Prediction of the Facial Growth Direction is Challenging

Stanisław Kaźmierczak; Zofia Juszka; Vaska Vandevska-Radunovic; Thomas J.J. Maal; Piotr Fudalej; Jacek Mańdziuk

doi:10.1007/978-3-030-92310-5_77

Prediction of the Facial Growth Direction is Challenging

Stanisław Kaźmierczak^*, Zofia Juszka, Vaska Vandevska-Radunovic, Thomas J.J. Maal, Piotr Fudalej, Jacek Mańdziuk

^*Corresponding author for this work

Maxillofacial Surgery (AMC)

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

Abstract

Facial dysmorphology or malocclusion is frequently associated with abnormal growth of the face. The ability to predict facial growth (FG) direction would allow clinicians to prepare individualized therapy to increase the chance for successful treatment. Prediction of FG direction is a novel problem in the machine learning (ML) domain. In this paper, we perform feature selection and point the attribute that plays a central role in the abovementioned problem. Then we successfully apply data augmentation (DA) methods and improve the previously reported classification accuracy by 2.81%. Finally, we present the results of two experienced clinicians that were asked to solve a similar task to ours and show how tough is solving this problem for human experts.

Original language	English
Title of host publication	Neural Information Processing
Subtitle of host publication	28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part VI
Editors	Teddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	665-673
Number of pages	9
Volume	6
ISBN (Electronic)	9783030923105
ISBN (Print)	9783030923099
DOIs	https://doi.org/10.1007/978-3-030-92310-5_77
Publication status	Published - 2021
Event	28th International Conference on Neural Information Processing, ICONIP 2021 - Virtual, Online Duration: 8 Dec 2021 → 12 Dec 2021

Publication series

Name	Communications in Computer and Information Science
Volume	1517 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	28th International Conference on Neural Information Processing, ICONIP 2021
City	Virtual, Online
Period	8/12/21 → 12/12/21

Bibliographical note

Funding Information:
Studies were funded by BIOTECHMED-1 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (ID-UB). We would like to thank the custodian of AAOF Craniofacial Growth Legacy Collection for the possibility to use the lateral cephalograms from Craniofacial Growth Legacy Collection.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Funding

Studies were funded by BIOTECHMED-1 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (ID-UB). We would like to thank the custodian of AAOF Craniofacial Growth Legacy Collection for the possibility to use the lateral cephalograms from Craniofacial Growth Legacy Collection.

Keywords

Data augmentation
Facial growth
Orthodontics

Access to Document

10.1007/978-3-030-92310-5_77

Persistent URL (handle)

Persistent link

Cite this

Kaźmierczak, S., Juszka, Z., Vandevska-Radunovic, V., Maal, T. J. J., Fudalej, P., & Mańdziuk, J. (2021). Prediction of the Facial Growth Direction is Challenging. In T. Mantoro, M. Lee, M. A. Ayu, K. W. Wong, & A. N. Hidayanto (Eds.), Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part VI (Vol. 6, pp. 665-673). (Communications in Computer and Information Science; Vol. 1517 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-92310-5_77

Kaźmierczak, Stanisław ; Juszka, Zofia ; Vandevska-Radunovic, Vaska et al. / Prediction of the Facial Growth Direction is Challenging. Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part VI. editor / Teddy Mantoro ; Minho Lee ; Media Anugerah Ayu ; Kok Wai Wong ; Achmad Nizar Hidayanto. Vol. 6 Springer Science and Business Media Deutschland GmbH, 2021. pp. 665-673 (Communications in Computer and Information Science).

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abstract = "Facial dysmorphology or malocclusion is frequently associated with abnormal growth of the face. The ability to predict facial growth (FG) direction would allow clinicians to prepare individualized therapy to increase the chance for successful treatment. Prediction of FG direction is a novel problem in the machine learning (ML) domain. In this paper, we perform feature selection and point the attribute that plays a central role in the abovementioned problem. Then we successfully apply data augmentation (DA) methods and improve the previously reported classification accuracy by 2.81%. Finally, we present the results of two experienced clinicians that were asked to solve a similar task to ours and show how tough is solving this problem for human experts.",

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note = "Funding Information: Studies were funded by BIOTECHMED-1 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (ID-UB). We would like to thank the custodian of AAOF Craniofacial Growth Legacy Collection for the possibility to use the lateral cephalograms from Craniofacial Growth Legacy Collection. Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 28th International Conference on Neural Information Processing, ICONIP 2021 ; Conference date: 08-12-2021 Through 12-12-2021",

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Kaźmierczak, S, Juszka, Z, Vandevska-Radunovic, V, Maal, TJJ, Fudalej, P & Mańdziuk, J 2021, Prediction of the Facial Growth Direction is Challenging. in T Mantoro, M Lee, MA Ayu, KW Wong & AN Hidayanto (eds), Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part VI. vol. 6, Communications in Computer and Information Science, vol. 1517 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 665-673, 28th International Conference on Neural Information Processing, ICONIP 2021, Virtual, Online, 8/12/21. https://doi.org/10.1007/978-3-030-92310-5_77

Prediction of the Facial Growth Direction is Challenging. / Kaźmierczak, Stanisław; Juszka, Zofia; Vandevska-Radunovic, Vaska et al.
Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part VI. ed. / Teddy Mantoro; Minho Lee; Media Anugerah Ayu; Kok Wai Wong; Achmad Nizar Hidayanto. Vol. 6 Springer Science and Business Media Deutschland GmbH, 2021. p. 665-673 (Communications in Computer and Information Science; Vol. 1517 CCIS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

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AU - Maal, Thomas J.J.

AU - Fudalej, Piotr

AU - Mańdziuk, Jacek

N1 - Funding Information: Studies were funded by BIOTECHMED-1 project granted by Warsaw University of Technology under the program Excellence Initiative: Research University (ID-UB). We would like to thank the custodian of AAOF Craniofacial Growth Legacy Collection for the possibility to use the lateral cephalograms from Craniofacial Growth Legacy Collection. Publisher Copyright: © 2021, Springer Nature Switzerland AG.

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AB - Facial dysmorphology or malocclusion is frequently associated with abnormal growth of the face. The ability to predict facial growth (FG) direction would allow clinicians to prepare individualized therapy to increase the chance for successful treatment. Prediction of FG direction is a novel problem in the machine learning (ML) domain. In this paper, we perform feature selection and point the attribute that plays a central role in the abovementioned problem. Then we successfully apply data augmentation (DA) methods and improve the previously reported classification accuracy by 2.81%. Finally, we present the results of two experienced clinicians that were asked to solve a similar task to ours and show how tough is solving this problem for human experts.

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Kaźmierczak S, Juszka Z, Vandevska-Radunovic V, Maal TJJ, Fudalej P, Mańdziuk J. Prediction of the Facial Growth Direction is Challenging. In Mantoro T, Lee M, Ayu MA, Wong KW, Hidayanto AN, editors, Neural Information Processing: 28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8–12, 2021, Proceedings, Part VI. Vol. 6. Springer Science and Business Media Deutschland GmbH. 2021. p. 665-673. (Communications in Computer and Information Science). Epub 2021 Dec 2. doi: 10.1007/978-3-030-92310-5_77