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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
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, WestmeadNSW2145, Australia E-mail: eugene.hl.wong@gmail.com

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.

Type
Main Articles
Copyright
Copyright © JLO (1984) Limited, 2020

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Footnotes

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

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