Details

Autor(en) / Beteiligte

• Karjalainen, Markus
• Kontunen, Anton
• Anttalainen, Anna
• Mäkelä, Meri
• Varga, Soma
• Lepomäki, Maiju
• Anttalainen, Osmo
• Kumpulainen, Pekka
• Oksala, Niku
• Roine, Antti
• Vehkaoja, Antti

Titel

Characterization of signal kinetics in real time surgical tissue classification system

Ist Teil von

• Sensors and actuators. B, Chemical, 2022-08, Vol.365, p.131902, Article 131902

Ort / Verlag

Lausanne: Elsevier B.V

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Effective surgical margin assessment is paramount for good oncological outcomes and new methods are in active development. One emerging approach is the analysis of the chemical composition of surgical smoke from tissues. Surgical smoke is typically removed with a smoke evacuator to protect the operating room staff from its harmful effects to the respiratory system. Thus, analysis of the evacuated smoke without disturbing the operation is a feasible approach. Smoke transportation is subject to lags that affect system usability. We analyzed the smoke transportation delay and evaluated its effects to tissue classification with differential mobility spectrometry in a simulated setting using porcine tissues. With a typical smoke evacuator setting, the front of the surgical plume reaches the analysis system in 380 ms and the sensor within one second. For a typical surgical incision (duration 1.5 s), the measured signal reaches its maximum in 2.3 s and declines to under 10% of the maximum in 8.6 s from the start of the incision. Two-class tissue classification was tested with 2, 3, 5, and 11 s repetition rates resulting in no significant differences in classification accuracy, implicating that signal retention from previous samples is mitigated by the classification algorithm. •Transfer kinetics of DMS based surgical margin system are determined.•Surgical evacuation tube functions as a chemical signal transfer line.•System impulse response resembles heavy tailed Levy distribution.•Signal strength influences the classification accuracy.•Sampling frequency had no effect on the tissue classification accuracy.