评估牙科电子健康记录的准备情况，用于机器学习预测手术结果：来自大嘴存储库的关于复合材料和汞合金修复存活率的见解

IF 4.8 2区医学 Q1 DENTISTRY, ORAL SURGERY & MEDICINE

Journal of dentistry Pub Date : 2025-06-06 DOI:10.1016/j.jdent.2025.105865

Hend Alqaderi , Hesham Alhazmi , Lauren Gritzer , Narjes Bencheikh , Mary Tavares , Jay Patel , Athanasios Zavras

{"title":"评估牙科电子健康记录的准备情况，用于机器学习预测手术结果：来自大嘴存储库的关于复合材料和汞合金修复存活率的见解","authors":"Hend Alqaderi , Hesham Alhazmi , Lauren Gritzer , Narjes Bencheikh , Mary Tavares , Jay Patel , Athanasios Zavras","doi":"10.1016/j.jdent.2025.105865","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><div>Dental electronic health records (EHRs) often lack comprehensive data for evaluating procedure outcomes. Machine learning (ML) enables predictive modeling but its applicability to dental EHR data remains unclear. This study assessed the readiness of dental EHRs for predicting restoration failure using classical and ML models.</div></div><div><h3>Methods</h3><div>Data from the BigMouth Dental Data Repository was analyzed, focusing on posterior restorations in permanent molars of vital teeth. Failure was defined as root canal treatment or extraction within five years. Predictors included restoration type, caries depth, number of surfaces filled, time to failure, age, and gender. Cox proportional hazards analysis and ML models compared amalgam and composite restorations.</div></div><div><h3>Results</h3><div>A total of 21,510 restorations placed between 2011 and 2020 across nine universities were identified. Both classical and ML analyses indicated superior five-year survival for composite restorations. Classic survival analysis showed a higher failure rate for amalgam (8.84 %) compared to composite (4.36 %). Among ML survival models, Cox Proportional Hazards (Cox pH), Random Survival Forest (RSF), and DeepSurv, Cox pH had the highest Concordance Index (C-Index) at 0.64, while RSF and DeepSurv both achieved 0.63. RSF recorded the highest Time-Dependent Area Under Curve (0.620). Age, restoration type, and number of surfaces filled were the most significant predictors of restoration survival.</div></div><div><h3>Conclusion</h3><div>Our findings demonstrate that dental EHRs, in their current state, are not yet equipped to support accurate assessment and prediction of restoration failure. ML models achieved only moderate predictive performance, largely due to the absence of key clinical variables necessary for precise evaluation.</div></div><div><h3>Clinical Significance</h3><div>Gaps in dental EHR data limit the accuracy of ML-based risk prediction. Enhancing diagnostic coding and data standardization can improve predictive modeling, support evidence-based decision-making, and optimize treatment planning. Strengthening EHR documentation will enable Artificial Intelligence-driven tools to enhance clinical outcomes and advance precision dentistry.</div></div>","PeriodicalId":15585,"journal":{"name":"Journal of dentistry","volume":"160 ","pages":"Article 105865"},"PeriodicalIF":4.8000,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Assessing the readiness of dental electronic health records for machine learning prediction of procedure outcomes: Insights from the bigmouth repository on composite and amalgam restoration survival rates\",\"authors\":\"Hend Alqaderi , Hesham Alhazmi , Lauren Gritzer , Narjes Bencheikh , Mary Tavares , Jay Patel , Athanasios Zavras\",\"doi\":\"10.1016/j.jdent.2025.105865\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><div>Dental electronic health records (EHRs) often lack comprehensive data for evaluating procedure outcomes. Machine learning (ML) enables predictive modeling but its applicability to dental EHR data remains unclear. This study assessed the readiness of dental EHRs for predicting restoration failure using classical and ML models.</div></div><div><h3>Methods</h3><div>Data from the BigMouth Dental Data Repository was analyzed, focusing on posterior restorations in permanent molars of vital teeth. Failure was defined as root canal treatment or extraction within five years. Predictors included restoration type, caries depth, number of surfaces filled, time to failure, age, and gender. Cox proportional hazards analysis and ML models compared amalgam and composite restorations.</div></div><div><h3>Results</h3><div>A total of 21,510 restorations placed between 2011 and 2020 across nine universities were identified. Both classical and ML analyses indicated superior five-year survival for composite restorations. Classic survival analysis showed a higher failure rate for amalgam (8.84 %) compared to composite (4.36 %). Among ML survival models, Cox Proportional Hazards (Cox pH), Random Survival Forest (RSF), and DeepSurv, Cox pH had the highest Concordance Index (C-Index) at 0.64, while RSF and DeepSurv both achieved 0.63. RSF recorded the highest Time-Dependent Area Under Curve (0.620). Age, restoration type, and number of surfaces filled were the most significant predictors of restoration survival.</div></div><div><h3>Conclusion</h3><div>Our findings demonstrate that dental EHRs, in their current state, are not yet equipped to support accurate assessment and prediction of restoration failure. ML models achieved only moderate predictive performance, largely due to the absence of key clinical variables necessary for precise evaluation.</div></div><div><h3>Clinical Significance</h3><div>Gaps in dental EHR data limit the accuracy of ML-based risk prediction. Enhancing diagnostic coding and data standardization can improve predictive modeling, support evidence-based decision-making, and optimize treatment planning. Strengthening EHR documentation will enable Artificial Intelligence-driven tools to enhance clinical outcomes and advance precision dentistry.</div></div>\",\"PeriodicalId\":15585,\"journal\":{\"name\":\"Journal of dentistry\",\"volume\":\"160 \",\"pages\":\"Article 105865\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-06-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of dentistry\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0300571225003094\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"DENTISTRY, ORAL SURGERY & MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of dentistry","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0300571225003094","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}

引用次数: 0

摘要

目的牙科电子健康记录（EHRs）往往缺乏全面的数据来评估手术结果。机器学习（ML）可以实现预测建模，但其对牙科电子病历数据的适用性尚不清楚。本研究使用经典模型和ML模型评估了牙科电子病历预测修复失败的准备情况。方法对来自大嘴牙科数据库的数据进行分析，重点分析生牙恒磨牙的后牙修复。失败被定义为在五年内进行根管治疗或拔牙。预测因素包括修复类型、龋深、填充面数、失效时间、年龄和性别。Cox比例风险分析和ML模型比较了汞合金和复合材料修复体。结果在2011年至2020年期间，9所大学共确定了21,510个修复项目。经典和ML分析均表明复合修复体的5年生存率更高。经典生存分析显示，汞合金的失败率（8.84%）高于复合合金（4.36%）。在ML生存模型、Cox比例风险（Cox pH）、随机生存森林（RSF）和DeepSurv中，Cox pH的一致性指数（C-Index）最高，为0.64，RSF和DeepSurv均达到0.63。RSF记录了最高的曲线下时间依赖面积（0.620）。年龄、修复类型和填充面数是修复存活最显著的预测因子。结论目前的牙科电子病历尚不能准确评估和预测修复失败。ML模型仅实现了适度的预测性能，主要是由于缺乏精确评估所需的关键临床变量。临床意义牙科电子病历数据的缺陷限制了基于ml的风险预测的准确性。加强诊断编码和数据标准化可以改善预测建模，支持循证决策，优化治疗计划。加强EHR文档将使人工智能驱动的工具能够提高临床结果并推进精确牙科。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Assessing the readiness of dental electronic health records for machine learning prediction of procedure outcomes: Insights from the bigmouth repository on composite and amalgam restoration survival rates

Objective

Dental electronic health records (EHRs) often lack comprehensive data for evaluating procedure outcomes. Machine learning (ML) enables predictive modeling but its applicability to dental EHR data remains unclear. This study assessed the readiness of dental EHRs for predicting restoration failure using classical and ML models.

Methods

Data from the BigMouth Dental Data Repository was analyzed, focusing on posterior restorations in permanent molars of vital teeth. Failure was defined as root canal treatment or extraction within five years. Predictors included restoration type, caries depth, number of surfaces filled, time to failure, age, and gender. Cox proportional hazards analysis and ML models compared amalgam and composite restorations.

Results

A total of 21,510 restorations placed between 2011 and 2020 across nine universities were identified. Both classical and ML analyses indicated superior five-year survival for composite restorations. Classic survival analysis showed a higher failure rate for amalgam (8.84 %) compared to composite (4.36 %). Among ML survival models, Cox Proportional Hazards (Cox pH), Random Survival Forest (RSF), and DeepSurv, Cox pH had the highest Concordance Index (C-Index) at 0.64, while RSF and DeepSurv both achieved 0.63. RSF recorded the highest Time-Dependent Area Under Curve (0.620). Age, restoration type, and number of surfaces filled were the most significant predictors of restoration survival.

Conclusion

Our findings demonstrate that dental EHRs, in their current state, are not yet equipped to support accurate assessment and prediction of restoration failure. ML models achieved only moderate predictive performance, largely due to the absence of key clinical variables necessary for precise evaluation.

Clinical Significance

Gaps in dental EHR data limit the accuracy of ML-based risk prediction. Enhancing diagnostic coding and data standardization can improve predictive modeling, support evidence-based decision-making, and optimize treatment planning. Strengthening EHR documentation will enable Artificial Intelligence-driven tools to enhance clinical outcomes and advance precision dentistry.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Journal of dentistry 医学-牙科与口腔外科

CiteScore

7.30

自引率

11.40%

发文量

349

审稿时长

35 days

期刊介绍： The Journal of Dentistry has an open access mirror journal The Journal of Dentistry: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. The Journal of Dentistry is the leading international dental journal within the field of Restorative Dentistry. Placing an emphasis on publishing novel and high-quality research papers, the Journal aims to influence the practice of dentistry at clinician, research, industry and policy-maker level on an international basis. Topics covered include the management of dental disease, periodontology, endodontology, operative dentistry, fixed and removable prosthodontics, dental biomaterials science, long-term clinical trials including epidemiology and oral health, technology transfer of new scientific instrumentation or procedures, as well as clinically relevant oral biology and translational research. The Journal of Dentistry will publish original scientific research papers including short communications. It is also interested in publishing review articles and leaders in themed areas which will be linked to new scientific research. Conference proceedings are also welcome and expressions of interest should be communicated to the Editor.