An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans.
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 locati...
| Published in: | Journal of Laryngology & Otology Vol. 134; no. 1; pp. 52 - 56 |
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| Main Authors: | , , , , , , , , |
| Format: | diagnostic images research tables/charts Journal Article |
| Published: |
Cambridge University Press
Jan2020
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=141682055&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141682055 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00222151 1GA jtl: Journal of Laryngology & Otology issn: 00222151 maglogo: N pubinfo: dt: Jan2020 vid: 134 iid: 1 pid: 15979 pub: Cambridge University Press artinfo: ui: 141682055 141682055 141682055 10.1017/S0022215119002536 141682055 ppf: 52 ppct: 4 formats: tig: atl: An artificial intelligence algorithm that differentiates anterior ethmoidal artery location on sinus computed tomography scans. aug: au: Huang, J Habib, A-R Mendis, D Chong, J Smith, M Duvnjak, M Chiu, C Singh, N Wong, E affil: Department of Otolaryngology, Head and Neck Surgery, Westmead Hospital, Australia sug: subj: Neural Networks (Computer) Algorithms Preoperative Period Tomography, X-Ray Computed Methods Ethmoid Sinus Blood Supply Ophthalmic Artery Radiography Skull Base Radiography Mesentery Radiography Surgery, Otorhinolaryngologic Methods Endoscopy Methods Human Artificial Intelligence Specialties, Medical Interns and Residents Programming Languages Software Confidence Intervals kappa Statistic Diagnosis, Differential Paranasal Sinuses Surgery Ophthalmic Artery Injuries Wounds and Injuries Risk Factors Wounds and Injuries Prevention and Control Treatment Outcomes ab: 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. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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