Classifying small volumes of tissue for real-time monitoring radiofrequency ablation

Emre Besler, Yearnchee Curtis Wang, Terence Chan, Alan Varteres Sahakian*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

An increasingly-popular treatment for ablation of cancerous and non-cancerous masses is thermal ablation by radiofrequency joule heating. Real-time monitoring of the thermal tissue ablation process is essential in order to maintain the reliability of the treatment technique. Common methods for monitoring the extent of ablation have proven to be accurate, though they are time-consuming and often require powerful computers to run on, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. In this study, a Machine Learning (ML) approach is presented to reduce the time to calculate the progress of ablation while keeping the accuracy of the conventional methods. Different setups were used to perform the ablation and collect impedance data at the same time and different ML algorithms were tested to predict the ablation depth in three dimensions, based on the collected data. In the end, it is shown that an optimal pair of hardware setup and ML algorithm were able to control the ablation by estimating the lesion depth within an average of micrometer-magnitude error range while keeping the estimation time within 5.5 s on conventional x86-64 computing hardware.

Original languageEnglish (US)
Title of host publicationArtificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings
EditorsSzymon Wilk, Annette ten Teije, David Riaño
PublisherSpringer Verlag
Pages205-215
Number of pages11
ISBN (Print)9783030216412
DOIs
StatePublished - Jan 1 2019
Event17th Conference on Artificial Intelligence in Medicine, AIME 2019 - Poznan, Poland
Duration: Jun 26 2019Jun 29 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11526 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Conference on Artificial Intelligence in Medicine, AIME 2019
CountryPoland
CityPoznan
Period6/26/196/29/19

Fingerprint

Ablation
Monitoring
Tissue
Real-time
Learning systems
Machine Learning
Learning algorithms
Learning Algorithm
Hardware
Joule Heating
Joule heating
Impedance
Three-dimension
Calculate
Predict
Computing

Keywords

  • Ablation
  • Artificial intelligence
  • Data
  • Ensemble
  • Lesion
  • Machine learning
  • Monitoring
  • Radiofrequency

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Besler, E., Wang, Y. C., Chan, T., & Sahakian, A. V. (2019). Classifying small volumes of tissue for real-time monitoring radiofrequency ablation. In S. Wilk, A. ten Teije, & D. Riaño (Eds.), Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings (pp. 205-215). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-21642-9_26
Besler, Emre ; Wang, Yearnchee Curtis ; Chan, Terence ; Sahakian, Alan Varteres. / Classifying small volumes of tissue for real-time monitoring radiofrequency ablation. Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. editor / Szymon Wilk ; Annette ten Teije ; David Riaño. Springer Verlag, 2019. pp. 205-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{608f79e538424af595b073927a0a7e3e,
title = "Classifying small volumes of tissue for real-time monitoring radiofrequency ablation",
abstract = "An increasingly-popular treatment for ablation of cancerous and non-cancerous masses is thermal ablation by radiofrequency joule heating. Real-time monitoring of the thermal tissue ablation process is essential in order to maintain the reliability of the treatment technique. Common methods for monitoring the extent of ablation have proven to be accurate, though they are time-consuming and often require powerful computers to run on, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. In this study, a Machine Learning (ML) approach is presented to reduce the time to calculate the progress of ablation while keeping the accuracy of the conventional methods. Different setups were used to perform the ablation and collect impedance data at the same time and different ML algorithms were tested to predict the ablation depth in three dimensions, based on the collected data. In the end, it is shown that an optimal pair of hardware setup and ML algorithm were able to control the ablation by estimating the lesion depth within an average of micrometer-magnitude error range while keeping the estimation time within 5.5 s on conventional x86-64 computing hardware.",
keywords = "Ablation, Artificial intelligence, Data, Ensemble, Lesion, Machine learning, Monitoring, Radiofrequency",
author = "Emre Besler and Wang, {Yearnchee Curtis} and Terence Chan and Sahakian, {Alan Varteres}",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-21642-9_26",
language = "English (US)",
isbn = "9783030216412",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "205--215",
editor = "Szymon Wilk and {ten Teije}, Annette and David Ria{\~n}o",
booktitle = "Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings",
address = "Germany",

}

Besler, E, Wang, YC, Chan, T & Sahakian, AV 2019, Classifying small volumes of tissue for real-time monitoring radiofrequency ablation. in S Wilk, A ten Teije & D Riaño (eds), Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11526 LNAI, Springer Verlag, pp. 205-215, 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Poznan, Poland, 6/26/19. https://doi.org/10.1007/978-3-030-21642-9_26

Classifying small volumes of tissue for real-time monitoring radiofrequency ablation. / Besler, Emre; Wang, Yearnchee Curtis; Chan, Terence; Sahakian, Alan Varteres.

Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. ed. / Szymon Wilk; Annette ten Teije; David Riaño. Springer Verlag, 2019. p. 205-215 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11526 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Classifying small volumes of tissue for real-time monitoring radiofrequency ablation

AU - Besler, Emre

AU - Wang, Yearnchee Curtis

AU - Chan, Terence

AU - Sahakian, Alan Varteres

PY - 2019/1/1

Y1 - 2019/1/1

N2 - An increasingly-popular treatment for ablation of cancerous and non-cancerous masses is thermal ablation by radiofrequency joule heating. Real-time monitoring of the thermal tissue ablation process is essential in order to maintain the reliability of the treatment technique. Common methods for monitoring the extent of ablation have proven to be accurate, though they are time-consuming and often require powerful computers to run on, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. In this study, a Machine Learning (ML) approach is presented to reduce the time to calculate the progress of ablation while keeping the accuracy of the conventional methods. Different setups were used to perform the ablation and collect impedance data at the same time and different ML algorithms were tested to predict the ablation depth in three dimensions, based on the collected data. In the end, it is shown that an optimal pair of hardware setup and ML algorithm were able to control the ablation by estimating the lesion depth within an average of micrometer-magnitude error range while keeping the estimation time within 5.5 s on conventional x86-64 computing hardware.

AB - An increasingly-popular treatment for ablation of cancerous and non-cancerous masses is thermal ablation by radiofrequency joule heating. Real-time monitoring of the thermal tissue ablation process is essential in order to maintain the reliability of the treatment technique. Common methods for monitoring the extent of ablation have proven to be accurate, though they are time-consuming and often require powerful computers to run on, which makes the clinical ablation process more cumbersome and expensive due to the time-dependent nature of the clinical procedure. In this study, a Machine Learning (ML) approach is presented to reduce the time to calculate the progress of ablation while keeping the accuracy of the conventional methods. Different setups were used to perform the ablation and collect impedance data at the same time and different ML algorithms were tested to predict the ablation depth in three dimensions, based on the collected data. In the end, it is shown that an optimal pair of hardware setup and ML algorithm were able to control the ablation by estimating the lesion depth within an average of micrometer-magnitude error range while keeping the estimation time within 5.5 s on conventional x86-64 computing hardware.

KW - Ablation

KW - Artificial intelligence

KW - Data

KW - Ensemble

KW - Lesion

KW - Machine learning

KW - Monitoring

KW - Radiofrequency

UR - http://www.scopus.com/inward/record.url?scp=85068349429&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068349429&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-21642-9_26

DO - 10.1007/978-3-030-21642-9_26

M3 - Conference contribution

AN - SCOPUS:85068349429

SN - 9783030216412

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 205

EP - 215

BT - Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings

A2 - Wilk, Szymon

A2 - ten Teije, Annette

A2 - Riaño, David

PB - Springer Verlag

ER -

Besler E, Wang YC, Chan T, Sahakian AV. Classifying small volumes of tissue for real-time monitoring radiofrequency ablation. In Wilk S, ten Teije A, Riaño D, editors, Artificial Intelligence in Medicine - 17th Conference on Artificial Intelligence in Medicine, AIME 2019, Proceedings. Springer Verlag. 2019. p. 205-215. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-21642-9_26