SinusC-Net for automatic classification of surgical plans for maxillary sinus augmentation using a 3D distance-guided network

Autor: In-Kyung Hwang, Se-Ryong Kang, Su Yang, Jun-Min Kim, Jo-Eun Kim, Kyung-Hoe Huh, Sam-Sun Lee, Min-Suk Heo, Won-Jin Yi, Tae-Il Kim
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Scientific Reports, Vol 13, Iss 1, Pp 1-12 (2023)
Druh dokumentu: article
ISSN: 2045-2322
DOI: 10.1038/s41598-023-38273-9
Popis: Abstract The objective of this study was to automatically classify surgical plans for maxillary sinus floor augmentation in implant placement at the maxillary posterior edentulous region using a 3D distance-guided network on CBCT images. We applied a modified ABC classification method consisting of five surgical approaches for the deep learning model. The proposed deep learning model (SinusC-Net) consisted of two stages of detection and classification according to the modified classification method. In detection, five landmarks on CBCT images were automatically detected using a volumetric regression network; in classification, the CBCT images were automatically classified as to the five surgical approaches using a 3D distance-guided network. The mean MRE for landmark detection was 0.87 mm, and SDR for 2 mm or lower, 95.47%. The mean accuracy, sensitivity, specificity, and AUC for classification by the SinusC-Net were 0.97, 0.92, 0.98, and 0.95, respectively. The deep learning model using 3D distance-guidance demonstrated accurate detection of 3D anatomical landmarks, and automatic and accurate classification of surgical approaches for sinus floor augmentation in implant placement at the maxillary posterior edentulous region.
Databáze: Directory of Open Access Journals
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