Accuracy of computer‐assisted navigation: significant augmentation by facial recognition software. Issue 9 (14th July 2017)

• Record Type: Journal Article
• Authors: Glicksman, Jordan T.; Reger, Christine; Parasher, Arjun K.; Kennedy, David W.
• Is Part Of: International forum of allergy & rhinology. Volume 7:Issue 9(2017:Sep.)
• Page Start: 884
• Page End: 888
• Publication Date: 2017-07-14
• Subjects: endoscopic sinus surgery -- FESS -- image guidance -- navigation -- photo‐registration; 617.51005
• DOI: 10.1002/alr.21982
• Languages: English
• ISSNs: 2042-6976

Abstract

Abstract : Background: Over the past 20 years, image guidance navigation has been used with increasing frequency as an adjunct during sinus and skull base surgery. These devices commonly utilize surface registration, where varying pressure of the registration probe and loss of contact with the face during the skin tracing process can lead to registration inaccuracies, and the number of registration points incorporated is necessarily limited. The aim of this study was to evaluate the use of novel facial recognition software for image guidance registration. Methods: Consecutive adults undergoing endoscopic sinus surgery (ESS) were prospectively studied. Patients underwent image guidance registration via both conventional surface registration and facial recognition software. The accuracy of both registration processes were measured at the head of the middle turbinate (MTH), middle turbinate axilla (MTA), anterior wall of sphenoid sinus (SS), and nasal tip (NT). Results: Forty‐five patients were included in this investigation. Facial recognition was accurate to within a mean of 0.47 mm at the MTH, 0.33 mm at the MTA, 0.39 mm at the SS, and 0.36 mm at the NT. Facial recognition was more accurate than surface registration at the MTH by an average of 0.43 mm ( p = 0.002), at the MTA by an average of 0.44 mm ( p < 0.001), and at the SS by an average of 0.40 mm ( p < 0.001). The integration of facial recognition software did not adversely affect registration time. Conclusion: In this prospective study, automated facial recognition software significantly improved the accuracy of image guidance registration when compared to conventional surface registration.