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

• Published in: Scientific reports Vol. 13; no. 1; pp. 11653 - 12
• Main Authors: Hwang, In-Kyung, Kang, Se-Ryong, Yang, Su, Kim, Jun-Min, Kim, Jo-Eun, Huh, Kyung-Hoe, Lee, Sam-Sun, Heo, Min-Suk, Yi, Won-Jin, Kim, Tae-Il
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.07.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 692/700/1421/1846; 692/700/3032/3149/3106; Classification; Cone-Beam Computed Tomography - methods; Deep learning; Edentulous; Humanities and Social Sciences; Humans; Maxilla - diagnostic imaging; Maxilla - surgery; Maxillary sinus; Maxillary Sinus - diagnostic imaging; Maxillary Sinus - surgery; Mouth, Edentulous; multidisciplinary; Science; Science (multidisciplinary); Sinus Floor Augmentation - methods; Sinuses
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-023-38273-9

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.