Electrosurgical Cautery States.

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.

A comparison of postoperative complications in open versus robotic cystectomy.

BACKGROUND: Robotic cystectomy is an emerging alternative for treatment of invasive bladder cancer (BCa). However, reduction in postoperative morbidity relative to the open approach has not been demonstrated. OBJECTIVE: To compare complication rates in patients undergoing robotic versus open radical cystectomy (RC). DESIGN, SETTING, AND PARTICIPANTS: A prospective cohort study of 187 consecutive patients undergoing RC at our institution-104 open RC, 83 robotic RC. INTERVENTION: Open or robotic RC with urinary diversion. MEASUREMENTS: Demographic, perioperative, and complication data were recorded prospectively. Thirty-day and 90-d complication rates were assessed using the modified Clavien complication scale. Data were evaluated using chi(2) and multivariate logistic regression analyses. RESULTS AND LIMITATIONS: At 30 d, the open group demonstrated a higher overall complication rate (59% vs 41%; p=0.04) as well as more major complications (30% vs 10%; p=0.007). At 90 d, the overall complication rate was greater in the open group, but this was not statistically significant (62% vs 48%; p=0.07). However, there was a significantly higher major complication rate in the open cohort (31% vs 17%; p=0.03). When subjected to logistic regression analysis, robotic cystectomy was an independent predictor of fewer overall and major complications at 30 and 90 d. High American Society of Anesthesiologists (ASA) score (3-4) and longer surgical time were independent predictors of major complications. Though this is one of the largest published RC series, the sample size is relatively small. Moreover, despite the two patient cohorts being similarly matched, the study was not performed in a randomized fashion. CONCLUSIONS: Patients undergoing robotic cystectomy experienced fewer postoperative complications than those undergoing open cystectomy. Robotic cystectomy is an independent predictor of fewer overall and major complications. Until long-term oncologic results are available, robotic cystectomy should still be considered investigational.