Convolutional neural network for automated classification of osteonecrosis and related mandibular trabecular patterns

Abadi, 2016, TensorFlow: a system for large-scale machine learning, 265 Ahn, 2021, Automated mesiodens classification system using deep learning on panoramic radiographs of children, Diagnostics, 11, 1477, 10.3390/diagnostics11081477 Arce, 2009, Imaging findings in bisphosphonate-related osteonecrosis of jaws, J. Oral Maxillofac. Surg., 67, 75, 10.1016/j.joms.2008.12.002 Baron, 2011, Denosumab and bisphosphonates: different mechanisms of action and effects, Bone, 48, 677, 10.1016/j.bone.2010.11.020 Bedogni, 2012, Learning from experience. Proposal of a refined definition and staging system for bisphosphonate-related osteonecrosis of the jaw (BRONJ), Oral Dis., 18, 621, 10.1111/j.1601-0825.2012.01903.x Bradski, 2000 Buslaev, 2020, Albumentations: fast and flexible image augmentations, Information, 11, 125, 10.3390/info11020125 Chollet, 2015 Estai, 2022, Deep learning for automated detection and numbering of permanent teeth on panoramic images, Dentomaxillofac. Radiol., 51, 51, 10.1259/dmfr.20210296 Estai, 2022, Evaluation of a deep learning system for automatic detection of proximal surface dental caries on bitewing radiographs, Oral Surg. Oral Med. Oral Pathol. Oral Radiol., 10.1016/j.oooo.2022.03.008 Gaêta-Araujo, 2021, Why do some extraction sites develop medication-related osteonecrosis of the jaw and others do not? A within-patient study assessing radiographic predictors, Int. J. oral Implantol. (Berlin, Ger., 14, 87 Grandini, 2020 Hamada, 2014, A simple evaluation method for early detection of bisphosphonate-related osteonecrosis of the mandible using computed tomography, J. Cranio-Maxillofac. Surg., 42, 924, 10.1016/j.jcms.2014.01.012 Harris, 2020, Array programming with NumPy, Nature, 585, 357, 10.1038/s41586-020-2649-2 He, 2016, Deep residual learning for image recognition, 770 Huang, 2017, Densely connected convolutional networks, 2261 Hunter, 2007, Matplotlib: a 2D graphics environment, Comput. Sci. Eng., 9, 90, 10.1109/MCSE.2007.55 Khan, 2015, Diagnosis and management of osteonecrosis of the jaw: a systematic review and international consensus, J. Bone Miner. Res., 30, 3, 10.1002/jbmr.2405 Klingelhöffer, 2016, Can dental panoramic radiographic findings serve as indicators for the development of medication-related osteonecrosis of the jaw?, Dentomaxillofac. Radiol., 45, 10.1259/dmfr.20160065 Kubo, 2018, Panoramic radiographic features that predict the development of bisphosphonate-related osteonecrosis of the jaw, Oral Radiol., 34, 151, 10.1007/s11282-017-0293-9 Kuwada, 2020, Deep learning systems for detecting and classifying the presence of impacted supernumerary teeth in the maxillary incisor region on panoramic radiographs, Oral surgOral Med. Oral Pathol. Oral Radiol., 130, 464, 10.1016/j.oooo.2020.04.813 Kwon, 2020, Automatic diagnosis for cysts and tumors of both jaws on panoramic radiographs using a deep convolution neural network, Dentomaxillofac. Radiol., 49, 20200185, 10.1259/dmfr.20200185 Lahoud, 2022, Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT, J. Dent., 116, 10.1016/j.jdent.2021.103891 Leite, 2021, Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs, Clin. Oral Investig., 25, 2257, 10.1007/s00784-020-03544-6 Marx, 2003, Pamidronate (Aredia) and zoledronate (Zometa) induced avascular necrosis of the jaws: a growing epidemic, J. Oral Maxillofac. Surg., 61, 1115, 10.1016/S0278-2391(03)00720-1 Moreno-Rabié, 2020, Early imaging signs of the use of antiresorptive medication and MRONJ: a systematic review, Clin. Oral Investig., 24, 2973, 10.1007/s00784-020-03423-0 Moreno-Rabié, 2022, Radiographic predictors for MRONJ in oncologic patients undergoing tooth extraction, Sci. Rep., 12, 11280, 10.1038/s41598-022-15254-y Nicolatou-Galitis, 2020, Alveolar bone histological necrosis observed prior to extractions in patients, who received bone-targeting agents, Oral Dis., 26, 10.1111/odi.13294 Nicolielo, 2020, Relationship between trabecular bone architecture and early dental implant failure in the posterior region of the mandible, Clin. Oral Implants Res., 31, 153, 10.1111/clr.13551 Pachêco-Pereira, 2019, Dental imaging of trabecular bone structure for systemic disorder screening: a systematic review, Oral Dis., 10.1111/odi.12950 Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825 Rajee, 2021, Dental image segmentation and classification using inception Resnetv2, IETE J. Res., 1–17 Ruggiero, 2014, American Association of Oral and Maxillofacial Surgeons Position Paper on medication-related osteonecrosis of the Jaw—2014 update, J. Oral Maxillofac. Surg., 72, 1938, 10.1016/j.joms.2014.04.031 Ruggiero, 2022, American Association of Oral and Maxillofacial Surgeons’ position paper on medication-related osteonecrosis of the Jaws—2022 update, J. Oral Maxillofac. Surg., 80, 920, 10.1016/j.joms.2022.02.008 Russakovsky, 2015, ImageNet large scale visual recognition challenge, Int. J. Comput. Vis., 115, 211, 10.1007/s11263-015-0816-y Saia, 2010, Occurrence of bisphosphonate-related osteonecrosis of the jaw after surgical tooth extraction, J. Oral Maxillofac. Surg., 68, 797, 10.1016/j.joms.2009.10.026 Sandler, 2018, MobileNetV2: inverted residuals and linear bottlenecks, 4510 Selvaraju, 2020, Grad-CAM: visual explanations from deep networks via gradient-based localization, Int. J. Comput. Vis., 128, 336, 10.1007/s11263-019-01228-7 Shaheen, 2021, A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomographyA validation study, J. Dent., 115, 10.1016/j.jdent.2021.103865 Sivasundaram, 2021, Performance analysis of classification and segmentation of cysts in panoramic dental images using convolutional neural network architecture, Int. J. Imaging Syst. Technol., 31, 2214, 10.1002/ima.22625 Sukegawa, 2021, Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images, Biomolecules, 11, 815, 10.3390/biom11060815 Szegedy, 2016, Inception-v4, inception-ResNet and the impact of residual connections on learning, 2017, 4278 Taguchi, 1997, Observer agreement in the assessment of mandibular trabecular bone pattern from panoramic radiographs, Dentomaxillofac. Radiol., 26, 90, 10.1038/sj.dmfr.4600213 Thompson, 2020 Vranckx, 2020, Artificial intelligence (AI)-driven molar angulation measurements to predict third molar eruption on panoramic radiographs, Int. J. Environ. Res. Public Health, 17, 3716, 10.3390/ijerph17103716 Walton, 2019, Medication related osteonecrosis of the jaw in osteoporotic vs oncologic patients—quantifying radiographic appearance and relationship to clinical findings, Dentomaxillofac. Radiol., 48, 20180128, 10.1259/dmfr.20180128 Yamashita, 2018, Convolutional neural networks: an overview and application in radiology, Insights Imaging, 9, 611, 10.1007/s13244-018-0639-9 Yang, 2020, Deep learning for automated detection of cyst and tumors of the jaw in panoramic radiographs, J. Clin. Med., 9, 1, 10.3390/jcm9061839