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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.
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.
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.
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.
The nasal septal swell body is a normal anatomical structure located in the superior nasal septum anterior to the middle turbinate. However, the impact of the septal swell body in nasal breathing during normal function and disease remains unclear. This study aimed to establish that the septal swell body varies in size over time and correlates this with the natural variation of the inferior turbinates.
Consecutive patients who underwent at least two computed tomography scans were identified. The width and height of the septal swell body and the inferior turbinates was recorded. A correlation between the difference in septal swell body and turbinates between the two scans was performed using a Pearson's coefficient.
A total of 34 patients (53 per cent female with a mean age of 58.3 ± 20.2 years) were included. The mean and mean difference in septal swell body width between scans for the same patient was 1.57 ± 1.00 mm. The mean difference in turbinate width between scans was 2.23 ± 2.52 mm. A statistically significant correlation was identified between the difference in septal swell body and total turbinate width (r = 0.35, p = 0.04).
The septal swell body is a dynamic structure that varies in width over time in close correlation to the inferior turbinates. Further research is required to quantify its relevance as a surgical area of interest.
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.
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.
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.
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.
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