Multi-class biological tissue classification based on a multi-classifier: Preliminary study of an automatic output power control for ultrasonic surgical units

Abstract Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target ti...

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Bibliographic Details
Published in:Computers in biology and medicine 2015-06, Vol.61, p.92-100
Main Authors: Youn, Su Hyun, Sim, Taeyong, Choi, Ahnryul, Song, Jinsung, Shin, Ki Young, Lee, Il Kwon, Heo, Hyun Mu, Lee, Daeweon, Mun, Joung Hwan
Format: Article
Language:English
Subjects:
Abdomen
Accuracy
Algorithms
Classification
Electrodes
Humans
Impedance
Internal Medicine
Laparoscopy
Liver
Models, Theoretical
Multi-classifier
Other
Power auto-control
Studies
Surgery
Tissue classification
Ultrasonic Surgical Procedures - instrumentation
Ultrasonic Surgical Procedures - methods
Ultrasonic surgical units
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Summary:Abstract Ultrasonic surgical units (USUs) have the advantage of minimizing tissue damage during surgeries that require tissue dissection by reducing problems such as coagulation and unwanted carbonization, but the disadvantage of requiring manual adjustment of power output according to the target tissue. In order to overcome this limitation, it is necessary to determine the properties of in vivo tissues automatically. We propose a multi-classifier that can accurately classify tissues based on the unique impedance of each tissue. For this purpose, a multi-classifier was built based on single classifiers with high classification rates, and the classification accuracy of the proposed model was compared with that of single classifiers for various electrode types (Type-I: 6 mm invasive; Type-II: 3 mm invasive; Type-III: surface). The sensitivity and positive predictive value (PPV) of the multi-classifier by cross checks were determined. According to the 10-fold cross validation results, the classification accuracy of the proposed model was significantly higher ( p
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2015.03.021