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Exploring the Methodological Approaches of Studies on Radiographic Databases Used in Cariology to Feed Artificial
Amadou Diaw Ndiaye1, Marie Agnès Gasqui2,3, Fabien Millioz4
1Service d'Odontologie Conservatrice-Endodontie, Université Cheikh Anta Diop, Dakar, Senegal, adnndiaye@gmail.com.
Caries Research
|February 11, 2024
Summary
Studies using artificial intelligence for dental caries detection show low scientific quality. Developing standardized guidelines is crucial for improving AI algorithm reliability and clinical application in dentistry.
Area of Science:
- Dentistry
- Artificial Intelligence
- Radiology
Background:
- Computer-aided diagnostic systems demonstrate superior efficiency and accuracy in diagnostic imaging compared to dentists.
- Machine learning and deep learning are increasingly utilized in dental radiographic analysis.
- Dental caries classification, detection, and segmentation are key applications of AI in dentistry.
Purpose of the Study:
- To systematically review and evaluate the methodological approaches of studies using machine learning and deep learning for dental caries analysis on radiographic databases.
- To assess the quality and identify weaknesses in the methodologies of existing studies in this field.
Main Methods:
- A systematic literature search was conducted across major databases (MEDLINE, Embase, IEEE Xplore, Web of Science) until December 2022.
- Eligible studies utilizing dental radiographic databases for caries analysis were assessed by two independent reviewers, with a third reviewer for discrepancies.
- Methodological quality was evaluated using the CLAIM and QUADAS-AI checklists.
Main Results:
- Out of 325 screened articles, 35 studies were included, with bitewing radiographs being the most common (n=17) and detection being the most frequent task (n=15).
- Convolutional neural networks were the predominant models used. The mean completeness of CLAIM items was 49%, indicating significant methodological weaknesses.
- Most studies were monocentric, and only 9% used external test sets. QUADAS-AI revealed that 43% of studies were at low risk of bias regarding the standard reference domain.
Conclusions:
- The overall scientific quality of studies developing AI algorithms for dental caries is low.
- Improvements in study design, validation, and the establishment of standardized guidelines are necessary for enhancing reproducibility and generalizability.
- Standardized guidelines are essential for the successful clinical application of AI in dentistry.
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