aceCT-based deep learning for identifying sinonasal inverted papilloma: a single-center retrospective study
This study aimed to develop a CT-based deep learning model as a decision-support system for the classification of sinonasal inverted papilloma (IP), nasal polyps, and normal paranasal anatomy. A retrospective dataset of 871 patients who underwent paranasal CT imaging between 2020 and 2024 was analyzed. All diagnoses were confirmed histopathologically. Two representative CT slices per patient were selected at the patient level by an experienced radiologist blinded to final diagnosis. The dataset was split into 70…
A CT-based ResNet50 deep learning model achieved 98.47% test accuracy for classifying sinonasal inverted papilloma versus nasal polyps and normal anatomy, serving as a decision-support tool to assist clinical triage.
Single-center retrospective design using only two CT slices per patient and no clinical variables, limiting generalizability and reliance solely on imaging data.
Evidence
- Peer-reviewedEuropean Archives of Oto-Rhino-Laryngology2026-10-04
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Truvace Impact Record TRV-2026-1331, v1: “CT-based deep learning for identifying sinonasal inverted papilloma: a single-center retrospective study.” Truvace, 2026-10-13. /record/TRV-2026-1331 (accessed at citation time). sha256 86965f4d70c6320e…
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