A review of deep learning in dentistry

C.R. Janes, et al., Global health. A Companion to Medical Anthropology, 2022, 109–125. Tong, 2022, Current and future threats to human health in the anthropocene, Environ. Int., 158, 10.1016/j.envint.2021.106892 Cai, 2022, High animal protein diet and gut microbiota in human health, Crit. Rev. Food Sci. Nutr., 62, 6225, 10.1080/10408398.2021.1898336 Salmon, 2022, Defining health and reorganizing medicine, Alternative Med., 252, 10.4324/9781003294900-10 Goh, 2022, Impact of psychological states on periodontitis severity and oral health-related quality of life, J. Oral Sci., 64, 1, 10.2334/josnusd.21-0267 Perić, 2022, Treatment of gingivitis is associated with reduction of systemic inflammation and improvement of oral health-related quality of life: A randomized clinical trial, J. Clin. Periodontol., 49, 899, 10.1111/jcpe.13690 Huang, 2022, Oral health: The first step to sustainable development goal 3, J. Formos. Med. Assoc., 121, 1348, 10.1016/j.jfma.2021.10.018 Schmidt, 2022, Association of oral health conditions in adolescents with social factors and obesity, Int. J. Environ. Res. Public Health, 19, 2905, 10.3390/ijerph19052905 Schillinger, 2022, Integrating oral, physical, and mental health via public health literacy, HLRP: Health Literacy Res. Practice, 6, e17, 10.3928/24748307-20220105-01 Hajek, 2022, Oral health-related quality of life, probable depression and probable anxiety: Evidence from a representative survey in germany, BMC Oral Health, 22, 1 A. Eder, et al., Tooth wear: Future considerations. Tooth Wear: An Authoritative Reference for Dental Professionals and Students, 2022: p. 295. Loomans, 2022, A pragmatic philosophy of tooth wear management, 105 Tu, 2022, Development and influencing factors of oral microbiota in early life, Sichuan da xue xue bao Yi xue ban= J. Sichuan Univ. Med. Sci. Ed., 53, 220 Kitamoto, 2022, Untangling the oral–gut axis in the pathogenesis of intestinal inflammation, Int. Immunol., 34, 485, 10.1093/intimm/dxac027 An, 2022, An in vitro biofilm model system to facilitate study of microbial communities of the human oral cavity, Lett. Appl. Microbiol., 74, 302, 10.1111/lam.13618 Godovalov, 2022, Express detection of escherichia coli and bacteria of the escherichia coli group at the oral cavity, Klin. Lab. Diagn., 67, 177, 10.51620/0869-2084-2022-67-3-177-179 Kühnisch, 2022, Caries detection on intraoral images using artificial intelligence, J. Dent. Res., 101, 158, 10.1177/00220345211032524 Momeni-Moghaddam, 2022, Diagnostic accuracy, available treatment, and diagnostic methods of dental caries in practice: A meta-analysis, Beni-Suef Univ. J. Basic Appl. Sci., 11, 1, 10.1186/s43088-022-00243-x Zhang, 2022, Development and evaluation of deep learning for screening dental caries from oral photographs, Oral Dis., 28, 173, 10.1111/odi.13735 Orlandi, 2022, Impact of the treatment of periodontitis on systemic health and quality of life: A systematic review, J. Clin. Periodontol., 49, 314, 10.1111/jcpe.13554 Bao, 2022, Periodontitis may induce gut microbiota dysbiosis via salivary microbiota, Int. J. Oral Sci., 14, 1, 10.1038/s41368-022-00183-3 Galindo-Moreno, 2022, The impact of tooth loss on cognitive function, Clin. Oral Invest., 26, 3493, 10.1007/s00784-021-04318-4 Saydzai, 2022, Comparison of the efficacy of periodontal prognostic systems in predicting tooth loss, J. Clin. Periodontol., 49, 740, 10.1111/jcpe.13672 Nørgaard Petersen, 2022, Implant treatment after traumatic tooth loss: A systematic review, Dent. Traumatol., 38, 105, 10.1111/edt.12730 Ambikathanaya, 2022, Effect of acrylic removable partial denture in caries prevalence among diabetic and non-diabetic patients, J. Pharmacy Bioall. Sci., 14, 917, 10.4103/jpbs.jpbs_126_22 Ravoori, 2022, Oral hygiene practices and caries experience among school leaving children in rural area, J. Indian Assoc. Public Health Dentistry, 20, 379, 10.4103/jiaphd.jiaphd_53_21 Bright, 2022, Dual action nitric oxide and fluoride ion-releasing hydrogels for combating dental caries, ACS Appl. Mater. Interfaces, 14, 21916, 10.1021/acsami.2c02301 Zhang, 2022, Dual-sensitive antibacterial peptide nanoparticles prevent dental caries, Theranostics, 12, 4818, 10.7150/thno.73181 Di Stefano, 2022, Impact of oral microbiome in periodontal health and periodontitis: A critical review on prevention and treatment, Int. J. Mol. Sci., 23, 5142, 10.3390/ijms23095142 Shah, 2022, Pulp therapy and root canal treatment techniques in immature permanent teeth: An update, Br. Dent. J., 232, 524, 10.1038/s41415-022-4139-4 Ballikaya, 2022, The quality of root canal treatment and periapical status of permanent teeth in turkish children and teens: A retrospective cbct study, Oral Radiol., 38, 405, 10.1007/s11282-021-00570-2 Abramson, 2022, Ct-based modeling of the dentition for craniomaxillofacial surgical planning, J. Craniofacial Surgery, 33, 1574, 10.1097/SCS.0000000000008402 Albers, 2023, Early incisor lesions and equine odontoclastic tooth resorption and hypercementosis: reliability of radiographic findings, Equine Vet. J., 55, 261, 10.1111/evj.13577 Oda, 2022, Imaging characteristics of the gubernaculum tracts in successional teeth related to deciduous fused teeth on computed tomography, Congenit. Anom., 62, 241, 10.1111/cga.12493 Arora, 2023, Exploiting multimodal cnn architecture for automated teeth segmentation on dental panoramic x-ray images, Proc. Institution Mech. Eng. Part H-J. Eng. Med., 237, 395, 10.1177/09544119231157137 Mima, 2022, Tooth detection for each tooth type by application of faster r-cnns to divided analysis areas of dental panoramic x-ray images, Radiol. Phys. Technol., 15, 170, 10.1007/s12194-022-00659-1 Shajahan, 2022, Denoising of impulse noise using partition- supported median, interpolation and dwt in dental x-ray images, Int. J. Adv. Comput. Sci. Appl., 13, 274 Santipipat, 2023, Facilitating dental disease screening program in prisoners using an intraoral camera in teledentistry, BDJ OPEN, 9, 18, 10.1038/s41405-023-00145-9 Kassis, 2023, Triple scan evaluation of internal and marginal adaptation of overlays using different restorative materials, J. Esthet. Restor. Dent., 35, 493, 10.1111/jerd.12977 Stănuşi, 2023, Morphological and optical coherence tomography aspects of non-carious cervical lesions, J. Personalized Med., 13, 772, 10.3390/jpm13050772 Kaplan, 2023, Evaluation of optical coherence tomography findings and visual evoked potentials in charcot–marie–tooth disease, Int. Ophthalmol., 43, 333, 10.1007/s10792-022-02452-w Cao, 2022, Ai in finance: Challenges, techniques, and opportunities, ACM Comput. Surveys (CSUR), 55, 1, 10.1145/3502289 Cetinic, 2022, Understanding and creating art with ai: review and outlook, ACM Trans. Multimedia Comput., Commun., and Appl. (TOMM), 18, 1 Rajpurkar, 2022, Ai in health and medicine, Nat. Med., 28, 31, 10.1038/s41591-021-01614-0 Singer, 2023, Ai creates the message: Integrating ai language learning models into social work education and practice, J. Soc. Work. Educ., 59, 294, 10.1080/10437797.2023.2189878 Sharma, 2023, India's courts and artificial intelligence: A future outlook, LeXonomica, 15, 99, 10.18690/lexonomica.15.1.99-120.2023 J. Yang, Design of architectural decoration based on smart home system. in 2022 14th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). 2022. IEEE. p. 899–902. Yang, 2022, The development of smart home based on big data technology and computer aided design, 236 Mallinson, 2022, Smart home technology: challenges and opportunities for collaborative governance and policy research, Rev. Policy Res., 39, 330, 10.1111/ropr.12470 Zhu, 2022, A technical review on driverless vehicle technologies in smart cities, Big Data Privacy and Security in Smart Cities, 53, 10.1007/978-3-031-04424-3_3 Lu, 2022, Autonomous vision of driverless car in machine learning, 2113 Menon, 2022, Artificial intelligence-based trajectory planning for driverless vehicles—a review, Recent Adv. Hybrid and Electric Automotive Technol., 167, 10.1007/978-981-19-2091-2_14 Wang, 2022, Ai-based heterogenous large-scale english translation strategy, Mob. Inf. Syst., 2022 Hou, 2022, Research on translation corpus building with the assistance of ai, 136 Deng, 2022, Design of intelligent recognition english translation model based on improved machine translation algorithm, 233 Sharifani, 2023, Machine learning and deep learning: A review of methods and applications, World Informat. Technol. Eng. J., 10, 3897 Zhang, 2023, A review on machine unlearning, SN Computer Sci., 4, 337, 10.1007/s42979-023-01767-4 Grote, 2023, Uncertainty, evidence, and the integration of machine learning into medical practice, J. Med. Philos.: Forum Bioethics Philos. Med., 48, 84, 10.1093/jmp/jhac034 Liu, 2023, Deep learning based single sample face recognition: A survey, Artif. Intell. Rev., 56, 2723, 10.1007/s10462-022-10240-2 P Jayabharathi., et al. A comprehensive analysis on face recognition using various deep learning techniques. in 2022 International Conference on Communication, Computing and Internet of Things (IC3IoT). 2022. IEEE. p. 1-6. Hu, 2023, Overview of behavior recognition based on deep learning, Artif. Intell. Rev., 56, 1833, 10.1007/s10462-022-10210-8 Ferrari, 2022, Deep learning and model personalization in sensor-based human activity recognition, J. Reliable Intelligent Environ., 1 Chen, 2023, Chatgpt and other artificial intelligence applications speed up scientific writing, J. Chin. Med. Assoc., 86, 351, 10.1097/JCMA.0000000000000900 M.A. Di Gangi, Neural speech translation: From neural machine translation to direct speech translation. in Proceedings of the 23rd Annual Conference of the European Association for Machine Translation. 2022. p. 7-8. Celard, 2023, A survey on deep learning applied to medical images: From simple artificial neural networks to generative models, Neural Comput. Appl., 35, 2291, 10.1007/s00521-022-07953-4 Zhang, 2021, Midcan: A multiple input deep convolutional attention network for covid-19 diagnosis based on chest ct and chest x-ray, Pattern Recogn. Lett., 150, 8, 10.1016/j.patrec.2021.06.021 Cengiz, 2023, Comparison between random forest and support vector machine algorithms for lulc classification, Int. J. Eng. Geosci., 8, 1, 10.26833/ijeg.987605 O.M.A. Ali, et al. Evaluation of electrocardiogram signals classification using cnn, svm, and lstm algorithm: A review. in 2022 8th International Engineering Conference on Sustainable Technology and Development (IEC). 2022. IEEE. p. 185-191. Rani, 2023, Self-supervised learning: A succinct review, Arch. Comput. Meth. Eng., 30, 2761, 10.1007/s11831-023-09884-2 Cavasotto, 2023, The impact of supervised learning methods in ultralarge high-throughput docking, J. Chem. Inf. Model., 63, 2267, 10.1021/acs.jcim.2c01471 Bo'riboyevna, A.G.z., et al., Support vector machine algorithm. Central asian journal of mathematical theory and computer sciences, 2023. 4(5): p. 164–167. Palimkar, 2022, Machine learning technique to prognosis diabetes disease: Random forest classifier approach, 219 Solano, 2023, Solar irradiation forecasting using ensemble voting based on machine learning algorithms, Sustainability, 15, 7943, 10.3390/su15107943 Wang, 2023, Risk assessment of coronary heart disease based on cloud-random forest, Artif. Intell. Rev., 56, 203, 10.1007/s10462-022-10170-z McAlpine, 2021, The utility of unsupervised machine learning in anatomic pathology, Am. J. Clin. Pathol., 157, 5, 10.1093/ajcp/aqab085 Hassan, 2022, Supervised and weakly supervised deep learning models for covid-19 ct diagnosis: A systematic review, Comput. Methods Programs Biomed., 218, 10.1016/j.cmpb.2022.106731 Pinto, 2023, Review of cybersecurity analysis in smart distribution systems and future directions for using unsupervised learning methods for cyber detection, Energies, 16, 1651, 10.3390/en16041651 Crum, 2022, The use of cluster analysis in entrepreneurship research: Review of past research and future directions, J. Small Bus. Manag., 60, 961, 10.1080/00472778.2020.1748475 Eltouny, 2023, Unsupervised learning methods for data-driven vibration-based structural health monitoring: A review, Sensors, 23, 3290, 10.3390/s23063290 Alim-Marvasti, 2023, Hierarchical clustering of prolonged post-concussive symptoms after 12 months: Symptom-centric analysis and association with functional impairments, Brain Inj., 37, 317, 10.1080/02699052.2022.2158229 Yates, 2023, Assessing similarity in continuous seismic cross-correlation functions using hierarchical clustering: Application to ruapehu and piton de la fournaise volcanoes, Geophys. J. Int., 233, 472, 10.1093/gji/ggac469 Hao, 2022, Deep graph clustering with enhanced feature representations for community detection, Appl. Intell., 1 Bisen, 2023, A hybrid deep learning model using cnn and k-mean clustering for energy efficient modelling in mobile edgeiot, Electronics, 12, 1384, 10.3390/electronics12061384 Chakraborty, 2023, Conjunction of hard k-mean and fuzzy c-mean techniques in clustering and identifying some critical meteorological parameters for thunderstorm formation over a metro city of india during pre-monsoon season, J. Earth Syst. Sci., 132, 59, 10.1007/s12040-023-02059-4 De Rosa, 2022, The ratio-cut polytope and k-means clustering, SIAM J. Optim., 32, 173, 10.1137/20M1348601 Gwak, 2023, Hybrid integrated decision-making algorithm for clustering analysis based on a bipolar complex fuzzy and soft sets, Alex. Eng. J., 67, 473, 10.1016/j.aej.2022.12.003 Phamtoan, 2023, The fuzzy cluster analysis for interval value using genetic algorithm and its application in image recognition, Comput. Stat., 38, 25, 10.1007/s00180-022-01215-6 Phamtoan, 2022, The fuzzy cluster analysis for interval value using genetic algorithm and its application in image recognition, Comput. Stat. Facendola, 2023, Profiling citizens on perception of key factors of food security: An application of k-means cluster analysis, Sustainability, 15, 9915, 10.3390/su15139915 Uzcategui-Salazar, 2023, Assessment of social vulnerability to groundwater pollution using k-means cluster analysis, Environ. Sci. Pollut. Res., 30, 14975, 10.1007/s11356-022-22810-6 Hohmann, 2023, Quantifying ideological polarization on a network using generalized euclidean distance, Sci. Adv., 9, 10.1126/sciadv.abq2044 Yang, 2023, Euclidean distance-based adaptive collaborative representation with tikhonov regularization for hyperspectral image classification, Multimed. Tools Appl., 82, 5823, 10.1007/s11042-022-13597-2 Zhang, 2023, A multi-fault diagnosis method for lithium-ion battery pack using curvilinear manhattan distance evaluation and voltage difference analysis, J. Storage Mater., 67 Uyanik, 2023, A manhattan distance based hybrid recommendation system, Int. J. Appl. Mathem. Electr. Comp., 11, 20, 10.18100/ijamec.1232090 Adiga, 2023, Applications of deep learning and machine learning in computational medicine, J. Biochem. Technol., 14, 1, 10.51847/iW1DfVoXVw Li, 2023, A survey: evolutionary deep learning, Soft. Comput., 27, 9401, 10.1007/s00500-023-08316-4 Yu, 2023, Learning key steps to attack deep reinforcement learning agents, Mach. Learn., 112, 1499, 10.1007/s10994-023-06318-9 Zhao, 2023, Representing camera response function by a single latent variable and fully connected neural network, SIViP, 17, 1429, 10.1007/s11760-022-02351-8 Honcharenko, 2023, Information system based on multi-value classification of fully connected neural network for construction management, IAES Int. J. Artificial Intelligence, 12, 593 Li, 2023, Improved bathymetry in the south china sea from multisource gravity field elements using fully connected neural network, J. Marine Sci. Eng., 11, 1345, 10.3390/jmse11071345 Alrasheedi, 2023, Padding module: Learning the padding in deep neural networks, IEEE Access, 11, 7348, 10.1109/ACCESS.2023.3238315 M. Shahnaz, et al. On the performance of convolutional neural networks with resizing and padding. in Proceedings of International Conference on Frontiers in Computing and Systems. 2023. Springer. p. 51-62. Iqbal, 2023, On the analyses of medical images using traditional machine learning techniques and convolutional neural networks, Arch. Comput. Meth. Eng., 30, 3173, 10.1007/s11831-023-09899-9 Hwang, 2023, A universal activation function for deep learning, Computers, Mater. Continua, 75, 3553, 10.32604/cmc.2023.037028 Tao, 2022, Pooling <i>operations in deep</i> learning: From “invariable” to “variable”, Biomed Res. Int., 2022, 4067581, 10.1155/2022/4067581 Yadav, 2022, Survey on machine learning in speech emotion recognition and vision systems using a recurrent neural network (rnn), Arch. Comput. Meth. Eng., 29, 1753, 10.1007/s11831-021-09647-x Aldausari, 2022, Video generative adversarial networks: A review, ACM Comput. Surveys (CSUR), 55, 1, 10.1145/3487891 Ko, 2023, Superstargan: Generative adversarial networks for image-to-image translation in large-scale domains, Neural Netw., 162, 330, 10.1016/j.neunet.2023.02.042 Wang, 2023, Uav aerial image generation of crucial components of high-voltage transmission lines based on multi-level generative adversarial network, Remote Sens. (Basel), 15, 1412, 10.3390/rs15051412 Mao, 2023, An improved noise modeling method using a quasi-physical zone division model for algan/gan hemts, IEEE Trans. Electron Devices, 70, 1835, 10.1109/TED.2023.3249136 Im, 2023, Impact of fin width on low-frequency noise in algan/gan finfets: Evidence for bulk conduction, IEEE Access, 11, 10384, 10.1109/ACCESS.2023.3240409 Shi, 2023, A frequency-domain output-constrained active noise control algorithm based on an intuitive circulant convolutional penalty factor, IEEE/ACM Trans. Audio Speech Lang. Process., 31, 1318 Jiang, 2023, Analysis of epidemiological trends of and associated factors for tooth loss among 35-to 44-year-old adults in guangdong, southern china, 1995–2015: A population-based cross-sectional survey, BMC Oral Health, 23, 74, 10.1186/s12903-023-02776-8 Jabeen, 2023, Association of smoked and smokeless tobacco with tooth loss, Pakistan J. Med. Health Sci., 17 Yen, 2023, The performance of paralleling technique and bisecting angle technique for taking periapical radiographs: A systematic review, Dentistry J., 11, 155, 10.3390/dj11070155 Kusumawati, 2022, The accuracy of extraoral periapical radiography on determining the working length in endodontic treatment: Narrative review, Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI), 6, 41, 10.32793/jrdi.v6i1.770 Scott, 2023, Panoramic radiographs and dental patients with down syndrome: A scoping review, Spec. Care Dentist., 43, 199, 10.1111/scd.12762 Zhang, 2023, Children’s dental panoramic radiographs dataset for caries segmentation and dental disease detection, Sci. Data, 10, 380, 10.1038/s41597-023-02237-5 Chandrashekar, 2022, Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs, Comput. Biol. Med., 148, 10.1016/j.compbiomed.2022.105829 Baydar, 2023, The u-net approaches to evaluation of dental bite-wing radiographs: An artificial intelligence study, Diagnostics, 13, 453, 10.3390/diagnostics13030453 Diba, 2022, Artificial intelligence based on convolutional neural network for detecting dental caries on bitewing and periapical radiographs, Jurnal Radiologi Dentomaksilofasial Indonesia (JRDI), 6, 89, 10.32793/jrdi.v6i2.867 Wang, 2023, Artificial intelligence for visually impaired, Displays, 77, 10.1016/j.displa.2023.102391 Zhou, 2023, A survey of deep learning for alzheimer’s disease, Mach. Learn. Knowledge Extraction, 5, 611, 10.3390/make5020035 Bharadiya, 2023, A comprehensive survey of deep learning techniques natural language processing, Eur. J. Technol., 7, 58, 10.47672/ejt.1473 Wu, 2023, Graph neural networks for natural language processing: A survey, Found. Trends® Mach. Learn., 16, 119, 10.1561/2200000096 Li, 2023, Natural language processing applications for computer-aided diagnosis in oncology, Diagnostics, 13, 286, 10.3390/diagnostics13020286 Sharifani, 2023, Machine learning and deep learning: A review of methods and applications, World Inf. Technol. Eng. J., 10, 3897 Jiménez-Moreno, 2023, Deep learning speech recognition for residential assistant robot, IAES Int. J. Artificial Intelligence, 12, 585 Ghori, 2023, Acoustic modelling using deep learning for quran recitation assistance, Int. J. Speech Technol., 26, 113, 10.1007/s10772-022-09979-4 Vu, 2023, A deep learning based approach for context-aware multi-criteria recommender systems, Comput. Syst. Sci. Eng., 44, 471, 10.32604/csse.2023.025897 Zheng, 2023, Automl for deep recommender systems: A survey, ACM Trans. Inf. Syst., 41, 1, 10.1145/3545798 Imran, 2023, Refrs: Resource-efficient federated recommender system for dynamic and diversified user preferences, ACM Trans. Inf. Syst., 41, 1, 10.1145/3560486 Kang, 2023, Three-dimensional digital assessment of typodont activations, Orthod. Craniofac. Res., 26, 285, 10.1111/ocr.12611 Grad, 2023, Application of hololens-based augmented reality and three-dimensional printed anatomical tooth reference models in dental education, Anat. Sci. Educ., 16, 743, 10.1002/ase.2241 Kakehbaraei, 2023, 3d tooth segmentation in cone-beam computed tomography images using distance transform, Biomed. Signal Process. Control, 79, 10.1016/j.bspc.2022.104122 Xie, 2023, Wits: Weakly-supervised individual tooth segmentation model trained on box-level labels, Pattern Recogn., 133, 10.1016/j.patcog.2022.108974 Sheng, 2023, Transformer-based deep learning network for tooth segmentation on panoramic radiographs, J. Syst. Sci. Complex., 36, 257, 10.1007/s11424-022-2057-9 A. Haghanifar, et al. Automated teeth extraction from dental panoramic x-ray images using genetic algorithm. in 2020 IEEE International Symposium on Circuits and Systems (ISCAS). 2020. IEEE. p. 1-5. A. Bhat, et al. Prediction of covid 19 using chest x-ray images through cnn optimised using genetic algorithm. in 2022 2nd International Conference on Intelligent Technologies (CONIT). 2022. IEEE. p. 1-8. Zhang, 2022, A relation-based framework for effective teeth recognition on dental periapical x-rays, Comput. Med. Imaging Graph., 95, 10.1016/j.compmedimag.2021.102022 Hellİ, 2021, Tooth instance segmentation on panoramic dental radiographs using u-nets and morphological processing, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 10, 39, 10.29130/dubited.950568 Tao, 2022, Tooth ct image segmentation method based on the u-net network and attention module, Comput. Math. Methods Med., 2022, 1 Punn, 2022, Modality specific u-net variants for biomedical image segmentation: A survey, Artif. Intell. Rev., 1 Rohrer, 2022, Segmentation of dental restorations on panoramic radiographs using deep learning, Diagnostics (Basel), 12, 1316, 10.3390/diagnostics12061316 Wang, 2021, Multiclass cbct image segmentation for orthodontics with deep learning, J. Dent. Res., 100, 943, 10.1177/00220345211005338 Pelt, 2018, A mixed-scale dense convolutional neural network for image analysis, Proc. Natl. Acad. Sci., 115, 254, 10.1073/pnas.1715832114 Kim, 2020, Automatic tooth detection and numbering using a combination of a cnn and heuristic algorithm, Appl. Sci., 10, 5624, 10.3390/app10165624 Erciyas, 2023, Improving detection and classification of diabetic retinopathy using cuda and mask rcnn, SIViP, 17, 1265, 10.1007/s11760-022-02334-9 Sheng, 2023, Faster rcnn target detection algorithm integrating cbam and fpn, Appl. Sci., 13, 6913, 10.3390/app13126913 Hou, 2023, Detecting diseases in apple tree leaves using fpn–isresnet–faster rcnn, Eur. J. Remote Sensing, 56, 2186955, 10.1080/22797254.2023.2186955 Shaheen, 2021, A novel deep learning system for multi-class tooth segmentation and classification on cone beam computed tomography. A validation study, J. Dent., 115, 10.1016/j.jdent.2021.103865 Schneider, 2022, Benchmarking deep learning models for tooth structure segmentation, J. Dent. Res., 10.1177/00220345221100169 Park, 2022, A review and comparison of convolution neural network models under a unified framework, Commun. Statistical Appl. Methods, 29, 161, 10.29220/CSAM.2022.29.2.161 Salih, 2022, The local ternary pattern encoder–decoder neural network for dental image segmentation, IET Image Proc., 16, 1520, 10.1049/ipr2.12416 Salih, 2020, Skin lesion segmentation using local binary convolution-deconvolution architecture, Image Anal. Stereol., 39, 169, 10.5566/ias.2397 Al-Ghamdi, 2022, Detection of dental diseases through x-ray images using neural search architecture network, Comput. Intell. Neurosci., 2022, 3500552, 10.1155/2022/3500552 S. Sharma, et al. Deep learning models for image classification: Comparison and applications. in 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). 2022. IEEE. p. 1733-1738. Bhattacharjee, 2022, Automated dental cavity detection system using deep learning and explainable ai, 140 Cheng, 2022, Expert consensus on dental caries management, Int. J. Oral Sci., 14, 1, 10.1038/s41368-022-00167-3 Peng, 2022, Oral microbiota in human systematic diseases, Int. J. Oral Sci., 14, 1, 10.1038/s41368-022-00163-7 Al-Asmar, 2023, Clinical implementation of comprehending dentin on a microscopic level, Int. J. Morphol., 41, 195, 10.4067/S0717-95022023000100195 Murata, 2023, Human dentin materials for minimally invasive bone regeneration: Animal studies and clinical cases, J. Oral Biosci., 65, 13, 10.1016/j.job.2022.10.003 Zhu, 2022, Cariesnet: A deep learning approach for segmentation of multi-stage caries lesion from oral panoramic x-ray image, Neural Comput. Appl., 1 Lee, 2021, Deep learning for early dental caries detection in bitewing radiographs, Sci. Rep., 11, 16807, 10.1038/s41598-021-96368-7 Majanga, 2021, Automatic blob detection for dental caries, Appl. Sci., 11, 9232, 10.3390/app11199232 Rashid, 2022, A hybrid mask rcnn-based tool to localize dental cavities from real-time mixed photographic images, PeerJ Comput. Sci., 8, e888, 10.7717/peerj-cs.888 Kuhnisch, 2022, Caries detection on intraoral images using artificial intelligence, J. Dent. Res., 101, 158, 10.1177/00220345211032524 Hossen, 2022 Lin, 2022, Detecting proximal caries on periapical radiographs using convolutional neural networks with different training strategies on small datasets, Diagnostics (Basel), 12, 1047, 10.3390/diagnostics12051047 Ngnamsie Njimbouom, 2022, Mmdcp: Multi-modal dental caries prediction for decision support system using deep learning, Int. J. Environ. Res. Public Health, 19, 10928, 10.3390/ijerph191710928 Thanh, 2022, Deep learning application in dental caries detection using intraoral photos taken by smartphones, Appl. Sci., 12, 5504, 10.3390/app12115504 Andrade, 2023, Automatic dental biofilm detection based on deep learning, J. Clin. Periodontol., 50, 571, 10.1111/jcpe.13774 Ray, 2022, Periodontitis: An oral disease with severe consequences, Appl. Biochem. Biotechnol., 1 Zhang, 2022, Identification of endoplasmic reticulum stress-related biomarkers of periodontitis based on machine learning: A bioinformatics analysis, Dis. Markers, 2022, 1, 10.1155/2022/1720414 Tsuchida, 2023, Recent clinical treatment and basic research on the alveolar bone, Biomedicines, 11, 843, 10.3390/biomedicines11030843 Siregar, 2023, The role of periodontics in forensic odontology: A literature review, J. Int. Dental Med. Res., 16, 411 Abdouh, 2023, Papillon-lefèvre syndrome: Prevalence and characteristics in the kingdom of saudi arabia, EC Dental Science, 22, 96 Ustianowski, 2023, The role of vitamin c and vitamin d in the pathogenesis and therapy of periodontitis—narrative review, Int. J. Mol. Sci., 24, 6774, 10.3390/ijms24076774 Zhu, 2023, Periodontitis auxiliary diagnosis based on deep learning and oral dental x-ray images, Russ. J. Nondestr. Test., 59, 487, 10.1134/S1061830923600144 Paraguassu, 2023, Basic periodontitis manual: What it is, symptoms and treatments, Brazil. J. Implantol. Health Sci., 5, 01, 10.36557/2674-8169.2023v5n2p01-03 Kearney, 2022, A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level, J. Dent., 123, 10.1016/j.jdent.2022.104211 Moran, 2021, Do radiographic assessments of periodontal bone loss improve with deep learning methods for enhanced image resolution?, Sensors, 21, 10.3390/s21062013 Ossowska, 2022, Evaluation of the progression of periodontitis with the use of neural networks. Journal of, Clin. Med., 11 Bayrakdar, 2022, A u-net approach to apical lesion segmentation on panoramic radiographs, Biomed Res. Int., 2022, 1, 10.1155/2022/7035367 Shon, 2022, Deep learning model for classifying periodontitis stages on dental panoramic radiography, Appl. Sci., 12, 8500, 10.3390/app12178500 Danks, 2021, Automating periodontal bone loss measurement via dental landmark localisation, Int. J. Comput. Assist. Radiol. Surg., 16, 1189, 10.1007/s11548-021-02431-z Alotaibi, 2022, Artificial intelligence (ai) diagnostic tools: Utilizing a convolutional neural network (cnn) to assess periodontal bone level radiographically-a retrospective study, BMC Oral Health, 22, 399, 10.1186/s12903-022-02436-3 Tsoromokos, 2022, Estimation of alveolar bone loss in periodontitis using machine learning, Int. Dent. J., 72, 621, 10.1016/j.identj.2022.02.009 Chang, 2022, Application of deep machine learning for the radiographic diagnosis of periodontitis, Clin. Oral Invest., 1 Jiang, 2022, A two-stage deep learning architecture for radiographic staging of periodontal bone loss, BMC Oral Health, 22, 1, 10.1186/s12903-022-02119-z Zhu, 2023, Automatic detection and classification of dead nematode-infested pine wood in stages based on yolo v4 and googlenet, Forests, 14, 601, 10.3390/f14030601 Muwafaq, 2023, Asd automatic detection by using yolo v3 and yolo v4 method, J. Emerg. Computer Technol., 2, 48 Xu, 2022, Evaluation of the effect of comprehensive nursing interventions on plaque control in patients with periodontal disease in the context of artificial intelligence, J. Healthcare Eng., 2022, 1 Huang, 2023, Additive manufacturing technologies in the oral implant clinic: A review of current applications and progress, Front. Bioeng. Biotechnol., 11, 1100155, 10.3389/fbioe.2023.1100155 Fiorillo, 2023, Bioengineering applied to oral implantology, a new protocol:“Digital guided surgery”, Prosthesis, 5, 234, 10.3390/prosthesis5010018 Thomas, 2023, Temporomandibular disorders: Implications in restorative dentistry and orthodontics, Dental Clinics, 67, 309 Shinya, 2023, Dental material research in prosthodontics—towards developing better and efficient biomimetic materials, J. Prosthodont. Res., 67, vi, 10.2186/jpr.JPR_D_23_00081 Alshadidi, 2023, Investigation on the application of artificial intelligence in prosthodontics, Appl. Sci., 13, 5004, 10.3390/app13085004 Nagarkar, 2023, Evidence-based fact checking for selective procedures in restorative dentistry, Clin. Oral Invest., 27, 475, 10.1007/s00784-022-04832-z Swanson, 2023, Digital restorative workflows for developmental dental defects in young patients, J. Am. Dent. Assoc., 154, 340, 10.1016/j.adaj.2022.11.014 Kong, 2023, Identification of 130 dental implant types using ensemble deep learning, Int. J. Oral Maxillofac. Implants, 38, 150, 10.11607/jomi.9818 Kwok, 2023, Dental implant prognostication: A commentary, J. Periodontol., 94, 713, 10.1002/JPER.22-0196 Xu, 2023, Calcium phosphate ceramics and synergistic bioactive agents for osteogenesis in implant dentistry, Tissue Eng. Part C Methods, 29, 197, 10.1089/ten.tec.2023.0042 Pan, 2023, The identity of the freshwater crab aiyunamon tumidum (wood-mason, 1871)(brachyura, potamidae), with description of a new species from china, Crustaceana, 96, 179, 10.1163/15685403-bja10276 Zhou, 2023, Three dimensional quantitative study of soft tissue changes in nasolabial folds after orthodontic treatment in female adults, BMC Oral Health, 23, 31, 10.1186/s12903-023-02733-5 Jing, 2022, Analysis and optimization of a novel flux reversal machine with auxiliary teeth, Energies, 15, 8906, 10.3390/en15238906 Ragodos, 2022, Dental anomaly detection using intraoral photos via deep learning, Sci. Rep., 12, 11577, 10.1038/s41598-022-15788-1 Sunnetci, 2023, Comparative parotid gland segmentation by using resnet-18 and mobilenetv2 based deeplab v3+ architectures from magnetic resonance images, Concurren. Comput.: Practice Experience, 35, e7405, 10.1002/cpe.7405 El-Feshawy, 2023, Iot framework for brain tumor detection based on optimized modified resnet 18 (omres), J. Supercomput., 79, 1081, 10.1007/s11227-022-04678-y Lee, 2022, Accuracy of deep learning-based integrated tooth models by merging intraoral scans and cbct scans for 3d evaluation of root position during orthodontic treatment, Prog. Orthod., 23, 15, 10.1186/s40510-022-00410-x Madi, 2023, Cone-beam computed tomography and histological findings for socket preservation techniques using different grafting materials: A systematic review, J. Funct. Biomater., 14, 282, 10.3390/jfb14050282 Choi, 2023, Deep learning synthesis of cone-beam computed tomography from zero echo time magnetic resonance imaging, Sci. Rep., 13, 6031, 10.1038/s41598-023-33288-8 Okazaki, 2022, Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs, Dent. Mater. J., 10.4012/dmj.2022-098 Li, 2022, Artificial intelligence for classifying and archiving orthodontic images, Biomed Res. Int., 2022, 1473977 Basaran, 2022, Diagnostic charting of panoramic radiography using deep-learning artificial intelligence system, Oral Radiol., 38, 363, 10.1007/s11282-021-00572-0 Liu, 2022, A pilot study of a deep learning approach to detect marginal bone loss around implants, BMC Oral Health, 22, 11, 10.1186/s12903-021-02035-8 Takahashi, 2021, Deep learning-based detection of dental prostheses and restorations, Sci. Rep., 11, 1960, 10.1038/s41598-021-81202-x Guo, 2022, Tvgg dental implant identification system, Front. Pharmacol., 13 Nijaguna, 2023, Quantum fruit fly algorithm and resnet50-vgg16 for medical diagnosis, Appl. Soft Comput., 136, 10.1016/j.asoc.2023.110055 Alkurdy, 2023, Ultrasound renal stone diagnosis based on convolutional neural network and vgg16 features, Int. J. Electr. Comput. Eng., 13, 3440 Sukegawa, 2021, Multi-task deep learning model for classification of dental implant brand and treatment stage using dental panoramic radiograph images, Biomolecules, 11, 14, 10.3390/biom11060815 Setzer, 2020, Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images, J. Endod., 46, 987, 10.1016/j.joen.2020.03.025 Ayidh Alqahtani, 2023, Deep convolutional neural network-based automated segmentation and classification of teeth with orthodontic brackets on cone-beam computed-tomographic images: A validation study, Eur. J. Orthod., 45, 169, 10.1093/ejo/cjac047 Koziel, 2022, Pros and cons of causative association between periodontitis and rheumatoid arthritis, Periodontology, 89, 83, 10.1111/prd.12432 Ma, 2022, Dementia and the risk of periodontitis: A population-based cohort study, J. Dent. Res., 101, 270, 10.1177/00220345211037220 Ma, 2022, Fibromyalgia and periodontitis: Bidirectional associations in population-based 15-year retrospective cohorts, J. Periodontol., 93, 877, 10.1002/JPER.21-0256 Parsegian, 2022, Association between periodontitis and chronic kidney disease, Periodontology, 89, 114, 10.1111/prd.12431 Pussinen, 2022, Periodontitis and cardiometabolic disorders: The role of lipopolysaccharide and endotoxemia, Periodontology, 89, 19, 10.1111/prd.12433 Li, 2021, A tongue features fusion approach to predicting prediabetes and diabetes with machine learning, J. Biomed. Inform., 115, 10.1016/j.jbi.2021.103693 Gheorghe, 2022, Periodontitis, metabolic and gastrointestinal tract diseases: Current perspectives on possible pathogenic connections, J. Personalized Med., 12, 341, 10.3390/jpm12030341 Borsa, 2021, Analysis the link between periodontal diseases and alzheimer’s disease: A systematic review, Int. J. Environ. Res. Public Health, 18, 9312, 10.3390/ijerph18179312 Ngamdu, 2022, Association between periodontal disease and cardiovascular disease, Am. J. Cardiol., 178, 163, 10.1016/j.amjcard.2022.05.028 R.R. Devi K., et al. Cloud-based cvd identification for periodontal disease, in Machine learning and autonomous systems. 2022, Springer. p. 591-607. Mahapatra, 2023, Graph node based interpretability guided sample selection for active learning, IEEE Trans. Med. Imaging, 42, 661, 10.1109/TMI.2022.3215017 Naseem, 2023, Vision-language transformer for interpretable pathology visual question answering, IEEE J. Biomed. Health Informatics, 27, 1681, 10.1109/JBHI.2022.3163751 Song, 2023, Diagnosis of glioblastoma multiforme progression via interpretable structure-constrained graph neural networks, IEEE Trans. Med. Imaging, 42, 380, 10.1109/TMI.2022.3202037 Liu, 2023, An interpretable cnn for the segmentation of the left ventricle in cardiac mri by real-time visualization, CMES-Computer Model. Eng. Sci., 135, 1571 Su, 2023, Performance of statistical and machine learning risk prediction models for surveillance benefits and failures in breast cancer survivors, Cancer Epidemiol. Biomark. Prev., 32, 561, 10.1158/1055-9965.EPI-22-0677 Tong, 2023, Fmri-based brain disease diagnosis: A graph network approach, IEEE Trans. Med. Robotics Bionics, 5, 312, 10.1109/TMRB.2023.3270481 Cabrera, A.A., et al., What did my ai learn? How data scientists make sense of model behavior. ACM Trans. Computer-Human Interact., 2023. 30(1): p. 1-27: Article ID. 1. Sqalli, M.T., et al., Humanizing ai in medical training: Ethical framework for responsible design. Front. Artificial Intelligence, 2023. 6: p. 1-6: Article ID. 1189914. Ahmad, 2023, Equity and artificial intelligence in surgical care: A comprehensive review of current challenges and promising solutions, BULLET: J. Multidisiplin Ilmu, 2, 443 Gaskins, 2023, Interrogating algorithmic bias: From speculative fiction to liberatory design, TechTrends, 67, 417, 10.1007/s11528-022-00783-0