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Related Concept Videos

Teeth01:15

Teeth

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The formation of teeth, also known as odontogenesis, is a complex process that begins in utero, around the sixth week of embryonic development. There are three stages to this process: the bud stage, the cap stage, and the bell stage.
In the bud stage, the tooth germ (an aggregation of cells) starts to form in the developing jawbone. During the cap stage, the tooth germ differentiates into enamel organ, dental papilla, and dental sac, which will later develop into the tooth's enamel, dentin...
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Tooth Anatomy01:21

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The human tooth enables us to eat a variety of foods, speak clearly, and even aid in shaping our faces. Teeth are composed of various elements that work together. Here's a detailed look at the anatomy of a human tooth.
The Crown, Neck, and Root
The visible part of the tooth is referred to as the crown. It's covered by enamel, the hardest substance in the human body. The crown is uniquely shaped for each type of tooth, allowing for different functions such as cutting, tearing, or...
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Related Experiment Video

Updated: Apr 9, 2026

Precision of In Vivo Quantitative Tooth Wear Measurement Using Intra-Oral Scans
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Data-Driven Tooth Selection Enhances Partial-Mouth Periodontal Screening.

N Lin1, C Wang1, Y Shan1

  • 1School of Stomatology, Zhejiang Chinese Medical University, The Stomatology Hospital of Zhejiang Chinese Medical University, Hangzhou, People's Republic of China.

Journal of Dental Research
|April 8, 2026
PubMed
Summary
This summary is machine-generated.

A new data-driven framework using SHapley Additive exPlanations (SHAP) values identifies key teeth for partial-mouth periodontal examination (PMPE). This interpretable method offers an efficient alternative to traditional full-mouth exams for large-scale surveys.

Keywords:
Periodontal Indexepidemiological monitoringhealth surveysmachine learningmass screeningperiodontitis

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Area of Science:

  • Periodontology
  • Dental Public Health
  • Machine Learning in Medicine

Background:

  • Periodontitis is a global health concern, necessitating efficient diagnostic methods.
  • Current partial-mouth periodontal examination (PMPE) protocols lack validated efficiency and subpopulation-specific assessments.
  • Full-mouth periodontal examination (FMPE) is accurate but resource-intensive.

Purpose of the Study:

  • To develop and validate an interpretable, data-driven framework for selecting teeth in PMPE protocols.
  • To assess the efficiency and diagnostic agreement of the developed protocol compared to existing methods.
  • To explore the potential for subpopulation-tailored PMPE protocols.

Main Methods:

  • Utilized SHapley Additive exPlanations (SHAP) values from XGBoost and LightGBM models trained on National Health and Nutrition Examination Survey (NHANES) data (2009-2014).
  • Ranked tooth importance based on maximum interproximal probing depth and clinical attachment loss in adults aged 35 and older.
  • Validated the top-ranked teeth against FMPE using quadratic weighted kappa (QWK) and inflation factor (IF) for prevalence bias, benchmarking against Community Periodontal Index (CPI) and modified Ramfjord protocols.

Main Results:

  • The unified XGBoost-derived protocol (FDI 47, 27, 17, 26, 37, 16, 42, 46, 36, 41) achieved a QWK of 0.85 and an IF of 127.29% in external validation.
  • The SHAP-guided protocol demonstrated significantly lower prevalence estimation bias (IF) compared to the CPI (P=0.003).
  • Age- and gender-specific optimizations showed localized benefits, but the unified protocol remained robust across strata.

Conclusions:

  • A SHAP-guided, data-driven tooth selection framework provides an interpretable and efficient alternative for PMPE.
  • This approach can enhance the accuracy and feasibility of large-scale periodontal epidemiological surveys.
  • The developed unified protocol shows potential to replace less efficient traditional methods like CPI.