ArticleInternational forum of allergy & rhinology2019
Automated classification of osteomeatal complex inflammation on computed tomography using convolutional neural networks.
Article in International forum of allergy & rhinology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Deep learning classification of nasal anatomical variants on computed tomography for endoscopic sinus surgery.Scientific reports · 2026Article
- Establishment of AI-assisted diagnosis of the infraorbital posterior ethmoid cells based on deep learning.BMC medical imaging · 2025Article
- Deep learning using nasal endoscopy and T2-weighted MRI for prediction of sinonasal inverted papilloma-associated squamous cell carcinoma: an exploratory study.European radiology experimental · 2025Article
- Diagnostic accuracy of artificial intelligence in the detection of maxillary sinus pathology using computed tomography: A concise systematic review.Imaging science in dentistry · 2025Article
- Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis.Frontiers in physiology · 2025Review
- Neoplasms in the Nasal Cavity Identified and Tracked with an Artificial Intelligence-Assisted Nasal Endoscopic Diagnostic System.Bioengineering (Basel, Switzerland) · 2024Article
- The accuracy of deep learning models for diagnosing maxillary fungal ball rhinosinusitis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
- Review
- [Research on the classification model of chronic sinusitis based on VGG].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024Article
- Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis.BMC medical imaging · 2024Article
- Applications and challenges of neural networks in otolaryngology (Review).Biomedical reports · 2024Review
- Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.PloS one · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results.Diagnostics (Basel, Switzerland) · 2023Article
- The Evolution and Application of Artificial Intelligence in Rhinology: A State of the Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2023Review
- A Scoping Review of Artificial Intelligence Research in Rhinology.American journal of rhinology & allergy · 2023Article
- A Computer Vision Algorithm to Classify Pneumatization of the Mastoid Process on Temporal Bone Computed Tomography Scans.The journal of international advanced otology · 2023Article
- Deep learning radiomics-based preoperative prediction of recurrence in chronic rhinosinusitis.iScience · 2023Article
- Clinical Validation and Extension of an Automated, Deep Learning-Based Algorithm for Quantitative Sinus CT Analysis.AJNR. American journal of neuroradiology · 2022Article
- Deep Learning Artificial Intelligence to Predict the Need for Tracheostomy in Patients of Deep Neck Infection Based on Clinical and Computed Tomography Findings-Preliminary Data and a Pilot Study.Diagnostics (Basel, Switzerland) · 2022Article
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4 authors.
Funding
Abstract
backgroundConvolutional neural networks (CNNs) are advanced artificial intelligence algorithms well suited to image classification tasks with variable features. These have been used to great effect in various real-world applications including handwriting recognition, face detection, image search, and fraud prevention. We sought to retrain a robust CNN with coronal computed tomography (CT) images to classify osteomeatal complex (OMC) occlusion and assess the performance of this technology with rhinologic data.
methodsThe Google Inception-V3 CNN trained with 1.28 million images was used as the base model. Preoperative coronal sections through the OMC were obtained from 239 patients enrolled in 2 prospective chronic rhinosinusitis (CRS) outcomes studies, labeled according to OMC status, and mirrored to obtain a set of 956 images. Using this data, the classification layer of Inception-V3 was retrained in Python using a transfer learning method to adapt the CNN to the task of interpreting sinonasal CT images.
resultsThe retrained neural network achieved 85% classification accuracy for OMC occlusion, with a 95% confidence interval for algorithm accuracy of 78% to 92%. Receiver operating characteristic (ROC) curve analysis on the test set confirmed good classification ability of the CNN with an area under the ROC curve (AUC) of 0.87, significantly different than both random guessing and a dominant classifier that predicts the most common class (p < 0.0001).
conclusionCurrent state-of-the-art CNNs may be able to learn clinically relevant information from 2-dimensional sinonasal CT images with minimal supervision. Future work will extend this approach to 3-dimensional images in order to further refine this technology for possible clinical applications.
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