Comparing polynomial regression and exponential growth model in predicting recession gain based on clinical gingival recession.

IF 1.4 Q3 DENTISTRY, ORAL SURGERY & MEDICINE
Minerva dental and oral science Pub Date : 2026-06-01 Epub Date: 2026-02-10 DOI:10.23736/S2724-6329.25.05045-4
Pradeep Kumar Yadalam, Maria Maddalena Marrapodi, Vincenzo Ronsivalle, Rocco Franco, Patrizio Bollero, Marco Cicciù, Giuseppe Minervini
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引用次数: 0

Abstract

Background: Gingival recession (GR) is a common issue causing plaque accumulation, root caries, abrasion, cervical wear, and dentin hypersensitivity. Studies show a prevalence of thick and thin gingival biotypes, with no significant association between age, gender, or recession. This study aims to use polynomial regression and exponential growth models to predict recession gain based on clinical data. GR is a common issue causing plaque accumulation, root caries, abrasion, cervical wear, and dentin hypersensitivity. Studies show a prevalence of thick and thin gingival biotypes, with no significant association between age, gender, or recession. This study aims to use polynomial regression and exponential growth models to predict recession gain based on clinical data.

Methods: The study analyzed recession coverage in upper and lower anterior teeth after connective tissue and free gingival grafting procedures from institute database. Data was preprocessed and analyzed using polynomial regression and exponential growth models. Polynomial regression allowed for nonlinear trends in recession gain, while exponential growth models provided insights into cumulative effects of specific factors on recession progression.

Results: The polynomial of degree 3 with the lowest MSE of 0.0612 fit best. This shows that cubic polynomials better represent the "Days"-"Gain Width" relationship than linear or higher-degree polynomials. The exponential growth model fitted to "Post Op" data predicts an initial value of 0.7735 and a growth rate of 0.0101. This model proposes that "Post Op" measurements expand exponentially, with predicted parameters reflecting this growth.

Conclusions: Based on clinical data, this study compares polynomial regression and exponential growth models for periodontal recession gain prediction. The findings emphasize the need of proper mathematical models to better understand periodontal diseases and guide dental care prevention and treatment.

比较多项式回归与指数增长模型在临床牙龈衰退预测中的应用。
背景:牙龈萎缩(GR)是引起牙菌斑堆积、牙根龋、磨蚀、颈椎磨损和牙本质过敏的常见问题。研究表明,厚和薄的牙龈生物型普遍存在,年龄、性别或衰退之间没有显著关联。本研究旨在利用多项式回归和指数增长模型来预测临床数据的衰退增益。GR是一种常见的疾病,可引起牙菌斑堆积、牙根龋、磨蚀、颈椎磨损和牙本质过敏。研究表明,厚和薄的牙龈生物型普遍存在,年龄、性别或衰退之间没有显著关联。本研究旨在利用多项式回归和指数增长模型来预测临床数据的衰退增益。方法:分析研究所数据库中结缔组织和游离牙龈移植手术后上、下前牙的退缩覆盖率。使用多项式回归和指数增长模型对数据进行预处理和分析。多项式回归允许衰退增益的非线性趋势,而指数增长模型提供了对特定因素对衰退进展的累积效应的见解。结果:3次多项式拟合最佳,最小均方差为0.0612。这表明三次多项式比线性或更高次多项式更能代表“天数”-“增益宽度”关系。“Post Op”数据拟合的指数增长模型预测初始值为0.7735,增长率为0.0101。该模型提出“后Op”测量呈指数增长,预测参数反映了这种增长。结论:基于临床数据,本研究比较了多项式回归模型和指数增长模型对牙周衰退增长的预测。研究结果强调,需要适当的数学模型,以更好地了解牙周病和指导牙齿保健预防和治疗。
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来源期刊
Minerva dental and oral science
Minerva dental and oral science DENTISTRY, ORAL SURGERY & MEDICINE-
CiteScore
2.50
自引率
5.00%
发文量
61
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