Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study.

Background: Radiographs play a key role in diagnosis of periodontal diseases. Deep learning models have been explored for image analysis in periodontal diseases. However, there is lacuna of research in the deep learning model-based detection of furcation involvements [FI]. The objective of this study was to determine the accuracy of deep learning model in the detection of FI in axial CBCT images.
Methodology: We obtained initial dataset 285 axial CBCT images among which 143 were normal (without FI) and 142 were abnormal (with FI). Data augmentation technique was used to create 600(300 normal and 300 abnormal) images by using 200 images from the training dataset. Remaining 85(43 normal and 42 abnormal) images were kept for testing of model. ResNet101V2 with transfer learning was used employed for the analysis of images.
Results: Training accuracy of model is 98%, valid accuracy is 97% and test accuracy is 91%. The precision and F1 score were 0.98 and 0.98 respectively. The Area under curve (AUC) was reported at 0.98. The test loss was reported at 0.2170.
Conclusion: The deep learning model (ResNet101V2) can accurately detect the FI in axial CBCT images. However, since our study was preliminary in nature and carried out with relatively smaller dataset, a study with larger dataset will further confirm the accuracy of deep learning models.
Competing Interests: Declarations. Ethics approval and consent to participate: All methods were carried out in accordance with relevant guidelines and regulations. All experimental protocols were approved by the Research ethics committee. Ref. no. REC-23-10-12-01-F (University of Sharjah). Informed written consent was obtained from all subjects involved in the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. Clinical trial number: Not applicable. Competing interests: The authors have no competing interests to declare.
(© 2024. The Author(s).)