Automated three-dimensional computed tomography analysis for surgical decisions in congenital nasal pyriform aperture stenosis

Background Congenital nasal pyriform aperture stenosis is a rare neonatal condition that causes respiratory distress and potentially requires surgery. Current diagnosis relies on clinical assessment and manual computed tomography (CT) measurements of the pyriform aperture width, which may not fully capture obstruction severity. Objective To evaluate the severity of pyriform aperture stenosis using automatic three-dimensional (3-D) analysis and identify parameters discriminating between conservative and surgical cases. Materials and methods This retrospective study analyzed CT scans of neonatal airways using a novel automated 3-D segmentation algorithm. We collected 22 CT scans (2010–2022) of newborns aged 0–35 days: 12 controls, four moderate cases treated conservatively, and six severe cases requiring surgery. The algorithm measured pyriform aperture width, nasal volumes, surface area, and cross-sectional areas. Results The algorithm achieved high accuracy (Dice coefficient, 0.961 ± 0.005) and aligned well with manual measurements of the pyriform aperture (average difference, -0.05 ± 0.77 mm, -0.7 ± 9.1%). All cases with stenosis showed anterior narrowing, while only severe cases exhibited stenosis along the entire mid-nasal cavity. Mid-nasal cavity volume, surface area, and cross-sectional areas at 50% and 75% of the mid-nasal cavity emerged as potential surgical predictors, with cross-sectional area at 75% being the most discriminating (moderate, 68.6 ± 7.5 mm 2 ; severe, 33.5 ± 13.7 mm 2 ; P <0.01). Conclusion Automated 3-D CT analysis quantifies pyriform aperture stenosis severity by measuring nasal airway dimensions. The study suggests objective parameters that may assist in surgical decisions and highlights the importance of considering the entire 3-D nasal cavity when planning surgical interventions. A multi-center study with a larger cohort is recommended to validate these findings.