Reuter, KonradKonradReuterThaysen, LennartLennartThaysenDoruk, BilkayBilkayDorukLatus, SarahSarahLatusHolst, BrigitteBrigitteHolstBecker, Benjamin TobiasBenjamin TobiasBeckerEggert, DennisDennisEggertBetz, Christian StephanChristian StephanBetzHoffmann, Anna SophieAnna SophieHoffmannSchlaefer, AlexanderAlexanderSchlaeferThomas M. DesernoAxel Wismüller2026-05-222026-05-222026-04-02SPIE Medical Imaging 2026https://hdl.handle.net/11420/63196Endoscopic 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.enTechnology::620: EngineeringConference Paper10.1117/12.3087376