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Artificial intelligence for detecting periapical radiolucencies: A systematic review and meta-analysis
1University for Digital Technologies in Medicine and Dentistry, Wiltz, Luxembourg.
Journal of Dentistry
|June 8, 2024
Summary
Artificial intelligence (AI) shows high accuracy in detecting periapical radiolucencies, with pooled sensitivity and specificity of 0.94 and 0.96, respectively. Further research is needed to confirm AI
Area of Science:
- Dentistry
- Medical Imaging
- Artificial Intelligence
Background:
- Dentists' diagnostic accuracy for periapical radiolucency detection varies.
- Artificial intelligence (AI) offers potential for improved diagnostic accuracy.
Approach:
- Systematic review and meta-analysis of studies published until November 2023.
- Searched PubMed/MEDLINE, ScienceDirect, and IEEE Xplore databases.
- Utilized MetaDTA Tool for pooled sensitivity and specificity calculations; QUADAS-2 for risk of bias assessment.
Key Points:
- 24 studies met inclusion criteria; 4 included in meta-analysis.
- AI demonstrated high pooled sensitivity (0.94) and specificity (0.96) for periapical radiolucency detection.
- Most studies assessed AI model accuracy, not fully developed software systems.
Conclusions:
- AI shows significant potential in supporting the detection of periapical radiolucencies.
- Current evidence base has a high risk of bias and applicability concerns.
- Prospective, real-world randomized controlled trials with diverse data are necessary to validate AI's clinical utility.
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