Dental caries is the world’s most common chronic disease. It affects people of all ages. Despite being preventable, it is often neglected. Caries have many causes. Untreated caries can affect overall health. Traditional detection methods, like probes and X-rays, are often inaccurate. Newer methods, such as laser fluorescence, are also imperfect. AI offers improved accuracy and real-time diagnosis. AI in dentistry is transforming dental practices and plays a crucial role in caries detection.

DENTAL CARIES AND ITS EITIOLOGY

Dental caries are a multifactorial disease. It’s caused by biofilms and leads to demineralization of teeth. Acids from bacteria dissolve apatite crystals. This forms a cavity over time. Several factors contribute, including tooth, host, and saliva factors. Early detection allows remineralization of early lesions. This is vital for minimally invasive dentistry.

AI IN DENTAL CARIES DETECTION: A NEW WAVE IN DIAGONSING

Dental caries typically begin at a microscopic level, making early signs challenging to detect with the naked eye or standard radiographs. However, leveraging AI-powered tools for caries detection enables dentists to identify lesions in their initial stages. This integration of AI in dentistry facilitates early detection and prevention.

AI algorithms play a crucial role in dental caries detection by swiftly processing radiographs to provide real-time diagnoses. This approach enhances efficiency, reduces human error, and empowers dentists to make well-informed decisions promptly.

HOW AI IN DENTISTRY WORKS FOR CARIES DETECTION.

The identification of caries involves several steps, from image acquisition to final diagnosis.

Image Acquisition

The images are acquired from digital radiographs like bitewing, OPG or other imaging techniques. It is highly crucial to make sure radiographs are of good quality and high resolution, which can help with accurate analysis.

Pre-processing

This step includes removing grainy images and increasing the contrast for better accuracy to improve the quality of the image for subsequent analysis.

Segmentation

The image is divided into various segments and converted into pixels to identify a tooth or grey area.

Feature Extraction

Data previously fed is now analysed to give a diagnosis of whether caries are detected based on the colour, shape, and texture of various regions.

Classification

The classification is done using various algorithms, like CNN (Convolution Neural Network), SVM (Support vector machines), and deep learning for the ICDAS radiographic scoring system.

Post-Processing

This stage concludes with refining the errors made by AI during analysis.

The result is now given as a diagnosis: with appropriate classification, which can be color-coded to represent various stages of demineralization based on the algorithm used.

BENEFITS AI IN DENTAL CARIES DETECTION.

Early Diagnosis

The earlier the detection of caries, the less invasive the procedure for treatment. AI in caries detection helps with identifying decay at early stages, which in turn results in no wide cavity preparation but minimal procedures to reverse caries.

Increased Efficiency

AI tools can scan and interpret data, eliminating any natural bias as different dentists can interpret data differently, which reduces human error.

Less Dental Chair-side time

Due to increased accuracy of data interpretation and the ability to provide diagnosis in seconds, the time taken to read and interpret X-rays is reduced.

Patient Compliance and Education

AI in dentistry is advancing so rapidly that it allows color-coded depictions which help patients to understand their condition better. They are more likely to accept treatment on seeing the same diagnosis given by an AI tool. The visual depiction also allows us to educate them better by encouraging better oral hygiene practices.

Cost-effective care

As dentistry is moving towards holistic and preventive care, early detection allows for less invasive procedures by protecting the tooth from root canals or extractions which can lead to loss of the tooth.

ETHICAL CONSIDERATIONS AND CHALLENGES

AI advancements in dentistry are at their peak, but ensuring data safety and consent remains a critical concern. The issue of obtaining genuine consent is a recurring challenge, emphasizing the need for patient education. Moreover, the risk of model bias arises when AI systems are trained on restricted datasets, potentially leading to subpar performance across various demographic groups.

To effectively leverage AI in dental care, clinics must establish robust digital infrastructure to support these advanced technologies. It is imperative that the tools employed for detecting dental caries using AI adhere to ethical and legal guidelines and undergo validation by regulatory bodies before being introduced to the market.

REAL-WORLD APPLICATIONS

AI technology is no longer confined to research labs; it’s actively improving diagnostic accuracy in real dental practices. Leading platforms like Pearl, Overjet, and scanO AI are using AI to detect dental caries with speed, precision, and clinical relevance.

Pearl- Pearl’s Second Opinion is an FDA-cleared AI platform designed to assist dentists in identifying a variety of oral conditions directly from dental radiographs, including dental caries.

How it detects caries: Pearl uses deep convolutional neural networks trained on millions of annotated dental images. It analyses intraoral X-rays to identify radiolucent areas that suggest enamel or dentin demineralization. Once detected, it highlights these areas with coloured overlays directly on the radiograph, helping dentists verify potential lesions quickly.

Clinical advantage: Second Opinion provides real-time detection and integrates with major practice management systems, acting as a supportive “second set of eyes” to reduce missed lesions and improve diagnostic consistency.

Overjet-Another FDA-cleared AI solution trusted by large dental groups and DSOs across the U.S. It specializes in dental radiograph interpretation with a focus on diagnosis support, compliance, and treatment planning.

 How it detects caries: Overjet uses advanced machine learning algorithms to perform pixel-level image analysis on bitewings and periapical radiographs. The software identifies variations in density patterns that signal enamel breakdown or various lesions. It precisely outlines the lesion area and provides clinical context, such as depth and proximity to the pulp.

Clinical advantage: What sets Overjet apart is its ability to pair detection with clinical reasoning. It doesn’t just mark a spot—it helps determine the stage of decay, providing more actionable insights for treatment decisions.

 scanO AI-Top dental AI company, gaining recognition for its innovative approach to early caries detection, particularly in remote dentistry.

How it detects caries: Scano AI integrates near-infrared imaging and optical fluorescence technologies with AI. Its system identifies very early demineralization by analysing light reflection and absorption changes on tooth surfaces—areas where X-rays may not detect early-stage caries. The AI compares current scans with historical data to highlight changes over time, thus supporting proactive caries management.

Clinical advantage: scanO AI excels in detecting non-cavitated lesions and monitoring lesion progression. Its non-invasive, radiation-free detection method makes it ideal for frequent monitoring in children and high-risk patients.

CONCLUSION

AI in Dental caries Detection-represents a major breakthrough in clinical dentistry. It helps clinicians detect cavities earlier, provides better treatments and provides patient-centred care with greater efficiency. As the article elucidates the benefits, it also assures dentists that AI cannot replace them but empowers them in ways which they couldn’t comprehend before AI in dentistry.