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Updated: Jul 7, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
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AI-Based Detection of Proximal Caries Across Radiographic Modalities: A Systematic Review and Meta-Analysis.

Yiyang Wang1, Di Fu1, Ge Zhou1

  • 1State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, Sichuan University, Chengdu, 610041, Sichuan, China.

Dento Maxillo Facial Radiology
|July 6, 2026
PubMed
Summary

Artificial intelligence (AI) shows promise for detecting proximal caries, especially with bitewing radiographs. However, heterogeneity and validation challenges limit clinical use, requiring more prospective studies.

Keywords:
Artificial intelligenceDental radiographyMeta analysisProximal cariesSystematic review

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Area of Science:

  • Dental diagnostics
  • Artificial intelligence in healthcare
  • Radiographic imaging analysis

Background:

  • Proximal caries detection is crucial for early intervention.
  • Traditional radiographic interpretation can be challenging.
  • AI offers potential for improved diagnostic accuracy.

Purpose of the Study:

  • To evaluate the diagnostic performance of AI models in detecting proximal caries.
  • To compare AI performance across different radiographic modalities (bitewing, panoramic, periapical).
  • To identify factors influencing AI model accuracy and heterogeneity.

Main Methods:

  • Systematic literature search across five major databases.
  • Extraction of study characteristics, AI models, and accuracy metrics.
  • Methodological quality assessment using QUADAS-2.
  • Meta-analysis of eligible studies with adequate quality.

Main Results:

  • Twenty studies were included; fifteen used bitewing, three panoramic, and two periapical radiographs.
  • AI accuracy varied widely (28.5%–100%).
  • Meta-analysis of ten studies yielded pooled sensitivity of 76%, specificity of 94%, and SROC AUC of 0.90, with substantial heterogeneity.

Conclusions:

  • AI models demonstrate promising diagnostic accuracy for proximal caries, particularly with bitewing radiographs.
  • High heterogeneity and limited external validation are barriers to clinical translation.
  • Future research should focus on prospective, multicenter validation and open datasets.