ArticleAJNR. American journal of neuroradiology2022
Clinical Validation and Extension of an Automated, Deep Learning-Based Algorithm for Quantitative Sinus CT Analysis.
Article in AJNR. American journal of neuroradiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Artificial intelligence in otolaryngology: current applications, limitations, and future perspectives.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 · 2026Review
- Deep Learning Improves Sensitivity to Change in Sinus Computed Tomography: Evidence From Two Randomized Controlled Trials.International forum of allergy & rhinology · 2026Article
- Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- Development and Validation of a Simplified Three-Item Score for Predicting Eosinophilic Chronic Rhinosinusitis.Laryngoscope investigative otolaryngology · 2026Article
- Artificial intelligence-assisted type 2 inflammatory endotyping in CRSwNP: from Sinus CT and digital pathology to biologic decision support.Frontiers in allergy · 2026Review
- Deep learning detection and classification of fungal and non-fungal calcifications on paranasal sinus CT imaging.PloS one · 2026Article
- A Novel Method for Assessing Sinusitis Activity in Minimally Symptomatic Patients with ANCA-Associated Vasculitis: A Pilot Study.Journal of clinical medicine · 2025Article
- Comparison of segmentation performance of cnns, vision transformers, and hybrid networks for paranasal sinuses with sinusitis on CT images.Scientific reports · 2025Article
- Establishment of AI-assisted diagnosis of the infraorbital posterior ethmoid cells based on deep learning.BMC medical imaging · 2025Article
- The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.Biomedical engineering online · 2025Article
- NeuroNasal: Advanced AI-Driven Self-Supervised Learning Approach for Enhanced Sinonasal Pathology Detection.Sensors (Basel, Switzerland) · 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
- Deep Learning for Automated Prediction of Sphenoid Sinus Pneumatization in Computed TomographyCurrent medical imaging · 2025Article
- Multi-institutional validation of an AI-based sinus CT analytic platform with olfactory assessments.International forum of allergy & rhinology · 2024Article
- Preliminary study on AI-assisted diagnosis of bone remodeling in chronic maxillary sinusitis.BMC medical imaging · 2024Article
- Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction.JAMA otolaryngology-- head & neck surgery · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Deep learning radiomics-based preoperative prediction of recurrence in chronic rhinosinusitis.iScience · 2023Article
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5 authors.
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Abstract
background and purposeSinus CT is critically important for the diagnosis of chronic rhinosinusitis. While CT is sensitive for detecting mucosal disease, automated methods for objective quantification of sinus opacification are lacking. We describe new measurements and further clinical validation of automated CT analysis using a convolutional neural network in a chronic rhinosinusitis population. This technology produces volumetric segmentations that permit calculation of percentage sinus opacification, mean Hounsfield units of opacities, and percentage of osteitis. MATERIALS AND
methodsDemographic and clinical data were collected retrospectively from adult patients with chronic rhinosinusitis, including serum eosinophil count, Lund-Kennedy endoscopic scores, and the SinoNasal Outcomes Test-22. CT scans were scored using the Lund-Mackay score and the Global Osteitis Scoring Scale. CT images were automatically segmented and analyzed for percentage opacification, mean Hounsfield unit of opacities, and percentage osteitis. These readouts were correlated with visual scoring systems and with disease parameters using the Spearman
resultsEighty-eight subjects were included. The algorithm successfully segmented 100% of scans and calculated features in a diverse population with CT images obtained on different scanners. A strong correlation existed between percentage opacification and the Lund-Mackay score (
conclusionsOur quantitative processing of sinus CT images provides objective measures that correspond well to established visual scoring methods. While automation is a clear benefit here, validation may be needed in a prospective, multi-institutional setting.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.