Identification of the electrocautery state to enable spatially navigated intra-operative mass spectrometry tissue analysis

Asselin, Mark; Jamzad, Amoon; Lasso, A.; Ungi, Tamás; Rudan, J; Fichtinger, Gabor

doi:10.31256/hsmr2019.29

Cited by 2 publications

(3 citation statements)

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“…We tested three different ex vivo tissue specimens for each cautery device and wattage including chicken, porcine, and bovine. Building on our previous work, we attached an SCT-013 current sensor (YHDC, Madrid, Spain) to the live and return electrodes of the cautery [ 8 , 22 ]. The two sensors were connected to a PicoScope P2204A USB oscilloscope (Pico Technologies, St Neots, UK) that digitized the electrical signals.…”

Section: Methodsmentioning

confidence: 99%

“…The data gathered shall guide the surgeon to the pre-operatively defined target anatomical locations. In CAS, it may be necessary to detect when an activated electrosurgical tool (applied part of a surgical device from the standards point of view [ 7 ]) comes into contact with the patient; this is known as the energy event [ 8 ]. For electrosurgical cautery, placing an accurate timestamp on the energy event is necessary when locating tissue classifications from an intra-operative sensor that characterizes tissue properties.…”

Section: Introductionmentioning

confidence: 99%

“…In previous works by Asselin et al and Carter et al, the feasibility of using relative voltage signals to detect the energy event of a surgical cautery was demonstrated [ 8 , 22 ]. To achieve this, relative voltage signals were collected, and the data was clustered based on the cautery mode [ 8 , 22 ]. However, to implement this system in the operating room, the energy event must be detected automatically.…”

Section: Introductionmentioning

confidence: 99%

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Sensor-Based Automated Detection of Electrosurgical Cautery States

Ehrlich

Jamzad

Asselin

et al. 2022

Sensors

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%