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

BACKGROUND 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.

The Olfactory Cleft Endoscopy Scale: a multi-institutional validation study in chronic rhinosinusitis

BACKGROUND

Olfactory dysfunction (OD) associated with chronic rhinosinusitis (CRS) remains quite challenging. Instruments to precisely assess olfactory cleft anatomy and their association with olfaction are needed.

METHODS

The olfactory cleft endoscopy scale (OCES) was used to assess the olfactory cleft in healthy control subjects and a cohort of patients with CRS. Psychophysical and psychosocial olfactory function were assessed and correlations with OCES scores were measured.

RESULTS

Control subjects and subjects with CRS with nasal polyps (CRSwNP) and CRS without nasal polyps (CRSsNP) were enrol- led. OCES correlated with both psychophysical and psychosocial olfaction, as measured by threshold, discrimination and identi- fication (TDI) scores and Questionnaire on Olfactory Disorders (QOD-NS) scores for all case and control subjects combined. OCES improved in both CRS groups postoperatively with the highest correlation seen in postoperative olfaction in CRSwNP patients. CRS patients who achieve near perfect OCES and sinus endoscopy scores after surgery have olfactory metrics that are indistin- guishable from controls regardless of polyp status.

CONCLUSIONS

The OCES is a valid olfactory-specific measure that demonstrates strong validity and provides complimentary infor- mation to traditional sinus endoscopy to aid in our understanding of OD associated with CRS.