Papapanou PN, Mariano S, Nurcan B, et al. Periodontitis: consensus report of workgroup 2 of the 2017 world workshop on the classification of periodontal and peri-implant diseases and conditions. J Periodontol. 2018;89:S173–82.
Tan L, Liu J, Liu Z. Association between periodontitis and the prevalence and prognosis of prediabetes: a population-based study. J Transl Med. 2023;21(1):484.
Article PubMed PubMed Central Google Scholar
Siow DSF, Goh EXJ, Ong MMA, et al. Risk factors for tooth loss and progression of periodontitis in patients undergoing periodontal maintenance therapy. J Clin Periodontol. 2022;50(1):61–70.
Luthra S, Orlandi M, Hussain SB, et al. Treatment of periodontitis and C-reactive protein: a systematic review and meta-analysis of randomized clinical trials. J Clin Periodontol. 2022;50(1):45–60.
Article PubMed PubMed Central Google Scholar
Li A, Qiu B, Goettsch M, et al. Association between the quality of plant-based diets and periodontitis in the US general population. J Clin Periodontol. 2023;50(5):591–603.
Article CAS PubMed Google Scholar
Frencken JE, Sharma P, Stenhouse L, et al. Global epidemiology of dental caries and severe periodontitis-a comprehensive review. J Clin Periodontol. 2017;44(Suppl 18):S94–105.
Gedik R, Marakoglu I, De Mirer S. Assessment of alveolar bone levels from bitewing, periapical and panoramic radiographs in periodontitis patients. West Indian Med J. 2008;57(4):410–3.
Huynh JD, Rhodes SC, Hatton JF, et al. Satisfaction of search in periapical radiograph interpretation. J Endod. 2021;47(2):291–6.
Hermanson BP, Burgdorf GC, Hatton JF, et al. Visual fixation and scan patterns of dentists viewing dental periapical radiographs: an eye tracking pilot study. J Endod. 2019;44(5):722–7.
Vandenberghe B, Jacobs R, Bosmans H. Modern dental imaging: a review of the current technology and clinical applications in dental practice. Eur Radiol. 2010;20(11):2637–55.
Musri N, Christie B, Ichwan S, et al. Deep learning convolutional neural network algorithms for the early detection and diagnosis of dental caries on periapical radiographs: a systematic review. Imaging Sci Dent. 2021;51(3):237–42.
Article PubMed PubMed Central Google Scholar
Zhu X, Cheng Z, Wang S, et al. Coronary angiography image segmentation based on PSPNet. Comput Methods Programs Biomed. 2020;200(4): 105897.
Liu D, Jia Z, Jin M, et al. Cardiac magnetic resonance image segmentation based on convolutional neural network. Comput Methods Programs Biomed. 2020;197(45): 105755.
Chang P, Dang J, Dai J, et al. Real-time respiratory tumor motion prediction based on a temporal convolutional neural network: prediction model development study. J Med Internet Res. 2021;23(8): e27235.
Article PubMed PubMed Central Google Scholar
Bayrakdar IS, Orhan K, Akarsu S, et al. Deep-learning approach for caries detection and segmentation on dental bitewing radiographs. Oral Radiol. 2022;38:468–79.
Ver Berne J, Saadi SB, Politis C, et al. A deep learning approach for radiological detection and classification of radicular cysts and periapical granulomas. J Dent. 2023;135: 104581.
Watanabe H, Ariji Y, Fukuda M, et al. Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study. Oral Radiol. 2021;37:487–93.
Liu Y, Fan R, Yi J, et al. A fusion framework of deep learning and machine learning for predicting sgRNA cleavage efficiency. Comput Biol Med. 2023;165: 107476.
Article CAS PubMed Google Scholar
Chang J, Chang MF, Angelov N, et al. Application of deep machine learning for the radiographic diagnosis of periodontitis. Clin Oral Invest. 2022;26(11):6629–37.
Lee CT, Kabir T, Nelson J, et al. Use of the deep learning approach to measure alveolar bone level. J Clin Periodontol. 2022;49(3):260–9.
Article CAS PubMed Google Scholar
Caton JG, Armitage G, Berglundh T, Chapple ILC, et al. A new classification scheme for periodontal and peri-implant diseases and conditions - Introduction and key changes from the 1999 classification. J Clin Periodontol. 2018;89(Suppl 1):S1–8.
Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Adv Neural Inf Proc Syst. 2012;25(2):84–90.
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. Computer Science. 2014. arXiv: 1409.1556.
Ou X, Yan P, Zhang Y, et al. Moving object detection method via ResNet-18 with encoder-decoder structure in complex scenes. IEEE Access. 2019. https://doi.org/10.1109/ACCESS.2019.2931922.
Tian L, Wu W, Yu T. Graph random forest: a graph embedded algorithm for identifying highly connected important features. Biomolecules. 2023;13(7):1153.
Article CAS PubMed PubMed Central Google Scholar
Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565–7.
Article CAS PubMed Google Scholar
Yu L, Gan S, Chen Y, et al. A novel hybrid approach: instance weighted hidden Naive Bayes. Mathematics (Basel). 2021;9(22):2982.
Cornish RP, Bartlett JW, Macleod J, et al. Complete case logistic regression with a dichotomised continuous outcome led to biased estimates. J Clin Epidemiol. 2022;154:33–41.
Zhang Q, Sheng J, Zhang Q, et al. Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer’s disease. Comput Biol Med. 2023;165: 107392.
Selvaraju RR, Cogswell M, Das A, et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vision. 2020;128(2):336–59.
Lakhani P, Sundaram B. Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology. 2017;284(2):574–82.
Chang J, Meng HW, Lalla E, et al. The impact of smoking on non-surgical periodontal therapy: a systematic review and meta-analysis. J Clin Periodontol. 2021;48(1):60–75.
Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA–J Am Med Assoc. 2016;316(22):2402–10.
Comments (0)