Hostname: page-component-7dd5485656-jtdwj Total loading time: 0 Render date: 2025-10-31T04:23:37.329Z Has data issue: false hasContentIssue false
  • English
  • Français

An artificial intelligence algorithm that identifies middle turbinate pneumatisation (concha bullosa) on sinus computed tomography scans

Published online by Cambridge University Press:  01 April 2020

P Parmar ,
A-R Habib ,
D Mendis ,
A Daniel ,
M Duvnjak ,
J Ho ,
M Smith ,
D Roshan ,
E Wong  and
N Singh
P Parmar
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
A-R Habib
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
D Mendis
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
A Daniel
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
M Duvnjak
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
J Ho
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
M Smith
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
D Roshan
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
E Wong*
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia Faculty of Medicine and Health Sciences, University of Sydney, Australia
N Singh
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia Faculty of Medicine and Health Sciences, University of Sydney, Australia
*
Author for correspondence: Dr Eugene H Wong, Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Westmead NSW 2145, Australia E-mail: eugene.hl.wong@gmail.com
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective

Convolutional neural networks are a subclass of deep learning or artificial intelligence that are predominantly used for image analysis and classification. This proof-of-concept study attempts to train a convolutional neural network algorithm that can reliably determine if the middle turbinate is pneumatised (concha bullosa) on coronal sinus computed tomography images.

Method

Consecutive high-resolution computed tomography scans of the paranasal sinuses were retrospectively collected between January 2016 and December 2018 at a tertiary rhinology hospital in Australia. The classification layer of Inception-V3 was retrained in Python using a transfer learning method to interpret the computed tomography images. Segmentation analysis was also performed in an attempt to increase diagnostic accuracy.

Results

The trained convolutional neural network was found to have diagnostic accuracy of 81 per cent (95 per cent confidence interval: 73.0–89.0 per cent) with an area under the curve of 0.93.

Conclusion

A trained convolutional neural network algorithm appears to successfully identify pneumatisation of the middle turbinate with high accuracy. Further studies can be pursued to test its ability in other clinically important anatomical variants in otolaryngology and rhinology.

Keywords

Deep LearningArtificial IntelligenceSinusitisTurbinatesSurgery

Information

Type
Main Articles
Information
The Journal of Laryngology & Otology , Volume 134 , Issue 4 , April 2020 , pp. 328 - 331
Copyright
Copyright © JLO (1984) Limited, 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

Footnotes

Dr E H Wong takes responsibility for the integrity of the content of the paper

References

Ozcan, KM, Selcuk, A, Ozkan, I, Akdogan, O, Dere, H. Anatomical variations of nasal turbinates. J Craniofac Surg 2008;19:1678–82CrossRefGoogle ScholarPubMed
Bolger, WE, Butzin, CA, Parsons, DS. Paranasal sinus bony anatomic variations and mucosal abnormalities: CT analysis for endoscopic sinus surgery. Laryngoscope 1991;101:56–64CrossRefGoogle ScholarPubMed
Stallman, JS, Lobo, JN, Som, PM. The incidence of concha bullosa and its relationship to nasal septal deviation and paranasal sinus disease. AJNR Am J Neuroradiol 2004;25:1613–18Google ScholarPubMed
El-Taher, M, AbdelHameed, WA, Alam-Eldeen, MH, Haridy, A. Coincidence of concha bullosa with nasal septal deviation; radiological study. Indian J Otolaryngol Head Neck Surg 2019;71:1918–22CrossRefGoogle ScholarPubMed
Smith, KD, Edwards, PC, Saini, TS, Norton, NS. The prevalence of concha bullosa and nasal septal deviation and their relationship to maxillary sinusitis by volumetric tomography. Int J Dent. 2010;2010:404982CrossRefGoogle ScholarPubMed
Kucybała, I, Janik, KA, Ciuk, S, Storman, D, Urbanik, A. Nasal septal deviation and concha bullosa - do they have an impact on maxillary sinus volumes and prevalence of maxillary sinusitis? Pol J Radiol 2017;82:126–33CrossRefGoogle ScholarPubMed
Clark, ST, Babin, RW, Salazar, J. The incidence of concha bullosa and its relationship to chronic sinonasal disease. Am J Rhinol Allergy 1989;3:11–12CrossRefGoogle Scholar
Ozkiris, M, Karacavus, S, Kapusuz, Z, Saydam, L. The impact of unilateral concha bullosa on mucociliary activity: an assessment by rhinoscintigraphy. Am J Rhinol Allergy 2013;27:54–7CrossRefGoogle ScholarPubMed
Havas, TE, Lowinger, DS. Comparison of functional endonasal sinus surgery with and without partial middle turbinate resection. Ann Otol Rhinol Laryngol 2000;109:634–40CrossRefGoogle ScholarPubMed
Eren, SB, Kocak, I, Dogan, R, Ozturan, O, Yildirim, YS, Tugrul, S. A comparison of the long-term results of crushing and crushing with intrinsic stripping techniques in concha bullosa surgery. Int Forum Allergy Rhinol 2014;4:753–8CrossRefGoogle ScholarPubMed
Deutschmann, MW, Yeung, J, Bosch, M, Lysack, JT, Kingstone, M, Kilty, SJ et al. Radiologic reporting for paranasal sinus computed tomography: a multi-institutional review of content and consistency. Laryngoscope 2013;123:1100–5CrossRefGoogle ScholarPubMed
Esteva, A, Kuprel, B, Novoa, RA, Ko, J, Swetter, SM, Blau, HM et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115–18CrossRefGoogle ScholarPubMed
Hoogendoorn, M, Szolovits, P, Moons, LM, Numans, ME. Utilizing uncoded consultation notes from electronic medical records for predictive modeling of colorectal cancer. Artif Intell Med 2016;69:53–61CrossRefGoogle ScholarPubMed
Chowdhury, NI, Smith, TL, Chandra, RK, Turner, JH. Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks. Int Forum Allergy Rhinol 2019;9:46–52CrossRefGoogle ScholarPubMed
Bur, AM, Shew, M, New, J. Artificial intelligence for the otolaryngologist: a state of the art review. Otolaryngol Head Neck Surg 2019;160:603–11CrossRefGoogle ScholarPubMed
Lloyd, GA. CT of the paranasal sinuses: study of a control series in relation to endoscopic sinus surgery. J Laryngol Otol 1990;104:477–81CrossRefGoogle ScholarPubMed
Calhoun, KH, Waggenspack, GA, Simpson, CB, Hokanson, JA, Bailey, BJ. CT evaluation of the paranasal sinuses in symptomatic and asymptomatic populations. Otolaryngol Head Neck Surg 1991;104:480–3CrossRefGoogle ScholarPubMed
Zinreich, SJ, Mattox, DE, Kennedy, DW, Chrisholm, HL, Diffley, DM, Rosenbaum, AE. Concha bullosa: CT evaluation. J Comput Assist Tomogr 1988;12:778–84CrossRefGoogle ScholarPubMed
Yousem, DM, Kennedy, DW, Rosenberg, S. Ostiomeatal complex risk factors for sinusitis: CT evaluation. J Otolaryngol 1991;20:419–24Google ScholarPubMed
Hatipoglu, HG, Cetin, MA, Yüksel, E. Concha bullosa types: their relationship with sinusitis, ostiomeatal and frontal recess disease. Diagn Interv Radiol 2005;11:145–9Google ScholarPubMed
Badran, HS. Role of surgery in isolated concha bullosa. Clin Med Insights Ear Nose Throat 2011;4:13–19CrossRefGoogle ScholarPubMed
Mehta, KS, Yousuf, A, Wazir, IA, Sideeq, K. Clinical benefits of surgical management of concha bullosa. Int J Otorhinolaryngol Head Neck Surg 2017;3:833–6CrossRefGoogle Scholar
Mehta, R, Kaluskar, S. Endoscopic turbinoplasty of concha bullosa: long term results. Indian J Otolaryngol Head Neck Surg 2013;65(suppl 2):251–54CrossRefGoogle ScholarPubMed