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Artificial intelligence for caries detection: Randomized trial.

Sarah Mertens1, Joachim Krois2, Anselmo Garcia Cantu2

  • 1Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany; Department of Operative and Preventive Dentistry, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany.

Journal of Dentistry
|October 17, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) software improved dentists' diagnostic accuracy for detecting enamel caries on radiographs. However, this AI support also led to an increase in invasive treatment decisions, necessitating careful guidance for clinicians.

Keywords:
Artificial intelligenceClinical studies/trialsComputer visionDecision-makingDeep learningPersonalized medicine

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Area of Science:

  • Dental diagnostics
  • Artificial intelligence in healthcare
  • Radiographic interpretation

Background:

  • Accurate detection of proximal caries on bitewing radiographs is crucial for timely intervention.
  • Artificial intelligence (AI) offers potential to enhance diagnostic capabilities in dentistry.
  • Evaluating the clinical impact of AI-driven diagnostic support is essential.

Purpose of the Study:

  • To assess the impact of AI-based diagnostic-support software on proximal caries detection.
  • To evaluate the effect of AI on dentists' diagnostic accuracy and treatment decisions.
  • To analyze AI performance across different caries severities (enamel, early dentin, advanced dentin).

Main Methods:

  • A cluster-randomized cross-over controlled trial involving 22 dentists.
  • Utilized a commercially available AI software (dentalXrai Pro) for caries detection on bitewing radiographs.
  • Compared AI-supported detection with unaided detection, using expert consensus as the reference standard.

Main Results:

  • Dentists using AI demonstrated a significantly higher area under the ROC curve (0.89 vs. 0.85).
  • AI significantly increased sensitivity (0.81 vs. 0.72) for detecting enamel caries, without affecting specificity.
  • Increased sensitivity correlated with more invasive treatment decisions.

Conclusions:

  • AI software enhances dentists' diagnostic accuracy, particularly for enamel caries.
  • AI may lead to an increase in invasive treatment decisions, requiring further investigation and clinical guidance.
  • The study highlights the need to explore variations in AI impact among dentists and refine AI-assisted treatment recommendations.