Skip to main content
SpringerLink
Log in

Artificial Intelligence and Big Data in Dentistry

  • Chapter
  • First Online:
Digitization in Dentistry

Abstract

Digitization has become important for practicing contemporary dentistry with the probability of most of the procedures being based on the digital techniques in near future. From taking x-rays or photographs, making impressions, recording jaw movements, educating and training new dentists, or patient motivation for practice buildup, all have become digital.

But with the technology changing so fast, this poses a great challenge. There is endless scope of digitization and technology in dentistry, be in the clinical and laboratory procedures like use of CAD-CAM technology, stereolithography, rapid prototyping, use of virtual articulators and digital face bows, digital radiographs, or in the field of training, education, and research by the use of virtual patient programs, dental software, audiovisual aids, etc. This chapter deals with the recent introduction of artificial intelligence and Big Data consisting of digitized patient records, and the data stored on computers (and then later on mobile devices and in the cloud and so on), along with the ability to share and manipulate all kinds of data, anywhere in the world, at the touch of a screen.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Lynch C. How do your data grow? Nature. 2008;455:28–9.

    Article  PubMed  Google Scholar 

  2. Boyd D, Crawford K. Critical questions for big data. Info Commun Soc. 2012;15:662–79.

    Article  Google Scholar 

  3. Joda T, Waltimo T, Pauli-Magnus C, Probst-Hensch N, Zitzmann NU. Population-based linkage of big data in dental research. Int J Environ Res Public Health. 2018;15:2357.

    Article  PubMed Central  Google Scholar 

  4. Hogan R, Goodwin M, Boothman N, Iafolla T, Pretty IA. Further opportunities for digital imaging in dental epidemiology. J Dent. 2018;74:S2–9.

    Article  PubMed  Google Scholar 

  5. Joda T, Waltimo T, Probst-Hensch N, Pauli-Magnus C, Zitzmann NU. Health data in dentistry: an attempt to master the digital challenge. Public Health Genomics. 2019;22:1–7.

    Article  PubMed  Google Scholar 

  6. Vandenberghe B. The digital patient—imaging science in dentistry. J Dent. 2018;74:S21–6.

    Article  PubMed  Google Scholar 

  7. Joda T, Wolfart S, Reich S, Zitzmann NU. Virtual dental patient: how long until It’s here? Curr Oral Health Rep. 2018;5:116–20.

    Article  Google Scholar 

  8. Nilsen WJ, Kumar S, Shar A, Varoquiers C, Wiley T, Riley WT, Pavel M, Atienza AA. Advancing the science of mHealth. J Health Commun. 2012;17:5–10.

    Article  PubMed  Google Scholar 

  9. Brodt ED, Skelly AC, Dettori JR, Hashimoto RE. Administrative database studies: goldmine or goose chase? Evid Based Spine Care J. 2014;5:74–6.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Liang H, Tsui BY, Ni H, Valentim CCS, Baxter SL, Liu G, Cai W, Kermany DS, Sun X, Chen J, et al. Evaluation and accurate diagnoses of paediatric diseases using artificial intelligence. Nat Med. 2019;25:433–8.

    Article  PubMed  Google Scholar 

  11. Joda T, Bornstein MM, Jung RE, Ferrari M, Waltimo T, Zitzmann NU. Recent trends and future direction of dental research in the digital era. Int J Environ Res Public Health. 2020;17:1987.

    Article  PubMed Central  Google Scholar 

  12. Wataha JC, Messer RL. Casting alloys. Dent Clin N Am. 2004;48:vii–viii. 499-512

    Article  PubMed  Google Scholar 

  13. Winder J, Bibb R. Medical rapid prototyping technologies: state of the art and current limitations for application in oral and maxillofacial surgery. J Oral Maxillofac Surg. 2005;63:1006–15.

    Article  PubMed  Google Scholar 

  14. Joda T, Ferrari M, Gallucci GO, Wittenben J-G, Bragger U. Digital technology in fixed implant prosthodontics. Periodontol. 2017;73:178–92.

    Article  Google Scholar 

  15. Azari A, Nikzad S. The evolution of rapid prototyping in dentistry: a review. Rapid Prototyp J. 2009;15:216–25.

    Article  Google Scholar 

  16. Dawood A, Marti B, Sauret-Jackson V, Darwood A. 3D printing in dentistry. Br Dent J. 2015;219:521–9.

    Article  PubMed  Google Scholar 

  17. Lech G, Nordström E. Dimensional stability of 3D printed dental models. Master’s Thesis, Malmö University Electronic Publishing, Malmö, Sweden 2018.

    Google Scholar 

  18. Galantea R, Figueiredo-Pinaa CG, Serro AP. Additive manufacturing of ceramics for dental applications: a review. Dent Mater. 2019;35:825–46.

    Article  Google Scholar 

  19. Zocca A, Colombo P, Gomes CM, Gunster J. Additive manufacturing of ceramics: issues, potentialities, and opportunities. J Am Ceram Soc. 2015;98:1983–2001.

    Article  Google Scholar 

  20. Bose S, Ke D, Sahasrabudhe H, Bandyopadhyay A. Additive manufacturing of biomaterials. Prog Mater Sci. 2018;93:45–111.

    Article  PubMed  Google Scholar 

  21. Shetty V, Yamamoto J, Yale K. Re-architecting oral healthcare for the 21st century. J Dent. 2018;74(Suppl 1):S10–4. https://doi.org/10.1016/j.jdent.2018.04.017.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Lee CH, Yoon HJ. Medical big data: promise and challenges. Kidney Res Clin Pract. 2017;36(1):3–11.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Laney D. 3D data management: controlling data volume, velocity, and variety, application delivery strategies. Stamford: META Group Inc; 2001.

    Google Scholar 

  24. Mauro AD, Greco M, Grimaldi M. A formal definition of big data based on its essential features. Libr Rev. 2016;65(3):122–35.

    Article  Google Scholar 

  25. McAfee A, Brynjolfsson E. Big data: the management revolution. Harv Bus Rev. 2012;90:60–6. 68, 128

    PubMed  Google Scholar 

  26. https://allofus.nih.gov/.

  27. Boden DF. What guidance is there for ethical records transfer and fee charges? J Am Dent Assoc. 2008;139:197–8.

    Article  PubMed  Google Scholar 

  28. Cederberg RA, Valenza JA. Ethics and the electronic health record in dental school clinics. J Dent Educ. 2012;76:584–9.

    Article  PubMed  Google Scholar 

  29. Szekely DG, Milam S, AKhademi J. Legal issues of the electronic dental record: security and confidentiality. J Dent Educ. 1996;60:19–23.

    Article  PubMed  Google Scholar 

  30. Ioannidis JP. Informed consent, big data, and the oxymoron of research that is not research. Am J Bioeth. 2013;13:40–2.

    Article  PubMed  Google Scholar 

  31. Martani A, Geneviève LD, Pauli-Magnus C, McLennan S, Elger BS. Regulating the secondary use of data for research: arguments against genetic exceptionalism. Front Genet. 2019;10:1254.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cederberg R, Walji M, Valenza J. Electronic health records in dentistry: clinical challenges and ethical issues. Cham, Switzerland: Springer Science and Business Media LLC; 2014. p. 1–12.

    Google Scholar 

  33. Benitez K, Malin B. Evaluating re-identification risks with respect to the HIPAA privacy rule. J Am Med Inform Assoc. 2010;17(2):169–77.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Kelman CW, Bass AJ, Holman CD. Research use of linked health data—a best practice protocol. Aust N Z J Public Health. 2002;26(3):251–5.

    Article  PubMed  Google Scholar 

  35. Cunningham J, Ainsworth J. Enabling patient control of personal electronic health records through distributed ledger technology. Stud Health Technol Inform. 2017;245:45–8.

    PubMed  Google Scholar 

  36. Till BM, Peters AW, Afshar S, Meara J. From blockchain technology to global health equity: can cryptocurrencies finance universal health coverage? BMJ Glob Health. 2017;2(4):e000570.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Angraal S, Krumholz HM, Schulz WL. Blockchain technology: applications in health care. Circ Cardiovasc Qual Outcomes. 2017;10(9):e003800.

    Article  PubMed  Google Scholar 

  38. Belle A, et al. Big data analytics in healthcare. Biomed Res Int. 2015;2015:370194.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Garcia I, Kuska R, Somerman MJ. Expanding the foundation for personalized medicine: implications and challenges for dentistry. J Dent Res. 2013;92:3–10.

    Article  Google Scholar 

  40. Marrazzo P, Paduano F, Palmieri F, Marrelli M, Tatullo M. Highly efficient in vitro reparative behaviour of dental pulp stem cells cultured with standardized platelet lysate. Stem Cells Int. 2016;2016:7230987.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Di Sanzo M, Borro M, La Russa R, Cipolloni L, Santurro A, Scopetti M, Simmaco M, Frati P. Clinical applications of personalized medicine: a new paradigm and challenge. Curr Pharm Biotechnol. 2017;18:194–203.

    Article  PubMed  Google Scholar 

  42. Jorm L. Routinely collected data as a strategic resource for research: priorities for methods and workforce. Public Health Res Pract. 2015;25:e2541540.

    Article  PubMed  Google Scholar 

  43. Zijlstra-Shaw S, Stokes CW. Learning analytics and dental education; choices and challenges. Eur J Dent Educ. 2018;22:e658–60.

    Article  PubMed  Google Scholar 

  44. Day PF, Petherick E, Godson J, Owen J, Douglas G. A feasibility study to explore the governance processes required for linkage between dental epidemiological, and birth cohort, data in the UK. Community Dent Health. 2018;35:228–34.

    Google Scholar 

  45. Eng G, Chen A, Vess T, Ginsburg GS. Genome technologies and personalized dental medicine. Oral Dis. 2011;18:223–35.

    Article  PubMed  Google Scholar 

  46. Glick M. Taking a byte out of big data. J Am Dent Assoc. 2015;146:793–4.

    Article  PubMed  Google Scholar 

  47. Aldridge RW, Shaji K, Hayward AC, Abubakar I. Accuracy of probabilistic linkage using the enhanced matching system for public health and epidemiological studies. PLoS One. 2015;10:e0136179.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Wang S, Parsons M, Stone-McLean J, Rogers P, Boyd S, Hoover K, Meruvis-Pastor O, Gong M, Smith A. Augmented reality as a telemedicine platform for remote procedural training. Sensors. 2017;17:2294.

    Article  PubMed Central  Google Scholar 

  49. Jampani ND, Nutalapati R, Dontula BS, Boyapati R. Applications of teledentistry: a literature review and update. J Int Soc Prev Community Dent. 2011;1:37–44.

    Article  PubMed  PubMed Central  Google Scholar 

  50. Estai M, Kruger E, Tennant M, Bunt S, Kanagasingam Y. Challenges in the uptake of telemedicine in dentistry. Rural Remote Health. 2016;16:3915.

    PubMed  Google Scholar 

  51. Kumar S, Nilsen W, Pavel M, Srivastava M. Mobile health: revolutionizing healthcare through transdisciplinary research. Computer. 2013;46:28–35.

    Article  Google Scholar 

  52. Nilsen W, Kumar S, Shar A, Varoquiers C, Wiley T, Riley WT, Pavel M, Atienza AA. Advancing the science of mHealth. J Health Commun. 2012;17:5–10. https://doi.org/10.1080/10810730.2012.677394.

    Article  PubMed  Google Scholar 

  53. Weinstein P, Milgrom P, Melnick S, Beach B, Spadafora A. How effective is oral hygiene instruction? Results after 6 and 24 weeks. J Public Health Dent. 1989;49:32–8. https://doi.org/10.1111/j.1752-7325.1989.tb02017.x.

    Article  PubMed  Google Scholar 

  54. Wainwright J, Sheiham A. An analysis of methods of toothbrushing recommended by dental associations, toothpaste and toothbrush companies and in dental texts. Br Dent J. 2014;217:E5. https://doi.org/10.1038/sj.bdj.2014.651.

    Article  PubMed  Google Scholar 

  55. Da Costa ALP, Silva AA, Pereira CB. Tele-orthodontics: tool aid to clinical practice and continuing education. Dental Press J Orthod Rev. 2012;16:15–21.

    Article  Google Scholar 

  56. Cvrkel T. The ethics of mHealth: moving forward. J Dent. 2018;74:S15–20.

    Article  PubMed  Google Scholar 

  57. Nutalapati R, Boyapati R, Jampani ND, Dontula BSK. Applications of teledentistry: a literature review and update. J Int Soc Prev Community Dent. 2011;1:37–44.

    Article  PubMed  PubMed Central  Google Scholar 

  58. World Health Organization. From innovation to implementation, eHealth in the WHO European Region. 2016.

    Google Scholar 

  59. Deshmukh SV. Artificial intelligence in dentistry. J Int Clin Dent Res Organ. 2018;10:47–8.

    Article  Google Scholar 

  60. Park WJ, Park JB. History and application of artificial neural networks in dentistry. Eur J Dent. 2018;12:594–601.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Rajaraman V. John McCarthy father of artificial intelligence. Reson. 2014:198e207.

    Google Scholar 

  62. Ramesh AN, Kambhampati C, Monson JRT, Drew PJ. Artificial intelligence in medicine. Ann R Coll Surg Engl. 2004;86:334–8.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Lee JH, Kim DH, Jeong SN, Choi SH. Diagnosis and prediction of periodontally compromised teeth using a deep learning- based convolutional neural network algorithm. J Periodontal Implant Sci. 2018;48:114–23.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Lee JH, Kim DH, Jeong SN, Choi SH. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm. J Dent. 2018;77:106–11.

    Article  PubMed  Google Scholar 

  65. Vinayahalingam S, Xi T, Bergé S, Maal T, de Jong G. Automated detection of third molars and mandibular nerve by deep learning. Sci Rep. 2019;9:9007.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Hiraiwa T, Ariji Y, Fukuda M, Kise Y, Nakata K, Katsumata A, et al. A deep-learning artificial intelligence system for assessment of root morphology of the mandibular first molar on panoramic radiography. Dentomaxillofac Radiol. 2019;48:20180218.

    Article  PubMed  Google Scholar 

  67. Tuzoff DV, Tuzova LN, Bornstein MM, Krasnov AS, Kharchenko MA, Nikolenko SI, et al. Tooth detection and numbering in panoramic radiographs using convolutional neural networks. Dentomaxillofac Radiol. 2019;48:20180051.

    Article  PubMed  PubMed Central  Google Scholar 

  68. Krois J, Ekert T, Meinhold L, Golla T, Kharbot B, Wittemeier A, et al. Deep learning for the radiographic detection of periodontal bone loss. Sci Rep. 2019;8:8995.

    Google Scholar 

  69. Ariji Y, Fukuda M, Kise Y, Nozawa M, Yanashita Y, Fujita H, et al. Contrast-enhanced computed tomography image assessment of cervical lymph node metastasis in patients with oral cancer by using a deep learning system of artificial intelligence. Oral Surg Oral Med Oral Pathol Oral Radiol. 2019;127:458–63.

    Article  PubMed  Google Scholar 

  70. Kim DW, Lee S, Kwon S, Nam W, Cha IH, Kim HJ. Deep learning-based survival prediction of oral cancer patients. Sci Rep. 2019;9:6994.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Das S, Dey A, Pal A, Roy N. Applications of artificial intelligence in machine learning: review and prospect. Int J Comput Appl. 2015;115:31–41.

    Google Scholar 

  72. Khanna SS, Dhaimade PA. Artificial intelligence: transforming dentistry today. Indian J Basic Appl Med Res. 2017;6:161–7.

    Google Scholar 

  73. Hwang JJ, Sergei A, Efros AA, Yu SX. Learning Beyond Human Expertise with Generative Models for Dental Restoration. CoRR abs/1804.00064; 2018.

    Google Scholar 

  74. Akst J. A primer: artificial intelligence versus neural networks. Inspiring Innovation: The Scientist Exploring Life, 2019: 65802.

    Google Scholar 

  75. Hopfield JJ. Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci U S A. 1982;79:2554–8.

    Article  PubMed  PubMed Central  Google Scholar 

  76. Doi K. Computer aided diagnosis in medical imaging: historical review, current status, and future potential. Comput Med Imaging Graph. 2007;31:198–211.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Dar-Odeh NS, Alsmadi OM, Bakri F, Abu-Hammour Z, Shehabi AA, Al-Omiri MK, et al. Predicting recurrent aphthous ulceration using genetic algorithms-optimized neural networks. Adv Appl Bioinforma Chem. 2010;3:7–13.

    PubMed Central  Google Scholar 

  78. Majumdar B, Saroda SC, Saroda GS, Patil S. Technology: artificial intelligence. BDJ. 2018;224:916.

    Article  PubMed  Google Scholar 

  79. Moghimi S, Talebi M, Parisay I. Design and implementation of a hybrid genetic algorithm and artificial neural network system for predicting the sizes of unerupted canines and premolars. Eur J Orthod. 2011;34:480–6.

    Article  PubMed  Google Scholar 

  80. Al Haidan A, Abu-Hammad O, Dar-Odeh N. Predicting tooth surface loss using genetic algorithms-optimized artificial neural networks. Comput Math Methods Med. 2014;2014:1–7.

    Article  Google Scholar 

  81. Mario MC, Abe JM, Ortega NR. Paraconsistent artificial neural network as auxiliary in cephalometric diagnosis. Artif Organs. 2010;34:E215–21.

    Article  PubMed  Google Scholar 

  82. Williams JS, Matthewman A, Brown D. An orthodontic expert system. Fuzzy Sets Syst. 1989;30:121–33.

    Article  Google Scholar 

  83. Chen YC, Hong DJ, Wu CW, Mupparapu M. The use of deep convolutional neural networks in biomedical imaging: a review. J Orofac Sci. 2019;11:3–10.

    Article  Google Scholar 

  84. Kunz F, Stellzig-Eisenhauer A, Zeman F, Boldt J. Artificial intelligence in orthodontics: evaluation of a fully automated cephalometric analysis using a customized convolutional neural network. J Orofac Orthop. 2020;81:52e68.

    Article  Google Scholar 

  85. Hwang HW, Park JH, Moon JH, et al. Automated identification of cephalometric landmarks: part 2-might it be better than human? Angle Orthod. 2020;90:69e76.

    Article  Google Scholar 

  86. Choi HI, Jung SK, Baek SH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg. 2019;30:1986e9.

    Article  Google Scholar 

  87. Khanna S. Artificial intelligence: contemporary applications and future compass. Int Dent J. 2010;60:269–72.

    PubMed  Google Scholar 

  88. Alexander B, John S. Artificial intelligence in dentistry: current concepts and a peep into the future. Int J Adv Res. 2018;6:1105–8.

    Article  Google Scholar 

  89. Papantonopoulos G, Takahashi K, Bountis T, Loos BG. Artificial neural networks for the diagnosis of aggressive periodontitis trained by immunologic parameters. PLoS One. 2014;9:e89757.

    Article  PubMed  PubMed Central  Google Scholar 

  90. Yauney G, Rana A, Wong LC, Javia P, Muftu A, Shah P. Automated process incorporating machine learning segmentation and correlation of oral diseases with systemic health. EMBC 2019:3387e93.

    Google Scholar 

  91. Johari M, Esmaeili F, Andalib A, Garjani S, Saberkari H. Detection of vertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an ex vivo study. Dentomaxillofac Radiol. 2017;46:20160107.

    Article  PubMed  PubMed Central  Google Scholar 

  92. Saghiri MA, Asgar K, Boukani KK, et al. A new approach for locating the minor apical foramen using an artificial neural network. Int Endod J. 2012;45:257e65.

    Article  Google Scholar 

  93. Saghiri MA, Garcia-Godoy F, Gutmann JL, Lotfi M, Asgar K. The reliability of artificial neural network in locating minor apical foramen: a cadaver study. J Endod. 2012;38:1130e4.

    Article  Google Scholar 

  94. Brickley MR, Shepherd JP, Armstrong RA. Neural networks: a new technique for development of decision support systems in dentistry. J Dent. 1998;26:305–9.

    Article  PubMed  Google Scholar 

  95. Khanagar SB, et al. Developments, application, and performance of artificial intelligence in dentistry- a systematic review. J Dent Sci. https://doi.org/10.1016/j.jds.2020.06.019.

  96. Schwendicke F, Golla T, Dreher M, Krois J. Convolutional neural networks for dental image diagnostics: a scoping review. J Dent. 2019;91:103226.

    Article  PubMed  Google Scholar 

  97. Gross D, Gross K, Wilhelmy S. Digitalization in dentistry: ethical challenges and implications. Quintessence International 2019. General Dentistry. Volume 50; number 10.

    Google Scholar 

  98. Leake N, Baxter T. Big data: storage, sharing and usage. In: Rekow D, editor. Digital dentistry. A comprehensive reference and preview of the future. Berlin: Quintessence Publishing; 2018. p. 335–53.

    Google Scholar 

  99. Goertz D. Digital practice: curse or blessing [in German]? DZW Spec. 2016;1:38–9.

    Google Scholar 

  100. Schweikardt C, Gross D. Technologized medicine- dehumanized medicine? Introductory thoughts [in German]. In: Brukamp K, Laryionava K, Schweikardt C, Gross D, editors. Technologized medicine- dehumanized medicine? Ethical, legal and social aspects of new medical technologies [in German]. Kassel: Kassel University Press; 2011. p. 7–11.

    Google Scholar 

  101. Kumar S, editor. Teledentistry. Cham: Springer; 2015.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Professor, Department of Endodontics, National University College of Dentistry, Manila, Philippines

    Priyanka Jain

  2. University of Dundee, Dundee, Ireland

    Catherine Wynne

Authors
  1. Priyanka Jain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Catherine Wynne
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Endodontics, National University College of Dentistry, Manila, Philippines

    Priyanka Jain

  2. Department of Prosthodontics, Indraprastha Dental College, Ghaziabad, Uttar Pradesh, India

    Mansi Gupta

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Jain, P., Wynne, C. (2021). Artificial Intelligence and Big Data in Dentistry. In: Jain, P., Gupta, M. (eds) Digitization in Dentistry. Springer, Cham. https://doi.org/10.1007/978-3-030-65169-5_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65169-5_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65168-8

  • Online ISBN: 978-3-030-65169-5

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation