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Related Concept Videos

Teeth01:15

Teeth

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 and...

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Developing Automated Computer Algorithms to Track Periodontal Disease Change from Longitudinal Electronic Dental

Jay S Patel1,2,3, Krishna Kumar1, Ahad Zai1,4

  • 1Dental Informatics, Department of Cariology Operative Dentistry and Dental Public Health, Indiana Univesity School of Dentistry, Indianapolis, IN 46202, USA.

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Summary
This summary is machine-generated.

Automated computer algorithms can track periodontal disease (PD) changes over 15 years using electronic dental records (EDR). This study developed and validated algorithms for patient cohort generation, enabling AI-driven clinical course analysis.

Keywords:
clinical course of periodontal diseasedata qualitydental informaticselectronic dental recordlongitudinal dataperiodontal cohort generationperiodontal disease

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Area of Science:

  • Dental Informatics
  • Artificial Intelligence in Healthcare
  • Periodontology

Background:

  • Longitudinal tracking of periodontal disease (PD) is crucial for patient management.
  • Electronic dental records (EDR) contain valuable longitudinal data but require sophisticated processing.
  • Developing automated methods to analyze EDR for PD progression is essential.

Purpose of the Study:

  • To develop and validate two automated computer algorithms for extracting information from clinical notes.
  • To generate patient cohorts (disease improvement, progression, no change) for tracking PD.
  • To utilize longitudinal EDR data for analyzing PD changes over time.

Main Methods:

  • Retrospective study of 28,908 patients from 2009-2014.
  • Development of algorithms using Python libraries (Pandas, TensorFlow, PyTorch) and NLP.
  • Performance evaluation via manual review, confusion matrix, precision, recall, sensitivity, specificity, and accuracy.
  • Analysis of EDR data density for follow-up periods up to 15 years.

Main Results:

  • 34% of the cohort had up to 5 years of follow-up data.
  • 42% of patients had at least two PD diagnoses for change determination.
  • Using clinician diagnoses, 72% showed no PD status change, 13% progressed, and 11% improved.

Conclusions:

  • Longitudinal EDR data can be feasibly used to track PD changes over 15 years.
  • Detailed steps and algorithms were provided for EDR data cleaning, preprocessing, and cohort generation.
  • The developed methods facilitate the study of clinical courses using AI and machine learning.