Dental caries detection methods
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Overview of Dental Caries Detection Methods
Dental caries, or tooth decay, remains a widespread health issue. Early and accurate detection is essential for effective prevention and treatment. Over the years, a variety of methods—ranging from traditional visual inspection to advanced artificial intelligence—have been developed to improve caries detection accuracy and efficiency Moayedi2012Ismail2004.
Traditional Visual and Tactile Caries Detection
Visual and visuo-tactile examination is the cornerstone of caries detection. Dentists use visual inspection, sometimes aided by explorers, to identify lesions. However, these methods can be subjective and may miss early or hidden lesions, especially if the criteria or examination conditions vary Moayedi2012Ismail2004. Studies show substantial variability in the criteria used for visual detection, which can affect reliability and accuracy .
Radiographic Methods for Caries Detection
Dental radiography, including periapical and bitewing X-rays, is widely used to detect caries, especially those not visible to the naked eye. Radiographs are particularly useful for identifying hidden or inaccessible lesions and are often considered a standard reference in research and clinical practice Majanga2021Qayyum2023Liu2025. However, radiographs have limitations, such as exposure to ionizing radiation and difficulty detecting very early lesions Moayedi2012Tashkandi2025.
Fluorescence-Based Caries Detection Devices
Fluorescence devices, which use red, blue, or green wavelengths, can help detect early enamel caries by highlighting areas of demineralization. These devices support visual examination and can improve early detection, but their performance varies widely. Blue and green fluorescence devices may outperform red ones, but overall sensitivity and specificity are moderate, and results can be inconsistent across studies Moayedi2012Macey2020. Fluorescence devices are best used in combination with other diagnostic methods .
Electrical Conductance Methods
Electrical conductance devices measure the electrical properties of tooth surfaces to detect early, non-cavitated caries. While these devices show promise, current evidence is limited and variable, with sensitivities and specificities ranging widely. More robust clinical studies are needed to confirm their effectiveness in real-world settings .
Near-Infrared Transillumination (NIRI) and Intraoral Scanners
Near-infrared transillumination (NIRI) is a non-invasive imaging technique that can detect early caries without ionizing radiation. NIRI-assisted intraoral scanners show high agreement with reference standards and traditional methods, though their sensitivity and specificity may be slightly lower. These scanners are promising, especially for detecting proximal caries in primary teeth, but further research is needed Casalegno2019Tashkandi2025.
Computer-Aided and Deep Learning Approaches
Recent advances in artificial intelligence have led to the development of deep learning models for automated caries detection. Convolutional neural networks (CNNs) and other machine learning techniques can analyze radiographic and transillumination images with high accuracy, sometimes outperforming traditional methods. These models can detect and segment caries, even in early or hidden stages, and are especially useful when large annotated datasets are available Lee2018Majanga2021Qayyum2023+2 MORE. Semi-supervised and cascaded learning approaches further improve performance, even with limited labeled data Qayyum2023Liu2025.
Combining Multiple Detection Methods
No single method is sufficient for all cases due to the dynamic nature of caries and the limitations of each technique. Combining visual inspection, radiography, fluorescence, electrical conductance, and AI-based analysis provides a more comprehensive and reliable approach to caries detection .
Conclusion
Dental caries detection has evolved from basic visual inspection to sophisticated AI-driven image analysis. Each method—visual, radiographic, fluorescence, electrical conductance, NIRI, and deep learning—has unique strengths and limitations. The most effective strategy involves combining multiple methods to ensure early, accurate, and comprehensive caries detection, ultimately improving patient outcomes Moayedi2012Lee2018Majanga2021+7 MORE.
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