IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0260194.html
   My bibliography  Save this article

Analysis of the mandibular canal course using unsupervised machine learning algorithm

Author

Listed:
  • Young Hyun Kim
  • Kug Jin Jeon
  • Chena Lee
  • Yoon Joo Choi
  • Hoi-In Jung
  • Sang-Sun Han

Abstract

Objectives: Anatomical structure classification is necessary task in medical field, but the inevitable variability of interpretation among experts makes reliable classification difficult. This study aims to introduce cluster analysis, unsupervised machine learning method, for classification of three-dimensional (3D) mandibular canal (MC) courses, and to visualize standard MC courses derived from cluster analysis in the Korean population. Materials and methods: A total of 429 cone-beam computed tomography images were used. Four sites in the mandible were selected for the measurement of the MC course and four parameters, two vertical and two horizontal parameters were measured per site. Cluster analysis was carried out as follows: parameter measurement, parameter normalization, cluster tendency evaluation, optimal number of clusters determination, and k-means cluster analysis. The 3D MC courses were classified into three types with statistically significant mean differences by cluster analysis. Results: Cluster 1 showed a smooth line running towards the lingual side in the axial view and a steep slope in the sagittal view. Cluster 2 ran in an almost straight line closest to the lingual and inferior border of mandible. Cluster 3 showed the pathway with a bent buccally in the axial view and an increasing slope in the sagittal view in the posterior area. Cluster 2 showed the highest distribution (42.1%), and males were more widely distributed (57.1%) than the females (42.9%). Cluster 3 comprised similar ratio of male and female cases and accounted for 31.9% of the total distribution. Cluster 1 had the least distribution (26.0%) Distributions of the right and left sides did not show a statistically significant difference. Conclusion: The MC courses were automatically classified as three types through cluster analysis. Cluster analysis enables the unbiased classification of the anatomical structures by reducing observer variability and can present representative standard information for each classified group.

Suggested Citation

  • Young Hyun Kim & Kug Jin Jeon & Chena Lee & Yoon Joo Choi & Hoi-In Jung & Sang-Sun Han, 2021. "Analysis of the mandibular canal course using unsupervised machine learning algorithm," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-13, November.
    • Handle: RePEc:plo:pone00:0260194
    DOI: 10.1371/journal.pone.0260194
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260194
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260194&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0260194?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023. "Bank business models, size, and profitability," Finance Research Letters, Elsevier, vol. 53(C).
    2. Reder, Maik & Yürüşen, Nurseda Y. & Melero, Julio J., 2018. "Data-driven learning framework for associating weather conditions and wind turbine failures," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 554-569.
    3. Marcin Gąsior, 2021. "Environmental Attitudes and Willingness to Purchase Online—Classification Approach," Sustainability, MDPI, vol. 13(15), pages 1-17, August.
    4. Roopam Shukla & Ankit Agarwal & Kamna Sachdeva & Juergen Kurths & P. K. Joshi, 2019. "Climate change perception: an analysis of climate change and risk perceptions among farmer types of Indian Western Himalayas," Climatic Change, Springer, vol. 152(1), pages 103-119, January.
    5. Saemi Shin & Won Suck Yoon & Sang-Hoon Byeon, 2022. "Trends in Occupational Infectious Diseases in South Korea and Classification of Industries According to the Risk of Biological Hazards Using K-Means Clustering," IJERPH, MDPI, vol. 19(19), pages 1-19, September.
    6. Song He & Xinyu Song & Xiaoxi Yang & Jijun Yu & Yuqi Wen & Lianlian Wu & Bowei Yan & Jiannan Feng & Xiaochen Bo, 2021. "COMSUC: A web server for the identification of consensus molecular subtypes of cancer based on multiple methods and multi-omics data," PLOS Computational Biology, Public Library of Science, vol. 17(3), pages 1-10, March.
    7. Jihane El Ouadi & Hanae Errousso & Nicolas Malhene & Siham Benhadou & Hicham Medromi, 2022. "A machine-learning based hybrid algorithm for strategic location of urban bundling hubs to support shared public transport," Quality & Quantity: International Journal of Methodology, Springer, vol. 56(5), pages 3215-3258, October.
    8. Cyril Atkinson-Clement & Eléonore Pigalle, 2021. "What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.
    9. Kreitmair, Ursula & Bower-Bir, Jacob, 2021. "Too different to solve climate change? Experimental evidence on the effects of production and benefit heterogeneity on collective action," Ecological Economics, Elsevier, vol. 184(C).
    10. Getaneh Addis Tessema & Jan van der Borg & Anton Van Rompaey & Steven Van Passel & Enyew Adgo & Amare Sewnet Minale & Kerebih Asrese & Amaury Frankl & Jean Poesen, 2022. "Benefit Segmentation of Tourists to Geosites and Its Implications for Sustainable Development of Geotourism in the Southern Lake Tana Region, Ethiopia," Sustainability, MDPI, vol. 14(6), pages 1-25, March.
    11. Wu, Tong & Rocha, Juan C. & Berry, Kevin & Chaigneau, Tomas & Hamann, Maike & Lindkvist, Emilie & Qiu, Jiangxiao & Schill, Caroline & Shepon, Alon & Crépin, Anne-Sophie & Folke, Carl, 2024. "Triple Bottom Line or Trilemma? Global Tradeoffs Between Prosperity, Inequality, and the Environment," World Development, Elsevier, vol. 178(C).
    12. Petricli, Gulcan & Inkaya, Tulin & Gokay Emel, Gul, 2024. "Identifying green citizen typologies by mining household-level survey data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    13. Turati, Pietro & Pedroni, Nicola & Zio, Enrico, 2017. "Simulation-based exploration of high-dimensional system models for identifying unexpected events," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 317-330.
    14. Ben Beck & Meghan Winters & Trisalyn Nelson & Chris Pettit & Simone Z Leao & Meead Saberi & Jason Thompson & Sachith Seneviratne & Kerry Nice & Mark Stevenson, 2023. "Developing urban biking typologies: Quantifying the complex interactions of bicycle ridership, bicycle network and built environment characteristics," Environment and Planning B, , vol. 50(1), pages 7-23, January.
    15. Haytham Mohamed Salem & Linda R. Schott & Julia Piaskowski & Asmita Chapagain & Jenifer L. Yost & Erin Brooks & Kendall Kahl & Jodi Johnson-Maynard, 2024. "Evaluating Intra-Field Spatial Variability for Nutrient Management Zone Delineation through Geospatial Techniques and Multivariate Analysis," Sustainability, MDPI, vol. 16(2), pages 1-23, January.
    16. Raquel Lourenço Carvalhal Monteiro & Valdecy Pereira & Helder Gomes Costa, 2019. "Analysis of the Better Life Index Trough a Cluster Algorithm," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(2), pages 477-506, April.
    17. Yürüşen, Nurseda Y. & Rowley, Paul N. & Watson, Simon J. & Melero, Julio J., 2020. "Automated wind turbine maintenance scheduling," Reliability Engineering and System Safety, Elsevier, vol. 200(C).
    18. Magacho, Guilherme & Espagne, Etienne & Godin, Antoine & Mantes, Achilleas & Yilmaz, Devrim, 2023. "Macroeconomic exposure of developing economies to low-carbon transition," World Development, Elsevier, vol. 167(C).
    19. Peter Radchenko & Gourab Mukherjee, 2017. "Convex clustering via l 1 fusion penalization," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1527-1546, November.
    20. Federica Scucchia & Paul Zaslansky & Chloë Boote & Annabelle Doheny & Tali Mass & Emma F. Camp, 2023. "The role and risks of selective adaptation in extreme coral habitats," Nature Communications, Nature, vol. 14(1), pages 1-12, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0260194. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.