Revolutionizing the diagnosis of dental caries using artificial intelligence-based methods

Abstract

Early diagnosis and carious lesion detection through artificial intelligence (AI) have transformed current standard methodologies because it generates precise results which work more efficiently and dependably. AI uses machine learning and deep learning technologies with computer-aided diagnostic systems to accomplish exceptional image evaluation of radiographic data and clinical records in dental caries detection through intraoral scans. This review discusses both prevailing challenges which limit and potential future uses of AI in dental diagnosis together with its ability to become a part of standard clinical work routines. Various researchers confirmed that AI works as a helpful tool which supports dental experts by improving diagnosis and minimizing human biases to enhance preventive care effects for patients.

INTRODUCTION

Dental caries exists as a major global long-standing disease which affects both adults and children, making it a crucial public health burden.[1] Early detection and classification of caries are vital for preventing tooth decay and its associated complications, such as pain, tooth loss, and systemic health issues. Manual detection and classification of caries by dentists can be subjective, time-consuming, and prone to errors, especially in cases of subtle or early-stage lesions.

However, the introduction of deep learning (DL) has transformed the field of medical image interpretation, offering automated and accurate solutions for various tasks.

Various researchers proposed that artificial intelligence (AI) diagnostic models identify hidden caries and develop lesions better than standard diagnostic protocols, which decreases disease progression risks.[2,3] In addition, the implementation of AI technology in clinical practice produces better decisions while reducing healthcare errors and achieving optimized patient care procedures.[4]

Researchers must address issues stemming from data bias with AI while solving problems with limited application range along with regulatory standards to fully optimize AI detection capabilities in caries diagnosis.[5] The review investigates present-day AI applications for caries diagnosis and future potential in dental practice.

TRADITIONAL METHODS VERSUS ARTIFICIAL INTELLIGENCE ADVANCEMENT IN DETECTING DENTAL CARIES

Conventionally, visual-tactile examination and radiographic imaging along with fluorescence-based diagnostic techniques were used for the detection of dental caries. The reliabilty of these clinical procedures is contingent upon the examiners level of expertise and the extent of caries detected throughout the diagnostic process.[6] Visual clinical assessment remains widespread but shows inconsistent results between different clinicians because of its subjectivity. Moreover, the diagnostic sensitivity of bitewing radiographs together with periapical imaging, remains limited when detecting early carious lesions.[7]

However, modern dentistry experienced a major diagnostic transformation after AI arrived into practice.[8]

Major dental image banks serve as training material for these algorithms to acquire knowledge that enables superior identification of patterns and better classification of lesions to enhance clinical decision support. AI technology helps dentists identify carious lesions in the early phases, which allows them to perform more minimal treatment methods and reach better treatment results.

Three machine learning (ML) models, such as support vector machines (SVMs) and decision trees together with random forests, have become standard methods for dental image classification through radiographic caries pattern detection.[9] Dental image analysis through DL networks called convolutional neural networks (CNNs) has surpassed traditional diagnosis techniques in recent times [Figure 1].

Figure 1.

Artificial intelligence in dental use

COMPARATIVE EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE AND TRADITIONAL DIAGNOSTIC METHODS

The high accuracy of AI model data processing enables the systems to discover dental structure patterns which human examiners typically miss because of their subtleness. The reduction of diagnostic variability among clinical experts occurs through AI models along with professional experience level.[10] Younas et al. found that AI treatment plans had an 87% success rate compared to 82% for traditional methods, and diagnostic time was reduced by 35%. Large-scale dental screenings, together with epidemiological studies, benefit from this consistency because it helps maintain precise data collection when diagnostic standards need to be uniform across all assessments.

The detection of carious lesions on periapical radiographs through DL models, especially CNNs, has produced exceptional performance outcomes. The analytical accuracy of CNN-based models reached 90% while surpassing human dental examiner abilities for detecting caries, according to research data.[11] Research shows that AI diagnostic tools function as trustworthy secondary support for human dental practitioners who need to make precise, quick caries treatment decisions [Table 1].

Table 1.

Summary of caries detection methods and artificial intelligence approaches

Method	Description	Advantages	Limitations
Fluorescence-based techniques (DIAGNOdent, QLF)	Utilizes laser fluorescence and light-induced fluorescence to identify demineralization	Sensitive to early demineralization; noninvasive approach	Expensive and not widely available; may produce false positives
Machine learning (SVM, decision trees, random forests)	AI-based supervised learning models classify dental images and detect caries patterns	Reduces examiner variability; improves early-stage lesion detection	Requires large labeled datasets; performance depends on training data quality
Deep learning (CNNs)	Deep learning models process radiographs and images with high sensitivity and specificity	Outperforms traditional methods in identifying hidden and early-stage lesions	Computationally intensive; may require extensive validation before clinical use

AI: Artificial intelligence, CNNs: Convolutional neural networks, SVM: Support vector machine

WORKFLOW OF ARTIFICIAL INTELLIGENCE MODEL

AI utilizes CNNs together with Computer aided diagnosis (CAD) tools, which process radiographic images, intraoral photographs, and optical coherence tomography scans with high levels of accuracy [Figure 2].[12]

Figure 2.

Workflow of artificial intelligence model. CNN: Convolutional neural network[24]

Currently, three AI-based methods for detecting dental cavities have been put forth.[13]

These include:

1. Integration of AI with computer-aided diagnosis (CAD) systems

2. AI-assisted caries-risk assessment

3. AI-based image-based caries detection.

Integration of artificial intelligence with computer-aided diagnosis systems

Dental caries detection receives an enhanced accuracy level through the combination of computer-aided diagnosis (CAD) systems integrating AI-based image processing elements. CAD systems use advanced algorithms to analyze X-ray images which generate highlights to demonstrate potential carious lesions that need further assessment. The detection of early-stage caries works best through these systems because they show conditions that standard clinical examinations cannot identify which enables medical personnel to implement treatments earlier to benefit the patients. Studies have proven that detector systems which use AI help radiologists identify occlusal and proximal caries better than traditional methods because these types of caries are hard to see on radiographic images. Clinical practitioners who include CAD as a standard part of their diagnostic process will achieve more precise diagnoses and simplify the time needed for visual image evaluation.

Artificial intelligence-assisted caries risk assessment

The trend toward minimally invasive and preventative dentistry has highlighted how crucial it is to detect and reduce risk factors that accelerate the development of dental cavities. The risk of getting caries is mostly determined by a number of factors, including dental care utilization, dietary habits, socioeconomic level, oral hygiene practices, and attitudes toward oral health.

In addition to clinical evaluations, ML algorithms have been developed to forecast the risk of root caries based on lifestyle and demographic factors. These models examine huge datasets, revealing variables that might not be normally taken into account.

Image-based caries detection

Various key methods have been used, such as artificial neural networks, CNNs, deep CNNs, and ML algorithm.

MACHINE LEARNING APPROACHES

The detection of dental caries uses ML algorithms that detect patterns in clinical and radiographic images.[14] Through large datasets, these algorithms gain the ability to precisely detect carious lesions from healthy ones. Dental image classification uses supervised learning techniques that include SVM and decision trees together with random forests, as they deliver superior results.[15] The algorithm of SVM takes input data through dimensional mapping and then establishes an optimal separation boundary for health and disease identification. These ML models lead to minimum subjective diagnostic variability by standardizing the criteria and delivering evidence-based predictive outcomes.

DEEP LEARNING AND NEURAL NETWORKS

Basic AI technology known as DL improved dental caries detection capabilities through its complex neural network processing capability of large dental data collections without substantial human involvement.[16] CNNs stand among the most influential DL architectures, which analyze intraoral radiographs, bitewing images, and optical coherence tomography scans with high sensitivity and specificity.[17] Through their ability to understand multilevel feature organization, CNNs become capable of detecting the smallest carious lesions that standard radiographic screenings tend to miss. Studies conducted recently showed that CNN models outperform traditional imaging approaches when identifying early lesions as well as secreted cavities and secondary caries in restored regions.[18] The automated features enabled by DL both improve diagnostic speed and enable doctors to make better decisions through their diagnostic system that provides second opinions.

DATASETS AND ANNOTATION FOR ARTIFICIAL INTELLIGENCE IN CARIES DETECTION

The development of AI models for caries detection relies heavily on annotated datasets that provide labeled dental images for training and validation. Notable datasets include:

• HUNT4 Oral Health Study dataset: Comprising 13,887 bitewing radiographs annotated by six experts, this dataset supports the training of AI models in detecting various dental conditions[19]

• ACTA-DIRECT dataset: Offers high-resolution images and annotations specifically designed to enhance AI-based early caries diagnosis[20]

• AI-Hub platform: Offers 156,965 panoramic and periapical radiographs to train AI models.[13]

ADVANTAGES OF ARTIFICIAL INTELLIGENCE IN CARIES DETECTION

1. Precise results and efficient process: The integration of AI technology into clinical workflows raises dental professionals efficiency and decreases their workload to deliver faster diagnosis-to-treatment processes that benefit patient care

2. Improved accuracy: The implementation of AI technology in caries detection reduces the amount of human judgment that influences diagnostic outcomes

3. Early lesion detection: AI systems can identify early-stage carious lesions that may be missed during visual examinations, enabling timely intervention and improved patient outcomes[21]

4. High sensitivity: Advanced AI equipment, including DL algorithms, provides the best sensitivity for detecting dental lesions, which enables healthcare providers to take early preventive and restorative actions

5. Consistency: AI provides standardized assessments, reducing variability in diagnoses among different practitioners and ensuring uniformity in caries detection

6. Time saving: Dentists face excessive time consumption when they need to analyze multiple images through traditional manual evaluation methods. AI diagnostics shortens the image analysis duration through automated detection, which enables dentists to maintain their patient care focus and treatment management strategy.[22]

Traditional diagnostics encounter difficulties to identify new caries across surfaces which remain tough to examine or lie beneath dental restorations.

The use of AI systems delivers standardized algorithm-based assessment of radiographic and clinical data to establish uniform diagnosis procedures which are repeatable in different settings.

CHALLENGES OF ARTIFICIAL INTELLIGENCE IN CARIES DETECTION[23]

• Data quality and bias: The effectiveness of AI models depends on the quality and huge amount of training datasets. Limited representation can lead to biases, affecting diagnostic accuracy across diverse populations

• Regulatory and ethical concerns: Integrating AI into clinical practice necessitates adherence to regulatory standards and addresses ethical issues related to patient data privacy and the extent of AI’s role in decision-making

• Generalization limitations: AI models trained on specific datasets may underperform when applied to different populations or imaging techniques, highlighting the need for continuous model updates and validation.

CLINICAL IMPLICATIONS

The integration of AI into dental practice offers several clinical benefits:

• Improved diagnostic accuracy: AI models can assist clinicians in identifying carious lesions more accurately, leading to better treatment planning

• Time efficiency: Automated analysis of dental images can reduce the time required for diagnosis, allowing clinicians to focus more on patient care

• Personalized treatment: AI can aid in assessing individual caries risk, facilitating personalized preventive strategies and interventions.

FUTURE PERSPECTIVES

AI technology for caries detection will progress through the development of better-advanced algorithms which link with in-chair diagnostic equipment to perform real-time analysis. OncoCare Health and the University of Texas MD Anderson Cancer Center employ emerging technologies which combine explainable AI with federated learning to enhance AI system adaptability and transparency in a clinical environment.

CONCLUSION

The detection of dental caries has experienced major advancements through AI technology because it delivers superior precision together with decreased diagnostic errors and faster operational speed. The incorporation of ML, DL, and CAD systems helped the dental professionals achieve strong results in carious lesion detection, displaying both sensitivity and specificity. AI development will likely target three different areas that include enhancing model generalization capabilities as well as routine practice integration and ethical implementation standards.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.