12 March 2015 Near-infrared imaging for management of chronic maxillary sinusitis
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Abstract
Efficient management of chronic sinusitis remains a great challenge for primary care physicians. Unlike ENT specialists using Computed Tomography scans, they lack an affordable and safe method to accurately screen and monitor sinus diseases in primary care settings. Lack of evidence-based sinusitis management leads to frequent under-treatments and unnecessary over-treatments (i.e. antibiotics). Previously, we reported low-cost optical imaging designs for oral illumination and facial optical imaging setup. It exploits the sensitivity of NIR transmission intensity and their unique patterns to the sinus structures and presence of fluid/mucous-buildup within the sinus cavities. Using the improved NIR system, we have obtained NIR sinus images of 45 subjects with varying degrees of sinusitis symptoms. We made diagnoses of these patients based on two types of evidence: symptoms alone or NIR images along. These diagnostic results were then compared to the gold standard diagnosis using computed tomography through sensitivity and specificity analysis. Our results indicate that diagnosis of mere presence of sinusitis that is, distinguishing between healthy individuals vs. diseased individuals did not improve much when using NIR imaging compared to the diagnosis based on symptoms alone (69% in sensitivity, 75% specificity). However, use of NIR imaging improved the differential diagnosis between mild and severe diseases significantly as the sensitivity improved from 75% for using diagnosis based on symptoms alone up to 95% for using diagnosis based on NIR images. Reported results demonstrate great promise for using NIR imaging system for management of chronic sinusitis patients in primary care settings without resorting to CT.
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Joon S. You, Albert E. Cerussi, James H. Kim, Sean Ison, Brian Wong, Haotian Cui, Naveen Bhandarkar, "Near-infrared imaging for management of chronic maxillary sinusitis", Proc. SPIE 9314, Optics and Biophotonics in Low-Resource Settings, 93140D (12 March 2015); doi: 10.1117/12.2080689; https://doi.org/10.1117/12.2080689
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