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[Preliminary study of alveolar socket measurement on CBCT based on SAM]
Linfeng Fan1, Zhongchen Song, Chunan Zhang
1Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine. Shanghai 200011, China.
Purpose:
To evaluate the tool of automatic measuring CBCT, developed based on deep learning technology, and to compare its accuracy with manual measurement and to verify its effectiveness and feasibility.
Methods:
Twenty-nine adult patients (11 males, 18 females) with mean age of (31.31±13.77) years old were enrolled, CBCT of enrolled patients were collected, and 427 transverse sections of alveolar teeth were extracted(5-5 position). A novel Segment Anything Model(SAM) -based interactive segmentation and measurement tool was developed and applied to the assessment of alveolar socket dimensions in CBCT. Manual and automatic measurements of bone mass in the buccal and lingual transverse section of the alveolar socket were performed by establishing the test set and the validation set, respectively, and the data were compared.
Results:
There was significant correlation and consistency between CBCT automatic measurement method and manual measurement. The coefficient of determination(R2) of regression analysis in test set was 0.942, the measurement error in validation set was mainly varing between -0.43～0.47 mm, Pearson correlation coefficient was 0.9746 (P＜0.001).
Conclusions:
This study developed an automatic CBCT measurement tool based on SAM through deep learning, with high accuracy and significantly improved the efficiency of alveolar socket measurement.
