J Adv Prosthodont.  2018 Dec;10(6):395-400. 10.4047/jap.2018.10.6.395.

A pilot study using machine learning methods about factors influencing prognosis of dental implants

• Ha SR
• Park HS
• Kim EH
• Kim HK
• Yang JY
• Heo J
• Yeo IS

• KMID: 2429371
• DOI: http://doi.org/10.4047/jap.2018.10.6.395

Abstract

PURPOSE
This study tried to find the most significant factors predicting implant prognosis using machine learning methods.
MATERIALS AND METHODS
The data used in this study was based on a systematic search of chart files at Seoul National University Bundang Hospital for one year. In this period, oral and maxillofacial surgeons inserted 667 implants in 198 patients after consultation with a prosthodontist. The traditional statistical methods were inappropriate in this study, which analyzed the data of a small sample size to find a factor affecting the prognosis. The machine learning methods were used in this study, since these methods have analyzing power for a small sample size and are able to find a new factor that has been unknown to have an effect on the result. A decision tree model and a support vector machine were used for the analysis.
RESULTS
The results identified mesio-distal position of the inserted implant as the most significant factor determining its prognosis. Both of the machine learning methods, the decision tree model and support vector machine, yielded the similar results.
CONCLUSION
Dental clinicians should be careful in locating implants in the patient's mouths, especially mesio-distally, to minimize the negative complications against implant survival.

Keyword

Machine learning; Decision tree; Support vector machine; Dental implant; Implant prognosis

MeSH Terms

Decision Trees
Dental Implants*
Dentists
Humans
Machine Learning*
Methods*
Mouth
Oral and Maxillofacial Surgeons
Pilot Projects*
Prognosis*
Sample Size
Seoul
Support Vector Machine
Dental Implants

Figure

• 

  Fig. 1 The decision tree model for implant survival. The mesio-distal position is the most significant factor determining implant prognosis (accuracy = 0.93).

• 

  Fig. 2 The decision tree model for complications of implant. Thread/fixture exposure appeared when the implant was placed with the bone graft even when the implants were placed adequately (accuracy = 0.64).

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