Automated Tracheal Morphometrics Using Deep Learning: Toward Custom Tracheostomy Tubes

Tracheostomy is a life-saving procedure with complications often linked to suboptimal tracheostomy tube fit. Customised tubes could reduce these risks, but their development requires precise tracheal morphometric data. This study presents an automated workflow for extracting high-resolution tracheal measurements from CT scans, using 3D Slicer and a deep learning segmentation model (VISTA-3D). One hundred anonymised scans were analysed, with 90 included after exclusions. Automated measurements revealed average tracheal lengths of 66.1± 12.2 mm, anteroposterior dimensions of 16.2 ± 2.5 mm, transverse dimensions of 17.9 ± 2.4 mm, and maximum inscribed circle diameters of 14.1 ± 1.9 mm. Sex-specific differences were observed, with males exhibiting larger dimensions. The results align with existing literature, validating the automated approach. This method enables efficient, objective, and high-resolution morphometric analysis, supporting the development of demographic-specific tracheostomy tubes and improving pre-operative planning.