Early Childhood Predictors for Dental Caries: A Machine Learning Approach

医学 逻辑回归 社会心理的 牙科 接收机工作特性 儿童早期龋齿 社会经济地位 队列 口腔健康 人口 环境卫生 精神科 内科学
作者
Lilian Toledo Reyes,Jéssica Klöckner Knorst,Fernanda Ruffo Ortiz,Bruna Brondani,Bruno Emmanuelli,Renata Saraiva Guedes,Fausto Medeiros Mendes,Thiago Machado Ardenghi
出处
期刊:Journal of Dental Research [SAGE Publishing]
卷期号:102 (9): 999-1006 被引量:22
标识
DOI:10.1177/00220345231170535
摘要

We aimed to develop and validate caries prognosis models in primary and permanent teeth after 2 and 10 y of follow-up through a machine learning (ML) approach, using predictors collected in early childhood. Data from a 10-y prospective cohort study conducted in southern Brazil were analyzed. Children aged 1 to 5 y were first examined in 2010 and reassessed in 2012 and 2020 regarding caries development. Dental caries was assessed using the Caries Detection and Assessment System (ICDAS) criteria. Demographic, socioeconomic, psychosocial, behavioral, and clinical factors were collected. ML algorithms decision tree, random forest, and extreme gradient boosting (XGBoost) were employed, along with logistic regression. The discrimination and calibration of models were verified in independent sets. From 639 children included at the baseline, we reassessed 467 (73.3%) and 428 (66.9%) children in 2012 and 2020, respectively. For all models, the area under receiver operating characteristic curve (AUC) at training and testing was above 0.70 for predicting caries in primary teeth after 2-y follow-up, with caries severity at the baseline being the strongest predictor. After 10 y, the SHAP algorithm based on XGBoost achieved an AUC higher than 0.70 in the testing set and indicated caries experience, nonuse of fluoridated toothpaste, parent education, higher frequency of sugar consumption, low frequency of visits to the relatives, and poor parents’ perception of their children’s oral health as top predictors for caries in permanent teeth. In conclusion, the implementation of ML shows potential for determining caries development in both primary and permanent teeth using easy-to-collect predictors in early childhood.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
1秒前
小小发布了新的文献求助10
1秒前
顾矜应助李长印采纳,获得10
1秒前
1秒前
2秒前
2秒前
量子星尘发布了新的文献求助10
3秒前
直率的惜寒完成签到,获得积分10
4秒前
5秒前
爱莉希雅发布了新的文献求助10
6秒前
7秒前
7秒前
8秒前
情怀应助笨笨山芙采纳,获得10
8秒前
小白发布了新的文献求助10
9秒前
10秒前
小二郎应助科研通管家采纳,获得10
10秒前
核桃应助科研通管家采纳,获得10
10秒前
Lucas应助科研通管家采纳,获得10
10秒前
领导范儿应助科研通管家采纳,获得10
10秒前
10秒前
在水一方应助科研通管家采纳,获得10
10秒前
ludov应助科研通管家采纳,获得10
10秒前
JamesPei应助科研通管家采纳,获得10
10秒前
555557发布了新的文献求助10
10秒前
怡然乌应助科研通管家采纳,获得10
10秒前
坦率的匪应助科研通管家采纳,获得10
10秒前
JamesPei应助科研通管家采纳,获得10
10秒前
坦率的匪应助科研通管家采纳,获得10
10秒前
10秒前
11秒前
11秒前
旺仔完成签到,获得积分10
12秒前
12秒前
端庄的萝发布了新的文献求助20
13秒前
YZQ发布了新的文献求助10
15秒前
tao发布了新的文献求助10
15秒前
爱莉希雅完成签到,获得积分10
16秒前
16秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
‘Unruly’ Children: Historical Fieldnotes and Learning Morality in a Taiwan Village (New Departures in Anthropology) 400
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3988868
求助须知:如何正确求助?哪些是违规求助? 3531255
关于积分的说明 11253071
捐赠科研通 3269858
什么是DOI,文献DOI怎么找? 1804822
邀请新用户注册赠送积分活动 881994
科研通“疑难数据库(出版商)”最低求助积分说明 809035