{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T08:32:38Z","timestamp":1743064358295,"version":"3.37.3"},"reference-count":35,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:00:00Z","timestamp":1659484800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NIH\/NIBIB","award":["grant 1R01EB021396-01A1"]},{"name":"CANARIE\u2019s Research Software Program, Ontario Graduate Scholarship (OGS)"},{"name":"Natural Sciences and Engineering Research Council of Canada (NSERC)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"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\u2019 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\u2014robustly 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.<\/jats:p>","DOI":"10.3390\/s22155808","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T03:33:01Z","timestamp":1659583981000},"page":"5808","source":"Crossref","is-referenced-by-count":3,"title":["Sensor-Based Automated Detection of Electrosurgical Cautery States"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7970-3520","authenticated-orcid":false,"given":"Josh","family":"Ehrlich","sequence":"first","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"given":"Amoon","family":"Jamzad","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"given":"Mark","family":"Asselin","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6950-5398","authenticated-orcid":false,"given":"Jessica Robin","family":"Rodgers","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"given":"Martin","family":"Kaufmann","sequence":"additional","affiliation":[{"name":"Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1402-1139","authenticated-orcid":false,"given":"Tamas","family":"Haidegger","sequence":"additional","affiliation":[{"name":"University Research and Innovation Center (EKIK), \u00d3buda University, 1034 Budapest, Hungary"}]},{"given":"John","family":"Rudan","sequence":"additional","affiliation":[{"name":"Department of Surgery, Kingston Health Sciences Centre, Kingston, ON K7L 2V7, Canada"}]},{"given":"Parvin","family":"Mousavi","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6354-262X","authenticated-orcid":false,"given":"Gabor","family":"Fichtinger","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4743-0609","authenticated-orcid":false,"given":"Tamas","family":"Ungi","sequence":"additional","affiliation":[{"name":"School of Computing, Queen\u2019s University, Kingston, ON K7L 3N6, Canada"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Taylor, R.H., Menciassi, A., Fichtinger, G., Fiorini, P., and Dario, P. 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