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

General Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 (US)
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 and Business Media B.V.
Pages	89-96
Number of pages	8
ISBN (Print)	9783030761462
DOIs	https://doi.org/10.1007/978-3-030-76147-9_10
State	Published - 2022
Event	7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021 - Virtual, Online Duration: Jun 7 2021 → Jun 9 2021

Publication series

Name	Mechanisms and Machine Science
Volume	106 MMS
ISSN (Print)	2211-0984
ISSN (Electronic)	2211-0992

Conference

Conference	7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021
City	Virtual, Online
Period	6/7/21 → 6/9/21

ASJC Scopus subject areas

Mechanics of Materials
Mechanical Engineering

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 (pp. 89-96). (Mechanisms and Machine Science; Vol. 106 MMS). Springer Science and Business Media B.V.. 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. Springer Science and Business Media B.V., 2022. p. 89-96 (Mechanisms and Machine Science; Vol. 106 MMS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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. Mechanisms and Machine Science, vol. 106 MMS, Springer Science and Business Media B.V., pp. 89-96, 7th International Workshop on New Trends in Medical and Service Robotics, MESROB 2021, Virtual, Online, 6/7/21. https://doi.org/10.1007/978-3-030-76147-9_10

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. Springer Science and Business Media B.V. 2022. p. 89-96. (Mechanisms and Machine Science). doi: 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. Springer Science and Business Media B.V., 2022. pp. 89-96 (Mechanisms and Machine Science).

@inproceedings{bb34c19706584aefa2eee49b84397864,

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 (0–25) 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 = "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",

year = "2022",

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

language = "English (US)",

isbn = "9783030761462",

series = "Mechanisms and Machine Science",

publisher = "Springer Science and Business Media B.V.",

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",

booktitle = "New Trends in Medical and Service Robotics, MESROB 2021",

}

TY - GEN

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

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 - http://www.scopus.com/inward/record.url?scp=85135094054&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85135094054&partnerID=8YFLogxK

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

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

M3 - Conference contribution

AN - SCOPUS:85135094054

SN - 9783030761462

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 and Business Media B.V.

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

Y2 - 7 June 2021 through 9 June 2021

ER -

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

Abstract

Publication series

Conference

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this