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A Comparison of Near-Infrared Imaging and Computerized Tomography Scan for Detecting Maxillary Sinusitis

Authors :
Adwight Risbud
Joon You
Khodayar Goshtasbi
Mehdi Abouzari
Kotaro Tsutsumi
Naveen D. Bhandarkar
Brooke Sarna
Source :
Annals of Otology, Rhinology & Laryngology. 131:1144-1150
Publication Year :
2021
Publisher :
SAGE Publications, 2021.

Abstract

Objective: To investigate the use of near-infrared (NIR) imaging as a tool for outpatient clinicians to quickly and accurately assess for maxillary sinusitis and to characterize its accuracy compared to computerized tomography (CT) scan. Methods: In a prospective investigational study, NIR and CT images from 65 patients who presented to a tertiary care rhinology clinic were compared to determine the sensitivity and specificity of NIR as an imaging modality. Results: The sensitivity and specificity of NIR imaging in distinguishing normal versus maxillary sinus disease was found to be 90% and 84%, normal versus mild maxillary sinus disease to be 76% and 91%, and mild versus severe maxillary sinus disease to be 96% and 81%, respectively. The average pixel intensity was also calculated and compared to the modified Lund-Mackay scores from CT scans to assess the ability of NIR imaging to stratify the severity of maxillary sinus disease. Average pixel intensity over a region of interest was significantly different ( P < .001) between normal, mild, and severe disease, as well as when comparing normal versus mild ( P < .001, 95% CI 42.22-105.39), normal versus severe ( P < .001, 95% CI 119.43-174.14), and mild versus severe ( P < .001, 95% CI 41.39-104.56) maxillary sinus disease. Conclusion: Based on this data, NIR shows promise as a tool for identifying patients with potential maxillary sinus disease as well as providing information on severity of disease that may guide administration of appropriate treatments.

Details

ISSN :
1943572X and 00034894
Volume :
131
Database :
OpenAIRE
Journal :
Annals of Otology, Rhinology & Laryngology
Accession number :
edsair.doi.dedup.....c5293be1d5ba23c49bd3a3385733a33c