Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm |
Lee, Jae-Hong
(Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry)
Kim, Do-hyung (Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry) Jeong, Seong-Nyum (Department of Periodontology, Daejeon Dental Hospital, Institute of Wonkwang Dental Research, Wonkwang University College of Dentistry) Choi, Seong-Ho (Department of Periodontology, Research Institute for Periodontal Regeneration, Yonsei University College of Dentistry) |
1 | Rajpurkar P, Irvin J, Zhu K, Yang B, Mehta H, Duan T, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv e-print 2017;arXiv:1711.05225. |
2 | Rajpurkar P, Hannun AY, Haghpanahi M, Bourn C, Ng AY. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv e-print 2017:arXiv:1707.01836. |
3 | Garcia-Hernandez JJ, Gomez-Flores W, Rubio-Loyola J. Analysis of the impact of digital watermarking on computer-aided diagnosis in medical imaging. Comput Biol Med 2016;68:37-48. DOI |
4 | Litjens G, Kooi T, Bejnordi BE, Setio AA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60-88. DOI |
5 | Kim TS, Obst C, Zehaczek S, Geenen C. Detection of bone loss with different X-ray techniques in periodontal patients. J Periodontol 2008;79:1141-9. DOI |
6 | Armitage GC. Periodontal diagnoses and classification of periodontal diseases. Periodontol 2000 2004;34:9-21. DOI |
7 | Page RC, Eke PI. Case definitions for use in population-based surveillance of periodontitis. J Periodontol 2007;78 (7 Suppl):1387-99. DOI |
8 | Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. Deep convolutional neural networks for computeraided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 2016;35:1285-98. DOI |
9 | Ohsugi H, Tabuchi H, Enno H, Ishitobi N. Accuracy of deep learning, a machine-learning technology, using ultra-wide-field fundus ophthalmoscopy for detecting rhegmatogenous retinal detachment. Sci Rep 2017;7:9425. DOI |
10 | Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv e-print 2014:arXiv:1409.556. |
11 | Lee JH, Lee JS, Park JY, Choi JK, Kim DW, Kim YT, et al. Association of lifestyle-related comorbidities with periodontitis: a nationwide cohort study in Korea. Medicine (Baltimore) 2015;94:e1567. DOI |
12 | Lee JH, Choi JK, Kim SH, Cho KH, Kim YT, Choi SH, et al. Association between periodontal flap surgery for periodontitis and vasculogenic erectile dysfunction in Koreans. J Periodontal Implant Sci 2017;47:96-105. DOI |
13 | Lee JH, Oh JY, Youk TM, Jeong SN, Kim YT, Choi SH. Association between periodontal disease and noncommunicable diseases: A 12-year longitudinal health-examinee cohort study in South Korea. Medicine (Baltimore) 2017;96:e7398. DOI |
14 | Choi JK, Kim YT, Kweon HI, Park EC, Choi SH, Lee JH. Effect of periodontitis on the development of osteoporosis: results from a nationwide population-based cohort study (2003-2013). BMC Womens Health 2017;17:77. DOI |
15 | Lee JH, Kweon HH, Choi JK, Kim YT, Choi SH. Association between periodontal disease and prostate cancer: results of a 12-year longitudinal cohort study in South Korea. J Cancer 2017;8:2959-65. DOI |
16 | Sklan JE, Plassard AJ, Fabbri D, Landman BA. Toward content based image retrieval with deep convolutional neural networks. Proc SPIE Int Soc Opt Eng 2015;9417. |
17 | Graziani F, Karapetsa D, Alonso B, Herrera D. Nonsurgical and surgical treatment of periodontitis: how many options for one disease? Periodontol 2000 2017;75:152-88. DOI |
18 | Martins SH, Novaes AB Jr, Taba M Jr, Palioto DB, Messora MR, Reino DM, et al. Effect of surgical periodontal treatment associated to antimicrobial photodynamic therapy on chronic periodontitis: A randomized controlled clinical trial. J Clin Periodontol 2017;44:717-28. DOI |
19 | Ainamo J, Barmes D, Beagrie G, Cutress T, Martin J, Sardo-Infirri J. Development of the World Health Organization (WHO) community periodontal index of treatment needs (CPITN). Int Dent J 1982;32:281-91. |
20 | Tonetti MS, Jepsen S, Jin L, Otomo-Corgel J. Impact of the global burden of periodontal diseases on health, nutrition and wellbeing of mankind: a call for global action. J Clin Periodontol 2017;44:456-62. DOI |
21 | Lee JH, Lee JS, Choi JK, Kweon HI, Kim YT, Choi SH. National dental policies and socio-demographic factors affecting changes in the incidence of periodontal treatments in Korean: A nationwide populationbased retrospective cohort study from 2002-2013. BMC Oral Health 2016;16:118. DOI |
22 | Lee JH, Oh JY, Choi JK, Kim YT, Park YS, Jeong SN, et al. Trends in the incidence of tooth extraction due to periodontal disease: results of a 12-year longitudinal cohort study in South Korea. J Periodontal Implant Sci 2017;47:264-72. DOI |
23 | Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 26-Jul 1; Las Vegas Valley (NV). Piscataway (NJ): IEEE; 2016. p.2818-26. |
24 | Chollet F. Keras [Internet]. San Francisco (CA): GitHub, Inc.; 2017 [cited 2018 Mar 19]. Available from: https://github.com/keras-team/keras. |
25 | Keskar NS, Mudigere D, Nocedal J, Smelyanskiy M, Tang PT. On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima. arXiv e-print 2017;arXiv:1609.0483. |
26 | Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017;542:115-8. DOI |
27 | Peng X, Sun B, Ali K, Saenko K. Learning deep object detectors from 3D models. 2015 IEEE International Conference on Computer Vision (ICCV); 2015 Dec 7-13; Santiago. Piscataway (NJ): IEEE; 2015. p.1278-86 |
28 | Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, et al. TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv e-print 2016:arXiv:1603.04467. |
29 | Lehman CD, Wellman RD, Buist DS, Kerlikowske K, Tosteson AN, Miglioretti DL. Diagnostic accuracy of digital screening mammography with and without computer-aided detection. JAMA Intern Med 2015;175:1828-37. DOI |
30 | Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-10. DOI |
31 | Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 2017;284:574-82. DOI |
32 | Wang R. Edge detection using convolutional neural network. In: Cheng L, Liu Q, Ronzhin A, editors. Advances in neural networks - ISNN 2016. 13th International Symposium on Neural Networks, ISNN 2016; 2016 Jul 6-8; Saint Petersburg. Cham: Springer International Publishing; 2016. p.12-20. |
33 | Nair V, Hinton GE. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on International Conference on Machine Learning; 2010 Jun 21-24; Haifa. Madison (WI): Omnipress; 2010. p.807-14. |
34 | Ouyang W, Wang X. Joint deep learning for pedestrian detection. 2013 IEEE International Conference on Computer Vision (ICCV); 2013 Dec 1-8; Sydney. Piscataway (NJ): IEEE; 2013. p.2056-63. |