Torul D, Bayrakdar IS, Bozkurt MH, Erdem H, Akcay-Celik M, Ersan-Erdem B, Salman FG. Deep learning-based approach for differential diagnosis of odontogenic cysts from histopathological images. Med Oral Patol Oral Cir Bucal. 2026 Mar 1;31 (2):e196-204.

doi:10.4317/medoral.27697

https://dx.doi.org/doi:10.4317/medoral.27697


1. Frydenlund A, Eramian M, Daley T. Automated classification of four types of developmental odontogenic cysts. Comput Med Imaging Graph. 2014;38(3):151-162. https://doi.org/10.1016/j.compmedimag.2013.12.002 PMid:24411103

2. Lee JH, Kim DH, Jeong SN. Diagnosis of cystic lesions using panoramic and cone beam computed tomographic images based on deep learning neural network. Oral Dis. 2020;26(1):152-158. https://doi.org/10.1111/odi.13223 PMid:31677205

3. Qiu L, Huang M, Xu X, et al. A classification-guided segmentation algorithm based on deep learning for epithelium segmentation in histopathological images of radicular cysts. Annu Int Conf IEEE Eng Med Biol Soc. 2021;2021:3779-3782. https://doi.org/10.1109/EMBC46164.2021.9630552 PMid:34892058

4. Deshmukh J, Shrivastava R, Bharath KP, Mallikarjuna R. Giant radicular cyst of the maxilla. BMJ Case Rep. 2014;2014. https://doi.org/10.1136/bcr-2014-203678 PMid:24792022 PMCid:PMC4025383

5. Rao RS, Shivanna DB, Mahadevpur KS, et al. Deep learning-based microscopic diagnosis of odontogenic keratocysts and non-keratocysts in haematoxylin and eosin-stained incisional biopsies. Diagnostics (Basel). 2021;11(12). https://doi.org/10.3390/diagnostics11122184 PMid:34943424 PMCid:PMC8700488

6. Thompson LD. Dentigerous cyst. Ear Nose Throat J. 2018;97(3):57. https://doi.org/10.1177/014556131809700317 https://doi.org/10.1177/014556131809700304 PMid:29554396

7. Mohanty S, Shivanna DB, Rao RS, et al. Building automation pipeline for diagnostic classification of sporadic odontogenic keratocysts and non-keratocysts using whole-slide images. Diagnostics (Basel). 2023;13(21). https://doi.org/10.3390/diagnostics13213384 PMid:37958281 PMCid:PMC10648794

8. Mohanty S, Shivanna DB, Rao RS, et al. Development of automated risk stratification for sporadic odontogenic keratocyst whole slide images with an attention-based image sequence analyzer. Diagnostics (Basel). 2023;13(23). https://doi.org/10.3390/diagnostics13233539 PMid:38066780 PMCid:PMC10706752

9. Krishna AB, Tanveer A, Bhagirath PV, Gannepalli A. Role of artificial intelligence in diagnostic oral pathology: A modern approach. J Oral Maxillofac Pathol. 2020;24(1):152-156. https://doi.org/10.4103/jomfp.JOMFP_215_19 PMid:32508465 PMCid:PMC7269295

10. Radakovich N, Nagy M, Nazha A. Machine learning in haematological malignancies. Lancet Haematol. 2020;7(7):e541-e550. https://doi.org/10.1016/S2352-3026(20)30121-6 PMid:32589980

11. Jiang Y, Yang M, Wang S, Li X, Sun Y. Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer Commun (Lond). 2020;40(4):154-166. https://doi.org/10.1002/cac2.12012 PMid:32277744 PMCid:PMC7170661

12. Liu Z, Liu J, Zhou Z, et al. Differential diagnosis of ameloblastoma and odontogenic keratocyst by machine learning of panoramic radiographs. Int J Comput Assist Radiol Surg. 2021;16(3):415-422. https://doi.org/10.1007/s11548-021-02309-0 PMid:33547985 PMCid:PMC7946691

13. Kwon O, Yong TH, Kang SR, et al. Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network. Dentomaxillofac Radiol. 2020;49(8):20200185. https://doi.org/10.1259/dmfr.20200185 PMid:32574113 PMCid:PMC7719862

14. Vu TH, Mousavi HS, Monga V, Rao G, Rao UK. Histopathological image classification using discriminative feature-oriented dictionary learning. IEEE Trans Med Imaging. 2016;35(3):738-751. https://doi.org/10.1109/TMI.2015.2493530 PMid:26513781 PMCid:PMC4807738

15. Goodfellow I, Bengio Y, Courville A. Deep learning. Cambridge (MA): MIT Press; 2016.

16. Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2016. p. 2818-2826. https://doi.org/10.1109/CVPR.2016.308

17. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv. 2014;1409.1556. https://doi.org/10.48550/arXiv.1409.1556

18. Chollet F. Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; 2017. p. 1800-1807. https://doi.org/10.1109/CVPR.2017.195

19. Yang H, Jo E, Kim HJ, et al. Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs. J Clin Med. 2020;9(6). https://doi.org/10.3390/jcm9061839 PMid:32545602 PMCid:PMC7356620

20. Cai X, Zhang H, Wang Y, Zhang J, Li T. Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts. Int J Oral Sci. 2024;16(1):16. https://doi.org/10.1038/s41368-024-00287-y PMid:38403665 PMCid:PMC10894880

21. Sukegawa S, Ono S, Tanaka F, et al. Effectiveness of deep learning classifiers in histopathological diagnosis of oral squamous cell carcinoma by pathologists. Sci Rep. 2023;13(1):11676. https://doi.org/10.1038/s41598-023-38343-y PMid:37468501 PMCid:PMC10356919

22. Rao RS, Shivanna DB, Lakshminarayana S, et al. Ensemble deep-learning-based prognostic and prediction for recurrence of sporadic odontogenic keratocysts on hematoxylin and eosin stained pathological images of incisional biopsies. J Pers Med. 2022;12(8). https://doi.org/10.3390/jpm12081220 PMid:35893314 PMCid:PMC9332803

23. Sakamoto K, Morita K, Ikeda T, Kayamori K. Deep learning-based identification of odontogenic keratocysts in hematoxylin- and eosin-stained jaw cyst specimens. arXiv. 2019;1901.03857. https://doi.org/10.48550/arXiv.1901.03857