Öz
Aim: The aim of this study is to create a model that enables the detection of dentigerous cysts on panoramic radiographs in order to enable dentistry students to meet and apply artificial intelligence applications.
Methods: E.O. and I.T. who are 5th year students of the faculty of dentistry, detected 36 orthopantomographs whose histopathological examinations were determined as Dentigerous Cyst, and the affected teeth and cystic cavities were segmented using CranioCatch's artificial intelligence supported clinical decision support system software. Since the sizes of the images in the dataset are different from each other, all images were resized as 1024x514 and augmented as vertical flip, horizontal flip and both flips were applied on the train-validation. Within the obtained data set, 200 epochs were trained with PyTorch U-Net with a learning rate of 0.001, train: 112 images (112 labels), val: 16 images (16 labels). With the model created after the segmentations were completed, new dentigerous cyst orthopantomographs were tested and the success of the model was evaluated.
Results: With the model created for the detection of dentigerous cysts, the F1 score (2TP / (2TP+FP+FN)) precision (TP/ (TP+N)) and sensitivity (TP/ (TP+FN)) were found to be 0.67, 0.5 and 1, respectively.
Conclusion: With a CNN approach for the analysis of dentigerous cyst images, the precision has been found to be 0.5 even in a small database. These methods can be improved, and new graduate dentists can gain both experience and save time in the diagnosis of cystic lesions with radiographs.
Anahtar Kelimeler
Destekleyen Kurum
We would like to thank CranioCatch for their confidence and support in our study.
Teşekkür
We would like to thank CranioCatch for their confidence and support in our study.
Kaynakça
- 1. Orhan, K. and G. Ünsal, Applications of Ultrasonography in Maxillofacial/Intraoral Inflammatory and Cystic Lesions, in Ultrasonography in Dentomaxillofacial Diagnostics, K. Orhan, Editor. 2021, Springer International Publishing. p. 259-273.
- 2. White, S.C. and M.J. Pharoah, Oral Radiology: Principles and Interpretation. 7 ed. 2013: Mosby.
- 3. Cakarer, S., et al., Decompression, enucleation, and implant placement in the management of a large dentigerous cyst. J Craniofac Surg, 2011. 22(3): p. 922-4.
- 4. Wright, J.M. and M. Soluk Tekkesin, Odontogenic tumors: where are we in 2017 ? J Istanb Univ Fac Dent, 2017. 51(3 Suppl 1): p. S10-S30.
- 5. Kurt Bayrakdar, S., et al., A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging, 2021. 21(1): p. 86.
- 6. Kayi Cangir, A., et al., CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: "Impact of surgical modality choice". World J Surg Oncol, 2021. 19(1): p. 147.
- 7. Gursoy Coruh, A., et al., A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification. Br J Radiol, 2021. 94(1123): p. 20210222.
- 8. Amasya, H., et al., Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models. Dentomaxillofac Radiol, 2020. 49(5): p. 20190441.
- 9. Murata, M., et al., Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol, 2019. 35(3): p. 301-307.
- 10. Pauwels, R., A brief introduction to concepts and applications of artificial intelligence in dental imaging. Oral Radiol, 2021. 37(1): p. 153-160.
- 11. Jiang, B., et al., Development and application of artificial intelligence in cardiac imaging. Br J Radiol, 2020. 93(1113): p. 20190812.
- 12. Grabowski, M., et al., A public database of macromolecular diffraction experiments. Acta Crystallogr D Struct Biol, 2016. 72(Pt 11): p. 1181-1193.
- 13. Xu, X., et al., 1000-Case Reader Study of Radiologists' Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System. Ultrasound Med Biol, 2018. 44(8): p. 1694-1702.
- 14. Aggarwal, R., et al., Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med, 2021. 4(1): p. 65.
- 15. Rauschecker, A.M., et al., Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI. Radiology, 2020. 295(3): p. 626-637.
- 16. Sin, C., et al., A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res, 2021.
Öz
Kaynakça
- 1. Orhan, K. and G. Ünsal, Applications of Ultrasonography in Maxillofacial/Intraoral Inflammatory and Cystic Lesions, in Ultrasonography in Dentomaxillofacial Diagnostics, K. Orhan, Editor. 2021, Springer International Publishing. p. 259-273.
- 2. White, S.C. and M.J. Pharoah, Oral Radiology: Principles and Interpretation. 7 ed. 2013: Mosby.
- 3. Cakarer, S., et al., Decompression, enucleation, and implant placement in the management of a large dentigerous cyst. J Craniofac Surg, 2011. 22(3): p. 922-4.
- 4. Wright, J.M. and M. Soluk Tekkesin, Odontogenic tumors: where are we in 2017 ? J Istanb Univ Fac Dent, 2017. 51(3 Suppl 1): p. S10-S30.
- 5. Kurt Bayrakdar, S., et al., A deep learning approach for dental implant planning in cone-beam computed tomography images. BMC Med Imaging, 2021. 21(1): p. 86.
- 6. Kayi Cangir, A., et al., CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: "Impact of surgical modality choice". World J Surg Oncol, 2021. 19(1): p. 147.
- 7. Gursoy Coruh, A., et al., A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification. Br J Radiol, 2021. 94(1123): p. 20210222.
- 8. Amasya, H., et al., Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models. Dentomaxillofac Radiol, 2020. 49(5): p. 20190441.
- 9. Murata, M., et al., Deep-learning classification using convolutional neural network for evaluation of maxillary sinusitis on panoramic radiography. Oral Radiol, 2019. 35(3): p. 301-307.
- 10. Pauwels, R., A brief introduction to concepts and applications of artificial intelligence in dental imaging. Oral Radiol, 2021. 37(1): p. 153-160.
- 11. Jiang, B., et al., Development and application of artificial intelligence in cardiac imaging. Br J Radiol, 2020. 93(1113): p. 20190812.
- 12. Grabowski, M., et al., A public database of macromolecular diffraction experiments. Acta Crystallogr D Struct Biol, 2016. 72(Pt 11): p. 1181-1193.
- 13. Xu, X., et al., 1000-Case Reader Study of Radiologists' Performance in Interpretation of Automated Breast Volume Scanner Images with a Computer-Aided Detection System. Ultrasound Med Biol, 2018. 44(8): p. 1694-1702.
- 14. Aggarwal, R., et al., Diagnostic accuracy of deep learning in medical imaging: a systematic review and meta-analysis. NPJ Digit Med, 2021. 4(1): p. 65.
- 15. Rauschecker, A.M., et al., Artificial Intelligence System Approaching Neuroradiologist-level Differential Diagnosis Accuracy at Brain MRI. Radiology, 2020. 295(3): p. 626-637.
- 16. Sin, C., et al., A deep learning algorithm proposal to automatic pharyngeal airway detection and segmentation on CBCT images. Orthod Craniofac Res, 2021.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Diş Hekimliği |
| Bölüm | Original Research Articles |
| Yazarlar | |
| Yayımlanma Tarihi | 30 Nisan 2022 |
| Gönderilme Tarihi | 1 Temmuz 2021 |
| Yayımlandığı Sayı | Yıl 2022 Cilt: 49 Sayı: 1 |
