Fundamentals of Treatment Planning. Lino Calvani
us to see what we cannot see with the naked eye, and so to touch, move, increase, and decrease – in a very practical and ‘quasi-normal’ intuitive way – the size of holographic virtual objects that physically appear in front of or around us. Users move their hands in a close, dedicated 3D virtual world that allows them to see, interact with, and use all types of actual (real) analog devices that are connected to the system. This means that we do not physically touch the instruments but rather touch and work with them from a virtual remote. We then receive useful written information about these devices that ‘float in the air’ before us so we can know, analyze, plan, and better control our workflows.
This situation is very difficult to imagine and understand if you are not actually working with it. However, it is extremely useful and will soon dramatically change the way we live and work.
Apart from all that has been discussed in this chapter, it is not possible for us to actually foresee which prosthodontic tools we will use in the future. Although the organization of treatment planning will certainly change, the clinical rationale on which treatments are based will not change. Even if one day an artificial general intelligence (AGI) team takes the place of humans at the chairside, the step-by-step planning procedure is simplified and sped up by new diagnostic methods, and workflows change according to the capabilities of new diagnostic and treatment tools, the clinical rationale remains the same.
The rise of deep-learning and self-learning AI algorithms is currently turning the world upside down. Practically, computers program themselves instead of being programmed by humans, enabling the computers themselves to ‘learn’ how to perform useful assignments. Computer programs have taken over from the old analog rules and are performing assignments in the most useful way. Training data programmed into increasingly large artificial neural networks are being adjusted and reordered to obtain the desired result. Furthermore, these results show that a deep-learning system that has been well-trained enough may find indirect and precise repeatable abstract patterns in data. This technique is already being used to perform an increasing number of practical tasks, from face recognition to predicting diseases from medical images, just like human doctors do when they investigate their patients’ signs and symptoms in order to understand their ailments, diseases or illnesses.114 So, how long before these incredibly quick machines completely change medical and dental medical science? By means of DNA sequencing manipulation, it will also be possible to program the elimination of diseases, including caries, and align the position of the teeth from their eruption. And when it is not possible to change something in that way, it will be removed, terminated, and rebuilt by powerful physically and chemically instructed nanocarriers, nanorobots, and machines.
Indeed, the evidence shows that everything that has been imaginable and thinkable in science has more or less been achieved in practice, because humans have an infinite capacity for curiosity and imagination. Therefore, it is foreseeable that in a few decades from now, the speciality of prosthodontic treatment planning and its current tools will be radically changed.
1. Academy of Prosthodontics. History. https://www.academyofprosthodontics.org/History.html. Accessed 8 February 2019.
2. Academy of Prosthodontics. Glossary of Prosthodontic Terms. https://www.academyofprosthodontics.org/_Library/ap_articles_download/GPT8.pdf. Accessed 20 March 2019.
3. The Academy of Denture Prosthetics. Principles, concepts, and practices in prosthodontics. J Prosthet Dent 1968;19:180–198.
4. McLean JW. The future of restorative materials. J Prosthet Dent 1979;42:154–158.
5. Rosenstiel SF, Land MF, Fujimoto J. Contemporary Fixed Prosthodontics, ed 3. Mosby Elsevier, 2001.
6. Shillingburg HT, Hobo S, Whitsett LD. Fundamentals of Fixed Prosthodontics, ed 2. Quintessence, 1981.
7. Smyd ES. Dental Engineering. J Dent Res 1948;27:649.
8. Tylman SD, Malone WF. Tylman’s Theory and Practice of Fixed Prosthodontics, ed 7. St. Louis: Mosby, 1978.
9. Richter WA, Mahler DB. Physical properties vs. clinical performance of pure gold restorations. J Prosthet Dent 1973;29:434–438.
10. Colmery BH 3rd. Composite restorative dentistry. Vet Clin North Am Small Anim Pract 1998;28:1261–1271.
11. DeWald JP, Moody CR, Ferracane JL, Cotmore JM. Crown retention: a comparative study of core type and luting agent. Dent Mater 1987;3:71–73.
12. Standlee JP, Caputo AA, Hanson EC. Retention of endodontic dowels: effects of cement, dowel length, diameter, and design. J Prosthet Dent 1978;39:400–405.
13. A Alharbi F, Nathanson D, Morgano SM, Baba NZ. Fracture resistance and failure mode of fatigued endodontically treated teeth restored with fiber-reinforced resin posts and metallic posts in vitro. Dent Traumatol 2014;30:317–325.
14. Eftekhar Ashtiani R, Nasiri Khanlar L, Mahshid M, Moshaverinia A. Comparison of dimensional accuracy of conventionally and digitally manufactured intracoronal restorations. J Prosthet Dent 2018;119:233–238.
15. Butz F, Lennon AM, Heydecke G, Strub JR. Survival rate and fracture strength of endodontically treated maxillary incisors with moderate defects restored with different post-and-core systems: an in vitro study. Int J Prosthodont 2001;14:58–64.
16. Gutmann JL. The dentin-root complex: anatomic and biologic considerations in restoring endodontically treated teeth. J Prosthet Dent 1992;67:458–467.
17. Guzy GE, Nicholls JI. In vitro comparison of intact endodontically treated teeth with and without endo-post reinforcement. J Prosthet Dent 1979;42:39–44.
18. Jendresen MD, Charbeneau GT, Hamilton AI, Phillips RW, Ramfjord SP. Report of the Committee on Scientific Investigation of the American Academy of Restorative Dentistry. J Prosthet Dent 1979;41:671–695.
19. Kantor ME, Pines MS. A comparative study of restorative techniques for pulpless teeth. J Prosthet Dent 1977;38:405–412.
20. Maroulakos G, Nagy WW, Kontogiorgos ED. Fracture