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An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed
J Huang1, A-R Habib1, D Mendis1
1Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia.
The Journal of Laryngology and Otology
|December 24, 2019
Summary
A convolutional neural network accurately identified the anterior ethmoidal artery location on CT scans, aiding in functional endoscopic sinus surgery planning. This artificial intelligence tool shows promise for identifying critical anatomical structures.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Otolaryngology
Background:
- Deep learning, a form of artificial intelligence, utilizes convolutional neural networks (CNNs) to identify patterns and make predictions from data.
- The anterior ethmoidal artery's location is crucial for safe functional endoscopic sinus surgery (FESS).
Purpose of the Study:
- To train a CNN to distinguish between the anterior ethmoidal artery adhered to the skull base versus within a bone mesentery.
- To evaluate the CNN's performance in classifying anterior ethmoidal artery location on sinus computed tomography (CT) scans.
Main Methods:
- Coronal sinus CT scans were reviewed by otolaryngology residents to determine anterior ethmoidal artery location.
- The Google Inception-V3 CNN architecture was utilized, with its classification layer retrained using Python and transfer learning.
Main Results:
- The CNN was trained on 675 images from 388 patients and tested on 197 unique images.
- The algorithm achieved an overall accuracy of 82.7%, with a kappa statistic of 0.62 and an area under the curve of 0.86.
Conclusions:
- CNNs show potential for identifying critical structures like the anterior ethmoidal artery on pre-operative sinus CT scans.
- This technology may enhance the safety and precision of functional endoscopic sinus surgery.
