Automated human tracheal morphometrics using deep learning: Toward custom tracheostomy tubes

Purpose

Tracheostomy is a critical, life-saving procedure, but complications often arise due to suboptimal tracheostomy tube fit. Customised tubes could mitigate these risks, yet their development hinges on precise tracheal morphometric data. This study aims to establish an automated workflow for extracting high-resolution tracheal measurements from CT scans to support the design of demographic-specific tracheostomy tubes.

Methods

This retrospective study utilised 100 anonymised CT scans, with 90 included after exclusions. An automated workflow was developed using 3D Slicer and a deep learning segmentation model (VISTA-3D) to extract tracheal measurements. Key parameters analysed included tracheal length, anteroposterior and transverse dimensions, and maximum inscribed circle diameter.

Results

Automated measurements revealed the following average tracheal dimensions:

• Length: 66.1 ± 12.2 mm

• Anteroposterior dimension: 16.2 ± 2.5 mm

• Transverse dimension: 17.9 ± 2.4 mm

• Maximum inscribed circle diameter: 14.1 ± 1.9 mm

Sex-specific differences were observed, with males exhibiting larger tracheal dimensions across all metrics.

Conclusion

The results align with existing literature, validating the automated approach for efficient, objective, and high-resolution morphometric analysis. This method supports the development of customised tracheostomy tubes tailored to demographic-specific needs, ultimately improving patient wellbeing.