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Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea

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Abstract

Objectives

The objectives of this study included using the cone beam computed tomography (CBCT) technology to assess: (1) intra- and inter-observer reliability of the volume measurement of the nasal cavity; (2) the accuracy of the segmentation protocol for evaluation of the nasal cavity.

Materials and methods

This study used test–retest reliability and accuracy methods within two different population sample groups, from Eastern Asia and North America. Thirty obstructive sleep apnea (OSA) patients were randomly selected from administrative and research oral health data archived at two dental faculties in China and Canada. To assess the reliability of the protocol, two observers performed nasal cavity volume measurement twice with a 10-day interval, using Amira software (v4.1, Visage Imaging Inc., Carlsbad, CA). The accuracy study used a computerized tomography (CT) scan of an OSA patient, who was not included in the study sample, to fabricate an anthropomorphic phantom of the nasal cavity volume with known dimensions (18.9 ml, gold standard). This phantom was scanned using one NewTom 5G (QR systems, Verona, Italy) CBCT scanner. The nasal cavity was segmented based on CBCT images and converted into standard tessellation language (STL) models. The volume of the nasal cavity was measured on the acquired STL models (18.99 ± 0.066 ml).

Results

The intra-observer and inter-observer intraclass correlation coefficients for the volume measurement of the nasal cavity were 0.980–0.997 and 0.948–0.992 consecutively. The nasal cavity volume measurement was overestimated by 1.1%-3.1%, compared to the gold standard.

Conclusions

The semi-automatic segmentation protocol of the nasal cavity in patients with sleep apnea and by using cone beam computed tomography is reliable and accurate.

Clinical relevance

This study provides a reliable and accurate protocol for segmentation of nasal cavity, which will facilitate the clinician to analyze the images within nasoethmoidal region.

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Data Availability

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Patel RG (2017) Nasal anatomy and function. Facial Plast Surg 33(1):3–8. https://doi.org/10.1055/s-0036-1597950

    Article  PubMed  Google Scholar 

  2. Tingelhoff K, Moral AI, Kunkel ME, Rilk M, Wagner I, Eichhorn KG, Wahl FM, Bootz F (2007) Comparison between manual and semi-automatic segmentation of nasal cavity and paranasal sinuses from CT images. Conf Proceed: Annu Int Conf IEEE Eng Med Biol Soc. Annual Conference 2007:5505–8. https://doi.org/10.1109/iembs.2007.4353592

  3. Lofaso F, Coste A, d’Ortho MP, Zerah-Lancner F, Delclaux C, Goldenberg F, Harf A (2000) Nasal obstruction as a risk factor for sleep apnoea syndrome. Eur Respir J 16(4):639–643

    Article  PubMed  Google Scholar 

  4. McNicholas WT (2008) The nose and OSA: variable nasal obstruction may be more important in pathophysiology than fixed obstruction. European Respir J 32(1):3–8. https://doi.org/10.1183/09031936.00050208

    Article  Google Scholar 

  5. Kohler M, Bloch KE, Stradling JR (2007) The role of the nose in the pathogenesis of obstructive sleep apnoea and snoring. Eur Respir J 30(6):1208–15. https://doi.org/10.1183/09031936.00032007

    Article  PubMed  Google Scholar 

  6. Sériès F, St Pierre S, Carrier G (1993) Surgical correction of nasal obstruction in the treatment of mild sleep apnoea: importance of cephalometry in predicting outcome. Thorax 48(4):360–3. https://doi.org/10.1136/thx.48.4.360

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pang KP (2013) The role of nasal surgery in the treatment of OSA. Curr Otorhinolaryngol Rep 1(1):20–4. https://doi.org/10.1007/s40136-012-0007-0

    Article  Google Scholar 

  8. Hoel HC, Kvinnesland K, Berg S (2020) Impact of nasal resistance on the distribution of apneas and hypopneas in obstructive sleep apnea. Sleep Med 71:83–8. https://doi.org/10.1016/j.sleep.2020.03.024

    Article  PubMed  Google Scholar 

  9. Parks ET (2014) Cone beam computed tomography for the nasal cavity and paranasal sinuses. Dent Clin North Am 58(3):627–51. https://doi.org/10.1016/j.cden.2014.04.003

    Article  PubMed  Google Scholar 

  10. Keeler JA, Patki A, Woodard CR, Frank-Ito DO (2016) A computational study of nasal spray deposition pattern in four ethnic groups. J Aerosol Med Pulm Drug Deliv 29(2):153–66. https://doi.org/10.1089/jamp.2014.1205

    Article  PubMed  PubMed Central  Google Scholar 

  11. Niu X, Madhan S, Cornelis MA, Cattaneo PM (2021) Novel three-dimensional methods to analyze the morphology of the nasal cavity and pharyngeal airway. Angle Orthod 91(3):320–8. https://doi.org/10.2319/070620-610.1

    Article  PubMed  PubMed Central  Google Scholar 

  12. Guijarro-Martínez R, Swennen GR (2011) Cone-beam computerized tomography imaging and analysis of the upper airway: a systematic review of the literature. Int J Oral Maxillofac Surg 40(11):1227–37. https://doi.org/10.1016/j.ijom.2011.06.017

    Article  PubMed  Google Scholar 

  13. Dawood A, Patel S, Brown J (2009) Cone beam CT in dental practice. Br Dent J 207:23. https://doi.org/10.1038/sj.bdj.2009.560

    Article  PubMed  Google Scholar 

  14. Tsolakis IA, Kolokitha OE, Papadopoulou E, Tsolakis AI, Kilipiris EG, Palomo JM (2022) Artificial intelligence as an aid in CBCT airway analysis: a systematic review. Life (Basel) 12(11):1894. https://doi.org/10.3390/life12111894

    Article  PubMed  Google Scholar 

  15. Maken P, Gupta A, Gupta MK (2023) A systematic review of the techniques for automatic segmentation of the human upper airway using volumetric images. Med Biol Eng Comput 61(8):1901–27. https://doi.org/10.1007/s11517-023-02842-x

    Article  PubMed  Google Scholar 

  16. Shujaat S, Jazil O, Willems H, Van Gerven A, Shaheen E, Politis C et al (2021) Automatic segmentation of the pharyngeal airway space with convolutional neural network. J Dent 111:103705. https://doi.org/10.1016/j.jdent.2021.103705

    Article  PubMed  Google Scholar 

  17. de Bataille C, Bernard D, Dumoncel J, Vaysse F, Cussat-Blanc S, Telmon N et al (2022) Machine learning analysis of the anatomical parameters of the upper airway morphology: a retrospective study from cone-beam CT examinations in a French population. J Clin Med 12(1):84. https://doi.org/10.3390/jcm12010084

    Article  PubMed  PubMed Central  Google Scholar 

  18. Alsufyani NA, Hess A, Noga M, Ray N, Al-Saleh MAQ, Lagravère MO, Major PW (2016) New algorithm for semiautomatic segmentation of nasal cavity and pharyngeal airway in comparison with manual segmentation using cone-beam computed tomography. Am J Orthod Dentofacial Orthop 150(4):703–712. https://doi.org/10.1016/j.ajodo.2016.06.024

    Article  PubMed  Google Scholar 

  19. Bui NL, Ong SH, Foong KWC (2015) Automatic segmentation of the nasal cavity and paranasal sinuses from cone-beam CT images. Int J CARS 10(8):1269–77. https://doi.org/10.1007/s11548-014-1134-5

    Article  Google Scholar 

  20. Sin Ç, Akkaya N, Aksoy S, Orhan K, Öz U (2021) A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res 24(Suppl 2):117–23. https://doi.org/10.1111/ocr.12480

    Article  PubMed  Google Scholar 

  21. Keustermans W, Huysmans T, Schmelzer B, Sijbers J, Dirckx JJ (2019) Matlab(®) toolbox for semi-automatic segmentation of the human nasal cavity based on active shape modeling. Comput Biol Med 105:27–38. https://doi.org/10.1016/j.compbiomed.2018.12.008

    Article  PubMed  Google Scholar 

  22. Chen H, van Eijnatten M, Wolff J, de Lange J, van der Stelt PF, Lobbezoo F, Aarab G (2017) Reliability and accuracy of three imaging software packages used for 3D analysis of the upper airway on cone beam computed tomography images. Dentomaxillofac Radiol 46(6):20170043. https://doi.org/10.1259/dmfr.20170043

    Article  PubMed  PubMed Central  Google Scholar 

  23. Emami E, Lavigne G, Feine JS, Karp I, Rompré PH, Almeida FR, Huynh NT (2021) Effects of nocturnal wearing of dentures on the quality of sleep and oral-health-related quality in edentate elders with untreated sleep apnea: a randomized cross-over trial. Sleep 44(10):zsab101. https://doi.org/10.1093/sleep/zsab101

  24. Shi X, Chen H, Lobbezoo F, de Lange J, van der Stelt P, Berkhout E et al (2023) Comparison of the upper airway morphology between Dutch and Chinese adults with obstructive sleep apnea. Sleep Breath. https://doi.org/10.1007/s11325-023-02834-z

    Article  PubMed  Google Scholar 

  25. Weissheimer A, Menezes LM, Sameshima GT, Enciso R, Pham J, Grauer D (2012) Imaging software accuracy for 3-dimensional analysis of the upper airway. Am J Orthod Dentofacial Orthop 142(6):801–13. https://doi.org/10.1016/j.ajodo.2012.07.015

    Article  PubMed  Google Scholar 

  26. Walter SDEM, Donner A (1998) Sample size and optimal designs for reliability studies. Stat Med 17:101–110. https://doi.org/10.1002/(sici)1097-0258(19980115)17:1%3c101::aid-sim727%3e3.0.co;2-e

    Article  PubMed  Google Scholar 

  27. Chen H, Aarab G, Parsa A, de Lange J, van der Stelt PF, Lobbezoo F (2016) Reliability of three-dimensional measurements of the upper airway on cone beam computed tomography images. Oral Surg Oral Med Oral Pathol Oral Radiol 122(1):104–10. https://doi.org/10.1016/j.oooo.2016.04.005

    Article  PubMed  Google Scholar 

  28. Venkatesh E, Elluru SV (2017) Cone beam computed tomography: basics and applications in dentistry. J Istanb Univ Fac Dent 51(3 Suppl 1):S102-s21. https://doi.org/10.17096/jiufd.00289

    Article  PubMed  PubMed Central  Google Scholar 

  29. von Arx T, Lozanoff S, Bornstein MM (2019) Extraoral anatomy in CBCT - a literature review. Part 1: Nasoethmoidal region. Swiss Dent J 129(10):804–15

    Google Scholar 

  30. Cai Y, Goldberg AN, Chang JL (2020) The nose and nasal breathing in sleep apnea. Otolaryngol Clin North Am 53(3):385–95. https://doi.org/10.1016/j.otc.2020.02.002

    Article  PubMed  Google Scholar 

  31. Mekhitarian Neto L, Fava AS, Lopes HC, Stamm A (2005) Epidemiological analysis of structural alterations of the nasal cavity associated with obstructive sleep apnea syndrome (OSA). Braz J Otorhinolaryngol 71(4):464–6. https://doi.org/10.1016/s1808-8694(15)31200-3

    Article  PubMed  Google Scholar 

  32. Hafezeqoran A, Rahbar M, Koodaryan R, Molaei T (2021) Comparing the dimensional accuracy of casts obtained from two types of silicone impression materials in different impression techniques and frequent times of cast preparation. Int J Dent 2021:9977478. https://doi.org/10.1155/2021/9977478

    Article  PubMed  PubMed Central  Google Scholar 

  33. Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2:315–37. https://doi.org/10.1146/annurev.bioeng.2.1.315

    Article  PubMed  Google Scholar 

  34. Ogawa T, Enciso R, Shintaku WH, Clark GT (2007) Evaluation of cross-section airway configuration of obstructive sleep apnea. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 103(1):102–8. https://doi.org/10.1016/j.tripleo.2006.06.008

    Article  PubMed  Google Scholar 

  35. El H, Palomo JM (2010) Measuring the airway in 3 dimensions: a reliability and accuracy study. Am J Orthod Dentofacial Orthop 137(4 Suppl):S50.e1-9. https://doi.org/10.1016/j.ajodo.2010.01.014. discussion S50-2

    Article  PubMed  Google Scholar 

  36. Stratemann S, Huang JC, Maki K, Hatcher D, Miller AJ (2011) Three-dimensional analysis of the airway with cone-beam computed tomography. Am J Orthod Dentofacial Orthop 140(5):607–15. https://doi.org/10.1016/j.ajodo.2010.12.019

    Article  PubMed  Google Scholar 

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Funding

This work was supported by Canadian Institutes of Health Research (grant 325899), the Canadian Foundation of Innovation (CFI 28236), Chinese Stomatological Association Clinical Research Fund (CSA-O2022-08), Shandong Provincial Natural Science Foundation (grant N.ZR2020QH161), China Oral Health Foundation (grant A2021-102), China Postdoctoral Science Foundation, grant number(2020M682173).

Author information

Authors and Affiliations

  1. Department of Orthodontics, School and Hospital of Stomatology, Shandong University, Shandong Key Laboratory of Oral Tissue Regeneration, Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong Provincial Clinical Research Center for Oral Diseases, Cheeloo College of Medicine, Shandong University, Jinan, 250100, Shandong, China

    Hui Chen & Tao Lv

  2. Hospital of Stomatology, Ningbo, Zhejiang, China

    Qing Luo

  3. Centre for Advanced Jet Engineering Technologies (CaJET), School of Mechanical Engineering, Key Laboratory of High-Efficiency and Clean Mechanical Manufacture at Shandong University, Ministry of Education, National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, China

    Lei Li

  4. Department of Orthodontics, Stomatological Hospital, Southern Medical University, Guangzhou, Guangdong, China

    Qing Wang

  5. Department of Otorhinolaryngology, NHC Key Laboratory of Otorhinolaryngology, Qilu Hospital of Shandong University, Jinan, China

    Yanzhong Li

  6. Key Laboratory of Special Functional Aggregated Materials, Ministry of Education, School of Chemistry and Chemical Engineering, Shandong University, Jinan, China

    Debo Zhou

  7. Faculty of Dentistry, McGill University, Montreal, Quebec, Canada

    Elham Emami

  8. Faculty of Dental Medicine, Université de Montréal, Montreal, Quebec, Canada

    Matthieu Schmittbuhl & Nelly Huynh

  9. Department of Oral Radilology, Academic Centre for Dentistry Amsterdam, Amsterdam, the Netherlands

    Paul van der Stelt

Authors
  1. Hui Chen
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Contributions

Hui Chen, study design, wrote the manuscript, prepared figures and tables;

Tao Lv, study design, revise the manuscript;

Qing Luo, analyzed the images, statistical analysis;

Lei Li, 3D printing; prepared the phantom;

Qing Wang, analyzed the data;

Yanzhong Li, 3D analysis of nasal cavity; prepared figures and tables;

Debo Zhou, analyzed the data, prepared the phantom;

Elham Emami, wrote and revised the manuscript;

Matthieu Schmittbuhl, wrote and revise the manuscript;

Paul van der Stelt, wrote the manuscript;

Nelly Huynh, study design, wrote and revise the manuscript.

Corresponding authors

Correspondence to Hui Chen or Tao Lv.

Ethics declarations

Ethics approval and consent to participate

This is a retrospective study based on the CBCT images of both Canadian and Chinese OSA patients.

The protocol of recruiting Canadian OSA patients was approved by the Research Ethics Board of Health of the Université de Montréal (13-076-CERES-D).

The protocol of recruiting patients from China was approved by the Medical Ethic Committee of the Dental School of Shandong University (NO.GR201814).

Competing interests

The authors declare no competing interests.

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Chen, H., Lv, T., Luo, Q. et al. Reliability and accuracy of a semi-automatic segmentation protocol of the nasal cavity using cone beam computed tomography in patients with sleep apnea. Clin Oral Invest 27, 6813–6821 (2023). https://doi.org/10.1007/s00784-023-05295-6

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