Automated estimation of anatomical risk metrics for endoscopic sinus surgery using deep learning

First published in
Proceedings of SPIE  
Number in series
13926
Citation
SPIE Medical Imaging 2026
Contribution to Conference
SPIE Medical Imaging 2026  
Publisher DOI
10.1117/12.3087376
Publisher
SPIE
Endoscopic sinus surgery requires careful preoperative assessment of the skull base anatomy to minimize risks such as cerebrospinal fluid leakage. Anatomical risk scores like the Keros, Gera and Thailand-Malaysia-Singapore score offer a standardized approach but require time-consuming manual measurements on coronal CT or CBCT scans. We propose an automated deep learning pipeline that estimates these risk scores by localizing key anatomical landmarks via heatmap regression. We compare a direct approach to a specialized global-to-local learning strategy and find mean absolute errors on the relevant anatomical measurements as low as 0.506 mm for the Keros, 4.516° for the Gera and 0.802 mm/0.777 mm for the TMS classification, each corresponding to the best-performing model for the respective measurement.
DDC Class
620: Engineering