ISSN 2415-3060 (print), ISSN 2522-4972 (online)
  • 32 of 49
УЖМБС 2019, 4(4): 200–210

The Decision-Making Support System of Doctor’s Choice of Dental Implantation Method

Chernenko V. M.1, Lyubchenko A. V.2, Kochina M. L.3

The dental implantation has recently become one of the most popular methods of treating complete or partial lack of teeth. Prosthetics on dental implants is a rehabilitation method for patients with dental defects, which allows solving the issues of professional, social, psychological, physiological, and aesthetic nature. Taking into account the existing clinical features of the somatic and dental condition of a patient, the dentist chooses an optimal method of dental implantation for each case. The complexity of medical decision is conditioned by the high level of responsibility that is why decisions are made not by one criterion, but by the set of many criteria considered simultaneously. The purpose of the work was to develop a decision-making support system for a dentist regarding the choice of dental implantation method. Material and methods. For the development of a decision-making support system, we analyzed the results of dental implantation of 90 patients, who had been set 180 implants. Preoperative laboratory studies of patients included clinical blood and urine tests, bacteriological sowings from the oral cavity, and also biochemical studies (determination of the level of sugar, calcium and phosphorus in the blood). We determined the absorption coefficient of the jaw tissue based on the results of X-ray studies. Using the Osstell ISQ apparatus, we assessed an implantation stability quality intraoperatively after its installation. The depth of the ascetic furrow was measured by the probing method; the index of gum bleeding was determined taking into account the number of the tooth which was implanted. Patients underwent the direct dental implantation with immediate or postponed loading, as well as standard two-stage implantation according to their indications. Results and discussion. We used fuzzy logic and subtractive clustering (by mountain method) to develop the results of the dental implantation prediction models. This clustering method allows the patients to be divided into groups with the same mechanisms of the studied indices influence on the result of dental implantation. To construct a prediction model for the dental implantation result, the Sugeno system of fuzzy inference was used. According to the data of patients who were conducted dental implantation by different methods, three fuzzy models were obtained, each consisting of two fuzzy logic equations, two functions of fuzzy rule belonging to the corresponding input variable, and two linear functions in the derivation of fuzzy rules for forecasting dental implantation results. The prediction of the dental implantation results with the general accuracy of prediction at 94.5% was performed according to the following intraoperative indicators: the ascetic furrow depth and the implant stability quality coefficient. Conclusions. Using the developed decision-making support system and the set of models for assessing intraoperative indexes of patients can help in prediction of the success of dental implantation conducted by different methods. These factors allow choosing the optimal implantation method in each case or adjusting the rehabilitation plan of the patient in case of obtaining an unfavorable prognosis.

Keywords: dental implantation, immediate and postponed loading, decision-making support system for a doctor, fuzzy logic

Full text: PDF (Ukr) 642K

  1. Antonova-Rafi YuV, Moskovskyy YuV. Doslidzhennya dotsilnosti vykorystannya system pidtrymky pryynyattya rishen v medytsyni. Analiz nedolikiv ta yikh usunennya [Research of expediency of the use of the systems of support of making decision is in medicine. Analysis of defects and their removal]. Scientific Journal «ScienceRise». 2015; 6(2): 49-52. [Ukrainian]
  2. Bidyuk PI, Hozhyy OP, Korshevnyuk LO. Komp'yuterni systemy pidtrymky pryynyattya rishen [Computer systems of support of making decision]. Posibnyk. Kyiv; 2010. 382 p. [Ukrainian]
  3. Zablotskyy YaV. Implantatsiya v neznimnomu protezuvanni [Implantation is in unremovable prosthetic appliance]. Lviv: HalDent; 2006. 156 p. [Ukrainian]
  4. Zlepko SM, Ovcharuk TI, Ovcharuk AA. Ohlyad medychnykh informatsiynykh system [Review of the medical informative systems]. Systemy obrobky informatsiyi. 2011; 3: 189–92. [Ukrainian]
  5. Kobzar AI. Prikladnaya matematicheskaya statistika [Applied mathematical statistics]. M: Fizmatlit; 2006. 816 p. [Russian]
  6. Malanchuk VA, Mammadov ZA. Bezposerednya dentalna implantatsiya [Direct dental implantation]. Kyiv; 2008. 155 p. [Ukrainian]
  7. Melnikova NI, Steblina KV. Osoblyvosti proektuvannya system pidtrymky likuvalnykh rishen [Features of planning of the systems of support of curative decisions]. Matematychni mashyny I systemy. 2014; 1: 92–100. [Ukrainian]
  8. Ivanov SYu, Muraev AA, Rukina EA, Bunev EA. Metod neposredstvennoy dentalnoy implantatsii [Method of direct dental implantation]. Sovremennye problemy nauki i obrazovaniya. 2015; 5. Available from: http:/ /article/view?id = 22310. [Russian]
  9. Sirak SV, Sletov AA, Gandylyan KS, Dagueva MV. Neposredstvennaya dentalnaya implantatsiya u patsientov s vklyuchennymi defektami zubnykh ryadov [Direct dental implantation for patients with the included defects of dental rows]. Meditsinskiy vestnik Severnogo Kavkaza. 2011; 21(1): 51-4. [Russian]
  10. Totsenko VH. Ekspertni systemy diahnostyky i pidtrymky rishen [Consulting models of diagnostics and support of decisions]. NAN Ukrainy Instytut problem reyestratsiyi informatsiyi. Kyiv: Nauk dumka; 2004. 126 p. [Ukrainian]
  11. Shtovba SD. Proektirovanie nechetkikh sistem sredstvami MATLAB [Planning of the unclear systems facilities MATLAB]. M: Goryachaya liniya–Telekom; 2007. 288 p. [Russian]
  12. Bezdec JC. Fuzzy Models and Algorithms for Pattern Recognition and Image Processing. NY: Springer; 2005. 785 p.
  13. Denissen HW, Kalk W, Veldhuis HA, Van Waas MA. Anatomic consideration for preventive implantation. Int J Oral Maxillofac Implants. 1993; 8(2): 191-6.
  14. Holger N, Jaime UG. Fuzzy Logic Toolbox [digital resource]. 2014. Available from:
  15. Open source software for numerical computation [digital resource]. Available from:
  16. Lekovic V, Camargo PM, Klokkevold PR, Weinlaender M, Kenney EB, Dimitrijevic B, et al. Preservation of alveolar bone in extraction sockets using bioabsorbable membranes. J Periodontol. 1998; 69(9): 1044-9.
  17. Singla J, Grover D, Bhandari A. Medical Expert Systems for Diagnosis of Various Diseases. International Journal of Computer Applications. 2014; 7: 36–43.
  18. Zadeh LA. Fuzzy logic and approximate reasoning. Synthese. 1975; 30(3/4): 407−28.