Beyond whole-image learning: anatomically partitioned deep learning models for superior sinonasal disease classification.

objectivesWhole-image deep learning models for CT diagnosis of nasal cavity and paranasal sinus diseases often underperform because they disregard anatomical heterogeneity. We hypothesized that anatomically partitioned deep learning would enhance diagnostic accuracy. MATERIALS AND

methodsA multicenter retrospective study enrolled 2947 CT examinations conducted from October 2019 to May 2022. From these, 150 patients were randomly selected for manual segmentation, covering 13 anatomical regions, including the bilateral nasal cavities, maxillary sinuses, anterior ethmoid sinuses, posterior ethmoid sinuses, sphenoid sinuses, frontal sinuses, and nasal septum. An nnU-Net v2 model was first used to perform automatic anatomical partitioning. Then, disease-specific networks were trained on the 13 anatomically segmented sinus subregions-each expanded by ten pixels-and compared with a whole-image classifier. External test data were used to evaluate sensitivity, specificity, and the area under the curve (AUC).

resultsThe anatomically partition model based on nnU-Net v2 achieved an average Dice coefficient of 0.739 across the 13 anatomical regions. For the external test cohort using the model trained with anatomical partitioning, the average AUC value for the 13 anatomical partitions was 0.801, whereas for the model trained on the whole image, the average AUC value for the same 13 anatomical partitions was 0.587. DeLong's test identified statistically significant improvements in AUC for 42 of the 73 diagnostic labels, with an average absolute increase of 0.214.

conclusionsAnatomically partitioned deep learning markedly improves the CT-based diagnosis of sinonasal diseases, providing more reliable lesion characterization than conventional whole-image approaches and demonstrating strong potential for routine clinical implementation. KEY POINTS: Question When whole-image deep learning models underperform across dozens of sinonasal pathologies, can an anatomically partitioned training strategy boost diagnostic performance on sinonasal CT? Findings Anatomically partitioned training decisively surpasses whole-image learning, delivering an average AUC improvement of 0.21 and establishing a potential framework for comprehensive sinonasal CT diagnosis. Clinical relevance This partitioned learning approach demonstrates a viable path for the clinical deployment of AI in sinonasal imaging, offering radiologists and rhinologists a reliable tool for multi-disease diagnosis that complements their expertise.