Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy

Ferda Canbaz; Hamed Abbasi; Yakub A. Bayhaqi; Philippe C. Cattin; Azhar Zam

doi:10.1007/978-3-030-76147-9_10

Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy

Ferda Canbaz^*
, Hamed Abbasi
, Yakub A. Bayhaqi
, Philippe C. Cattin
, Azhar Zam

^*Corresponding author for this work

University of Basel

Research output: Chapter/Conference proceeding › Chapter › Academic

Abstract

To obtain efficient laser ablation in bone, dehydration, early carbonization and carbonization need to be avoided. Achieving this can only be provided by using an automated control of the ablation laser and irrigation system. As a preliminary study, we demonstrated a laser-induced breakdown spectroscopy based early carbonization detection system by analyzing carbonized bone tissues. Carbonization of bone samples was generated in a controlled way, by applying different number of Er:YAG pulses (0–25) at different locations on bone sample. To detect number of applied pulses, leading to the detection of carbonization level, we used a feed-forward Artificial Neural Network (ANN) with multi-layer perceptron structure. The results of the ANN were compared with the actual label, and R-squared of 0.85, 0.88, 0.86, 0.83, and 0.84 (0.85 on average) were achieved.

Original language	English
Title of host publication	New Trends in Medical and Service Robotics, MESROB 2021
Editors	Georg Rauter, Giuseppe Carbone, Philippe C. Cattin, Azhar Zam, Doina Pisla, Robert Riener, Robert Riener
Publisher	Springer Science+Business Media
Pages	89-96
Number of pages	8
Volume	106
ISBN (Print)	9783030761462
DOIs	https://doi.org/10.1007/978-3-030-76147-9_10
Publication status	Published - 2022
Externally published	Yes
Event	7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021 - Virtual, Online Duration: 7 Jun 2021 → 9 Jun 2021

Publication series

Series	Mechanisms and Machine Science
Volume	106 MMS
ISSN	2211-0984

Conference

Conference	7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021
City	Virtual, Online
Period	7/06/21 → 9/06/21

Bibliographical note

Funding Information:
Acknowledgments. The authors gratefully acknowledge funding of the Werner Siemens Foundation through the Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE (MIRACLE) project.

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-030-76147-9_10

Cite this

Canbaz, F., Abbasi, H., Bayhaqi, Y. A., Cattin, P. C., & Zam, A. (2022). Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy. In G. Rauter, G. Carbone, P. C. Cattin, A. Zam, D. Pisla, R. Riener, & R. Riener (Eds.), New Trends in Medical and Service Robotics, MESROB 2021 (Vol. 106, pp. 89-96). Springer Science+Business Media. https://doi.org/10.1007/978-3-030-76147-9_10

Canbaz, Ferda ; Abbasi, Hamed ; Bayhaqi, Yakub A. et al. / Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy. New Trends in Medical and Service Robotics, MESROB 2021. editor / Georg Rauter ; Giuseppe Carbone ; Philippe C. Cattin ; Azhar Zam ; Doina Pisla ; Robert Riener ; Robert Riener. Vol. 106 Springer Science+Business Media, 2022. pp. 89-96 (Mechanisms and Machine Science, Vol. 106 MMS).

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title = "Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy",

abstract = "To obtain efficient laser ablation in bone, dehydration, early carbonization and carbonization need to be avoided. Achieving this can only be provided by using an automated control of the ablation laser and irrigation system. As a preliminary study, we demonstrated a laser-induced breakdown spectroscopy based early carbonization detection system by analyzing carbonized bone tissues. Carbonization of bone samples was generated in a controlled way, by applying different number of Er:YAG pulses (0{\textendash}25) at different locations on bone sample. To detect number of applied pulses, leading to the detection of carbonization level, we used a feed-forward Artificial Neural Network (ANN) with multi-layer perceptron structure. The results of the ANN were compared with the actual label, and R-squared of 0.85, 0.88, 0.86, 0.83, and 0.84 (0.85 on average) were achieved.",

author = "Ferda Canbaz and Hamed Abbasi and Bayhaqi, \{Yakub A.\} and Cattin, \{Philippe C.\} and Azhar Zam",

note = "Funding Information: Acknowledgments. The authors gratefully acknowledge funding of the Werner Siemens Foundation through the Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE (MIRACLE) project. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021 ; Conference date: 07-06-2021 Through 09-06-2021",

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doi = "10.1007/978-3-030-76147-9\_10",

language = "English",

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volume = "106",

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publisher = "Springer Science+Business Media",

pages = "89--96",

editor = "Georg Rauter and Giuseppe Carbone and Cattin, \{Philippe C.\} and Azhar Zam and Doina Pisla and Robert Riener and Robert Riener",

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Canbaz, F, Abbasi, H, Bayhaqi, YA, Cattin, PC & Zam, A 2022, Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy. in G Rauter, G Carbone, PC Cattin, A Zam, D Pisla, R Riener & R Riener (eds), New Trends in Medical and Service Robotics, MESROB 2021. vol. 106, Springer Science+Business Media, Mechanisms and Machine Science, vol. 106 MMS, pp. 89-96, 7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021, Virtual, Online, 7/06/21. https://doi.org/10.1007/978-3-030-76147-9_10

Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy. / Canbaz, Ferda; Abbasi, Hamed; Bayhaqi, Yakub A. et al.
New Trends in Medical and Service Robotics, MESROB 2021. ed. / Georg Rauter; Giuseppe Carbone; Philippe C. Cattin; Azhar Zam; Doina Pisla; Robert Riener; Robert Riener. Vol. 106 Springer Science+Business Media, 2022. p. 89-96 (Mechanisms and Machine Science, Vol. 106 MMS).

Research output: Chapter/Conference proceeding › Chapter › Academic

TY - CHAP

T1 - Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy

AU - Canbaz, Ferda

AU - Abbasi, Hamed

AU - Bayhaqi, Yakub A.

AU - Cattin, Philippe C.

AU - Zam, Azhar

N1 - Funding Information: Acknowledgments. The authors gratefully acknowledge funding of the Werner Siemens Foundation through the Minimally Invasive Robot-Assisted Computer-guided LaserosteotomE (MIRACLE) project. Publisher Copyright: © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

PY - 2022

Y1 - 2022

N2 - To obtain efficient laser ablation in bone, dehydration, early carbonization and carbonization need to be avoided. Achieving this can only be provided by using an automated control of the ablation laser and irrigation system. As a preliminary study, we demonstrated a laser-induced breakdown spectroscopy based early carbonization detection system by analyzing carbonized bone tissues. Carbonization of bone samples was generated in a controlled way, by applying different number of Er:YAG pulses (0–25) at different locations on bone sample. To detect number of applied pulses, leading to the detection of carbonization level, we used a feed-forward Artificial Neural Network (ANN) with multi-layer perceptron structure. The results of the ANN were compared with the actual label, and R-squared of 0.85, 0.88, 0.86, 0.83, and 0.84 (0.85 on average) were achieved.

AB - To obtain efficient laser ablation in bone, dehydration, early carbonization and carbonization need to be avoided. Achieving this can only be provided by using an automated control of the ablation laser and irrigation system. As a preliminary study, we demonstrated a laser-induced breakdown spectroscopy based early carbonization detection system by analyzing carbonized bone tissues. Carbonization of bone samples was generated in a controlled way, by applying different number of Er:YAG pulses (0–25) at different locations on bone sample. To detect number of applied pulses, leading to the detection of carbonization level, we used a feed-forward Artificial Neural Network (ANN) with multi-layer perceptron structure. The results of the ANN were compared with the actual label, and R-squared of 0.85, 0.88, 0.86, 0.83, and 0.84 (0.85 on average) were achieved.

UR - https://www.scopus.com/pages/publications/85135094054

U2 - 10.1007/978-3-030-76147-9_10

DO - 10.1007/978-3-030-76147-9_10

M3 - Chapter

AN - SCOPUS:85135094054

SN - 9783030761462

VL - 106

T3 - Mechanisms and Machine Science

SP - 89

EP - 96

BT - New Trends in Medical and Service Robotics, MESROB 2021

A2 - Rauter, Georg

A2 - Carbone, Giuseppe

A2 - Cattin, Philippe C.

A2 - Zam, Azhar

A2 - Pisla, Doina

A2 - Riener, Robert

PB - Springer Science+Business Media

T2 - 7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021

Y2 - 7 June 2021 through 9 June 2021

ER -

Canbaz F, Abbasi H, Bayhaqi YA, Cattin PC, Zam A. Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy. In Rauter G, Carbone G, Cattin PC, Zam A, Pisla D, Riener R, Riener R, editors, New Trends in Medical and Service Robotics, MESROB 2021. Vol. 106. Springer Science+Business Media. 2022. p. 89-96. (Mechanisms and Machine Science, Vol. 106 MMS). Epub 2022 Jul 24. doi: 10.1007/978-3-030-76147-9_10

Laser-Induced Breakdown Spectroscopy Combined with Artificial Neural Network for Pre-carbonization Detection in Laserosteotomy

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