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

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Název: Application of artificial intelligence-based detection of furcation involvement in mandibular first molar using cone beam tomography images- a preliminary study.
Autoři: Shetty, Shishir, Talaat, Wael, AlKawas, Sausan, Al-Rawi, Natheer, Reddy, Sesha, Hamdoon, Zaid, Kheder, Waad, Acharya, Anirudh, Ozsahin, Dilber Uzun, David, Leena R.
Zdroj: BMC Oral Health; 12/4/2024, Vol. 24 Issue 1, p1-8, 8p
Témata: MOLARS, PERIODONTAL disease, COMPUTED tomography, ARTIFICIAL intelligence, DESCRIPTIVE statistics, DEEP learning, MATHEMATICAL models, MANDIBLE, DIGITAL image processing, THEORY, DATA analysis software
Abstrakt: 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. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt: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. [ABSTRACT FROM AUTHOR]
ISSN:14726831
DOI:10.1186/s12903-024-05268-5