Clinical Validation and Extension of an Automated, Deep Learning–Based Algorithm for Quantitative Sinus CT Analysis

dc.contributor.authorMassey, C. J.
dc.contributor.authorRamos, L.
dc.contributor.authorBeswick, D. M.
dc.contributor.authorRamakrishnan, V. R.
dc.contributor.authorHumphries, S. M.
dc.contributor.departmentOtolaryngology -- Head and Neck Surgery, School of Medicine
dc.date.accessioned2024-09-23T12:08:56Z
dc.date.available2024-09-23T12:08:56Z
dc.date.issued2022
dc.description.abstractBackground and purpose: Sinus 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 methods: Demographic 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 ρ. Results: Eighty-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 (ρ = 0.85, P < .001). Both percentage opacification and the Lund-Mackay score exhibited moderate correlations with the Lund-Kennedy score (ρ = 0.58, P < .001, and ρ = 0.58, P < .001, respectively). The percentage osteitis correlated moderately with the Global Osteitis Scoring Scale (ρ = 0.48, P < .001). Conclusions: Our 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.
dc.eprint.versionFinal published version
dc.identifier.citationMassey CJ, Ramos L, Beswick DM, Ramakrishnan VR, Humphries SM. Clinical Validation and Extension of an Automated, Deep Learning-Based Algorithm for Quantitative Sinus CT Analysis. AJNR Am J Neuroradiol. 2022;43(9):1318-1324. doi:10.3174/ajnr.A7616
dc.identifier.urihttps://hdl.handle.net/1805/43505
dc.language.isoen_US
dc.publisherAmerican Society of Neuroradiology
dc.relation.isversionof10.3174/ajnr.A7616
dc.relation.journalAmerican Journal of Neuroradiology
dc.rightsPublisher Policy
dc.sourcePMC
dc.subjectChronic disease
dc.subjectRhinitis
dc.subjectSinusitis
dc.subjectOsteitis
dc.titleClinical Validation and Extension of an Automated, Deep Learning–Based Algorithm for Quantitative Sinus CT Analysis
dc.typeArticle
ul.alternative.fulltexthttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9451634/
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