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An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans

Published online by Cambridge University Press:  23 December 2019

J Huang
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
J Chong
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
M Smith
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
M Duvnjak
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
C Chiu
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia
N Singh
Affiliation:
Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia Faculty of Medicine and Health Sciences, University of Sydney, Australia
E Wong*
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, University of Sydney, Sydney, Australia E-mail: eugene.hl.wong@gmail.com

Abstract

Objective

Deep learning using convolutional neural networks represents a form of artificial intelligence where computers recognise patterns and make predictions based upon provided datasets. This study aimed to determine if a convolutional neural network could be trained to differentiate the location of the anterior ethmoidal artery as either adhered to the skull base or within a bone ‘mesentery’ on sinus computed tomography scans.

Methods

Coronal sinus computed tomography scans were reviewed by two otolaryngology residents for anterior ethmoidal artery location and used as data for the Google Inception-V3 convolutional neural network base. The classification layer of Inception-V3 was retrained in Python (programming language software) using a transfer learning method to interpret the computed tomography images.

Results

A total of 675 images from 388 patients were used to train the convolutional neural network. A further 197 unique images were used to test the algorithm; this yielded a total accuracy of 82.7 per cent (95 per cent confidence interval = 77.7–87.8), kappa statistic of 0.62 and area under the curve of 0.86.

Conclusion

Convolutional neural networks demonstrate promise in identifying clinically important structures in functional endoscopic sinus surgery, such as anterior ethmoidal artery location on pre-operative sinus computed tomography.

Type
Main Articles
Copyright
Copyright © JLO (1984) Limited, 2019

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Footnotes

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

References

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