Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

Mona Alsomali; Shatha Alghamdi; Shahad Alotaibi; Sara Alfadda; Najwa Altwaijry; Isra Alturaiki; Asma'a Al-Ekrish

doi:10.1016/j.sdentj.2022.01.002

Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations

Saudi Dent J. 2022 Mar;34(3):220-225. doi: 10.1016/j.sdentj.2022.01.002. Epub 2022 Jan 10.

Authors

Mona Alsomali¹, Shatha Alghamdi¹, Shahad Alotaibi², Sara Alfadda³, Najwa Altwaijry⁴, Isra Alturaiki⁵, Asma'a Al-Ekrish⁶

Affiliations

¹ College of Dentistry, King Saud University, P.O BOX 60169, Riyadh 11545, Saudi Arabia.
² College of Computer and Information Sciences, King Saud University, P.O BOX 145111, Riyadh 4545, Saudi Arabia.
³ Department of Prosthetic Dental Sciences, College of Dentistry, King Saud University, P.O BOX 60169, Riyadh 11545, Saudi Arabia.
⁴ Department of Computer Science, College of Computer and Information Sciences, King Saud University, P.O BOX 145111, Riyadh 4545, Saudi Arabia.
⁵ Department of Information Technology, College of Computer and Information Sciences, King Saud University, P.O BOX 145111, Riyadh 4545, Saudi Arabia.
⁶ Department of Oral Medicine and Diagnostic Sciences, College of Dentistry, King Saud University, P.O BOX 60169, Riyadh 11545, Saudi Arabia.

Abstract

Objectives: To develop a Deep Learning Artificial Intelligence (AI) model that automatically localizes the position of radiographic stent gutta percha (GP) markers in cone beam computed tomography (CBCT) images to identify proposed implant sites within the images, and to test the performance of the newly developed AI model.

Materials and methods: Thirty-four CBCT datasets were used for initial model training, validation and testing. The CBCT datasets were those of patients who had a CBCT examination performed wearing a radiographic stent for implant treatment planning. The datasets were exported in Digital Imaging and Communications in Medicine (DICOM), then imported into the software Horos ®. Each GP marker was manually labelled for object detection and recognition by the deep learning model by drawing rectangles around the GP markers in all axial images, then the labelled images were split into training, validation, and test sets. The axial sections of 30 CBCT datasets were randomly divided into training and validation sets. four CBCT datasets were used for testing the performance of the deep learning model. Descriptive statistics were calculated for the number of GP markers present, number of correct and incorrect identifications of GP markers.

Result: The AI model had an 83% true positive rate for identification of the GP markers. Of the areas labelled by the AI model as GP markers, 28 % were not truly GP markers, but the overall false positive rate was 2.8 %.

Conclusion: An AI model for localization of GP markers in CBCT images was able to identify most of the GP markers, but 2.8% of the results were false positive and 17% were missed GP markers. Using only axial images for training an AI program is not enough to give an accurate AI model performance.

Keywords: Algorithms; Artificial intelligence; Computed Tomography; Cone Beam; Deep learning; Dental Implant; Stents.