Brachytherapy, a type of radiotherapy, may be used to place radioactive sources into or in close proximity to tumors, providing a method for conformally escalating dose in the tumor and the local area surrounding the malignancy. High-dose-rate interstitial brachytherapy of vaginal tumors requires precise placement of multiple needles through holes in a plastic perineal template to deliver treatment while optimizing dose and avoiding overexposure of nearby organs at risk (OARs). Despite the importance of needle placement, image guidance for adaptive, intraoperative needle visualization, allowing misdirected needles to be identified and corrected during insertion, is not standard practice. We have developed a 360-deg three-dimensional (3-D) transvaginal ultrasound (TVUS) system using a conventional probe with a template-compatible custom sonolucent vaginal cylinder and propose its use for intraoperative needle guidance during interstitial gynecologic brachytherapy. We describe the 3-D TVUS mechanism and geometric validation, present mock phantom procedure results, and report on needle localization accuracy in patients. For the six patients imaged, landmark anatomical features and all needles were clearly visible. The implementation of 360-deg 3-D TVUS through a sonolucent vaginal cylinder provides a technique for visualizing needles and OARs intraoperatively during interstitial gynecologic brachytherapy, enabling implants to be assessed and providing the potential for image guidance.

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.