Background: Classifying sphenoid pneumatisation is an important but often overlooked task in reporting sinus CT scans. Artificial intelligence (AI) and one of its key methods, convolutional neural networks (CNNs), can create algorithms that can learn from data without being programmed with explicit rules and have shown utility in radiological image classification.
Objective: To determine if a trained CNN can accurately classify sphenoid sinus pneumatisation on CT sinus imaging.
Methods: Sagittal slices through the natural ostium of the sphenoid sinus were extracted from retrospectively collected bone-window CT scans of the paranasal sinuses for consecutive patients over 6 years. Two blinded Otolaryngology residents reviewed each image and classified the sphenoid sinus pneumatisation as either conchal, presellar or sellar. An AI algorithm was developed using the Microsoft Azure Custom Vision deep learning platform to classify the pattern of pneumatisation.
Results: Seven hundred eighty images from 400 patients were used to train the algorithm, which was then tested on a further 118 images from 62 patients. The algorithm achieved an accuracy of 93.2% (95% confidence interval [CI] 87.1-97.0), 87.3% (95% CI 79.9-92.7) and 85.6% (95% CI 78.0-91.4) in correctly identifying conchal, presellar and sellar sphenoid pneumatisation, respectively. The overall weighted accuracy of the CNN was 85.9%.
Conclusion: The CNN described demonstrated a moderately accurate classification of sphenoid pneumatisation subtypes on CT scans. The use of CNN-based assistive tools may enable surgeons to achieve safer operative planning through routine automated reporting allowing greater resources to be directed towards the identification of pathology.
Keywords: artificial intelligence; deep learning; endoscopic sinus surgery; paranasal sinuses; skull base; sphenoid sinus.
© 2023 John Wiley & Sons Ltd.