OBJECTIVES/HYPOTHESIS: To evaluate the accuracy of three-dimensional (3D) airway reconstructions obtained using quantitative endoscopy (QE). We developed this novel technique to reconstruct precise 3D representations of airway geometries from endoscopic video streams. This method, based on machine vision methodologies, uses a post-processing step of the standard videos obtained during routine laryngoscopy and bronchoscopy. We hypothesize that this method is precise and will generate assessment of airway size and shape similar to those obtained using computed tomography (CT). STUDY DESIGN: This study was approved by the institutional review board (IRB). We analyzed video sequences from pediatric patients receiving rigid bronchoscopy. METHODS: We generated 3D scaled airway models of the subglottis, trachea, and carina using QE. These models were compared to 3D airway models generated from CT. We used the CT data as the gold standard measure of airway size, and used a mixed linear model to estimate the average error in cross-sectional area and effective diameter for QE. RESULTS: The average error in cross sectional area (area sliced perpendicular to the long axis of the airway) was 7.7 mm(2) (variance 33.447 mm(4)). The average error in effective diameter was 0.38775 mm (variance 2.45 mm(2)), approximately 9% error. CONCLUSION: Our pilot study suggests that QE can be used to generate precise 3D reconstructions of airways. This technique is atraumatic, does not require ionizing radiation, and integrates easily into standard airway assessment protocols. We conjecture that this technology will be useful for staging airway disease and assessing surgical outcomes.
OBJECTIVES/HYPOTHESIS: To evaluate the accuracy of three-dimensional (3D) airway reconstructions obtained using quantitative endoscopy (QE). We developed this novel technique to reconstruct precise 3D representations of airway geometries from endoscopic video streams. This method, based on machine vision methodologies, uses a post-processing step of the standard videos obtained during routine laryngoscopy and bronchoscopy. We hypothesize that this method is precise and will generate assessment of airway size and shape similar to those obtained using computed tomography (CT). STUDY DESIGN: This study was approved by the institutional review board (IRB). We analyzed video sequences from pediatric patients receiving rigid bronchoscopy. METHODS: We generated 3D scaled airway models of the subglottis, trachea, and carina using QE. These models were compared to 3D airway models generated from CT. We used the CT data as the gold standard measure of airway size, and used a mixed linear model to estimate the average error in cross-sectional area and effective diameter for QE. RESULTS: The average error in cross sectional area (area sliced perpendicular to the long axis of the airway) was 7.7 mm(2) (variance 33.447 mm(4)). The average error in effective diameter was 0.38775 mm (variance 2.45 mm(2)), approximately 9% error. CONCLUSION: Our pilot study suggests that QE can be used to generate precise 3D reconstructions of airways. This technique is atraumatic, does not require ionizing radiation, and integrates easily into standard airway assessment protocols. We conjecture that this technology will be useful for staging airway disease and assessing surgical outcomes.
Authors: Martin Wagner; Benjamin Friedrich Berthold Mayer; Sebastian Bodenstedt; Katherine Stemmer; Arash Fereydooni; Stefanie Speidel; Rüdiger Dillmann; Felix Nickel; Lars Fischer; Hannes Götz Kenngott Journal: Surg Endosc Date: 2018-03-05 Impact factor: 4.584
Authors: Christian R Francom; Cameron A Best; Ryan G Eaton; Victoria Pepper; Amanda J Onwuka; Christopher K Breuer; Meredith N Merz Lind; Jonathan M Grischkan; Tendy Chiang Journal: Int J Pediatr Otorhinolaryngol Date: 2018-10-11 Impact factor: 1.675
Authors: Victoria K Pepper; Christian Francom; Cameron A Best; Ekene Onwuka; Nakesha King; Eric Heuer; Nathan Mahler; Jonathan Grischkan; Christopher K Breuer; Tendy Chiang Journal: Int J Pediatr Otorhinolaryngol Date: 2016-10-06 Impact factor: 1.675