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KMID : 1023820180100060395
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The Journal of Advanced Prosthodontics
2018 Volume.10 No. 6 p.395 ~ p.400
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A pilot study using machine learning methods about factors influencing prognosis of dental implants
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Ha Seung-Ryong
Park Hyun-Sung
Kim Eung-Hee
Kim Hong-Ki
Yang Jin-Yong
Heo Jun-Young
Yeo In-Sung Luke
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Abstract
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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.
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KEYWORD
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Machine learning, Decision tree, Support vector machine, Dental implant, Implant prognosis
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