[1] Bali J, Garg R, Bali RT. Artificial intelligence (AI) in healthcare and biomedical research: Why a strong computational/AI bioethics framework is required? [J]. Indian J Ophthalmol, 2019, 67(1):3-6. [2] Chan S, Siegel EL. Will machine learning end the viability of radiology as a thriving medical specialty ?[J]. Br J Radiol, 2019, 92(1094):20180416. [3] Mintz Y, Brodie R. Introduction to artificial intelligence in medicine [J].Minim Invasive Ther Allied Technol, 2019, 28(2):73-81. [4] Gibney E. What Google's winning go algorithm will do next [J]. Nature, 2016, 531(7594):284-285. [5] Horvitz E. AI, people, and society [J]. Science, 2017, 357(6346):7. [6] Henson DB, Thampy R. Preventing blindness from glaucoma [J]. BMJ, 2005, 331(7509):120-121. [7] Bourne RR, Taylor HR, Flaxman SR, et al. Number of people blind or visually impaired by glaucoma worldwide and in world regions 1990-2010: A Meta-analysis [J]. PLoS One, 2016, 11(10):e0162229. [8] Phan S, Satoh S, Yoda Y, et al. Evaluation of deep convolutional neural networks for glaucoma detection [J]. Jpn J Ophthalmol, 2019, 63(3):276-283. [9] Cerentini A, Welfer D, Cordeiro MD, et al. Automatic identification of glaucoma using deep learning methods [C].Medinfo Precision Healthcare Through Informatics, 2017. [10] Kell AJ, McDermott JH. Deep neural network models of sensory systems: windows onto the role of task constraints [J]. Curr Opin Neurobiol, 2019, 55:121-132. [11] Hirsch FR, Scagliotti GV, Mulshine JL, et al. Lung cancer: Current therapies and new targeted treatments [J]. Lancet, 2017, 389(10066):299-311. [12] Ciompi F, Chung K, Van Riel SJ, et al. Towards automatic pulmonary nodule management in lung cancer screening with deep learning [J]. Sci Rep, 2017, 7:46479. [13] Jacobs C, Van Rikxoort EM, Scholten ET, et al. Solid, part-solid, or non-solid? Classification of pulmonary nodules in low-dose chest computed tomography by a computer-aided diagnosis system [J]. Invest Radiol, 2015, 50(3):168-173. [14] Pehrson LM, Nielsen MB, Ammitzbøl Lauridsen C. Automatic pulmonary nodule detection applying deep learning or machine learning algorithmsto the LIDC-IDRI database: A systematic review [J]. Diagnostics (Basel), 2019, 9(1):29. [15] Torre LA, Islami F, Siegel RL, et al. Global cancer in women: burden and trends [J]. Cancer Epidemiol Biomarkers Prev, 2017, 26(4):444-457. [16] Islami F, Torre LA, Drope JM, et al. Global cancer in women: cancer control priorities [J]. Cancer Epidemiol Biomarkers Prev, 2017, 26(4):458-470. [17] Patcas R, Timofte R, Volokitin A, et al. Facial attractiveness of cleft patients: a direct comparison between artificial-intelligence-based scoring and conventional rater groups [J]. Eur J Orthod, 2019, 41(4):428-433. [18] Zhang W, Li J, Li ZB, et al. Predicting postoperative facial swelling following impacted mandibular third molars extraction by using artificial neural networks evaluation [J]. Sci Rep, 2018, 8(1):12281. [19] Kim DW, Kim H, Nam W, et al. Machine learning to predict the occurrence of bisphosphonate-related osteonecrosis of the jaw associated with dental extraction: A preliminary report [J]. Bone, 2018, 116:207-214. [20] Patcas R, Bernini DAJ, Volokitin A, et al. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age [J]. Int J Oral Maxillofac Surg, 2019, 48(1):77-83. [21] Cheng C, Cheng X, Dai N, et al. Prediction of facial deformation after complete denture prosthesis using BP neural network [J]. Comput Biol Med, 2015, 66:103-112. [22] Franco A, Thevissen P, Coudyzer W, et al. Feasibility and validation of virtual autopsy for dental identification using the Interpol dental codes [J]. J Forensic Leg Med, 2013, 20(4):248-254. [23] Marroquin TY, Karkhanis S, Kvaal SI, et al. Age estimation in adults by dental imaging assessment systematic review [J]. Forensic Sci Int, 2017, 275:203-211. [24] Wang CW, Huang CT, Lee JH, et al. A benchmark for comparison of dental radiography analysis algorithms [J]. Med Image Anal, 2016, 31:63-76. [25] Rad AE, Mohd Rahim MS, Rehman A, et al. Evaluation of current dental radiographs segmentation approaches in computer-aided applications [J]. IETE Technical Review, 2013, 30(3):210. [26] Said EH, Nassar DEM, Fahmy G, et al. Teeth segmentation in digitized dental X-ray films using mathematical morphology [J]. IEEE Transactions on Information Forensics and Security, 2006, 1(2):178-189. [27] Lee JH, Kim DH, Jeong SN, et al. Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm [J]. J Periodontal Implant Sci, 2018, 48(2):114-123. [28] Lee JH, Kim DH, Jeong SN, et al. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm [J]. J Dent, 2018, 77:106-111. [29] Dougherty G, Davros W. Digital image processing for medical applications [J]. Medical Physics, 2010, 37(2):948. [30] Le Hoang Son, Tran Manh Tuan. Dental segmentation from X-ray images using semi-supervised fuzzy clustering with spatial constraints [J]. Engineering Applications of Artificial Intelligence, 2017,186-195. [31] Subramanyam RB, Prasad KP. Different image segmentation techniques for dental image, extraction [J]. Int J Eng Res App, 2014, 4(7):173-177. [32] Amer YY, Aqel MJ. An efficient segmentation algorithm for panoramic dental images [J]. Procedia Computer Science, 2015, 65:718-725. [33] Son LH, Tuan TM. A cooperative semi-supervised fuzzy clustering framework for dental X-ray image segmentation [J]. Expert Systems with Applications, 2016, 46:380-393. [34] Jader G, Oliveira L, Pithon M. Automatic segmenting teeth in X-ray images: Trends, a novel data set, benchmarking and future perspectives [J]. Expert Systems with Applications, 2018,107:15-31. |