Journal of Prosthodontic Research
Online ISSN : 1883-9207
Print ISSN : 1883-1958
ISSN-L : 1883-1958
Original articles
Emergence angle: Comprehensive analysis and machine learning prediction for clinical application
Omnia SalehKosuke Nozaki Mayuko MatsumuraWataru YanakaAhmed AbdouHiroyuki MiuraKenji Fueki
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JOURNAL OPEN ACCESS
Supplementary material

2022 Volume 67 Issue 3 Pages 468-474

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Abstract

Purpose: To analyze and compare the emergence angle (EA) using two measurement methods, conventional and modified (EA-GPT and EA-R), the EAs of all-natural teeth were evaluated and classified to derive a suitable and predictable clinically applicable measurement method.

Methods: Natural human teeth (n=600) were classified, cleaned, and thoroughly inspected. Teeth were scanned using an intraoral scanner. The scanned data were analyzed using three-dimensional analysis software for both methods with several points per surface. A Bland-Altman analysis was used for statistical analysis and a heat map and a nonparametric density plot to assess the repetition and distribution. An XGBoost regression model was used for prediction.

Results: The EA-R method showed significantly different values compared to the EA-GPT method, representing an increase of 17.5–20.7% for the proximal surfaces. An insignificant difference between the two methods was observed for other surfaces. Different teeth classes showed variation in the normal range, thereby resulting in a new classification of the EA for all-natural teeth based on the interquartile range. The machine learning gradient boosting model predicted conventional data with an average mean absolute error of 0.9.

Conclusions: Variations in the natural teeth EA and measurement methods, suggest a new classification for EA. The established artificial intelligence method demonstrated robust performance, which could aid in implementing EA measurement in prosthetic designs.

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© 2023 Japan Prosthodontic Society

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