AI-Based Detection of Early Dental Wear Patterns: Transforming Diagnosis and Management

Abstract

Dental wear, encompassing attrition, abrasion, and erosion, poses a significant challenge for clinicians due to its subtle early presentation and multifactorial etiology. Recent advancements in artificial intelligence (AI) offer promising opportunities for the early detection and characterization of dental wear patterns. This review synthesizes current scientific evidence on the integration of AI in dental diagnostics, focusing on the epidemiology, pathophysiology, risk factors, clinical features, and the diagnostic potential of AI systems. Furthermore, it explores emerging therapies, guideline recommendations, and the future landscape for AI-assisted dental care, aiming to inform clinicians about best practices and translational implications.

Introduction

Dental wear is a cumulative, irreversible process that can significantly compromise oral function, aesthetics, and quality of life. Traditional diagnostic approaches often fail to identify early, subtle manifestations of wear, leading to delayed interventions. The recent proliferation of AI, particularly machine learning and deep learning algorithms, has revolutionized image-based diagnostics across medical specialties. In dentistry, AI-driven systems are increasingly being developed and validated to enhance the objectivity, speed, and accuracy of early dental wear detection, facilitating timely management. This article provides a comprehensive overview of the clinical and scientific landscape surrounding AI-based dental wear detection, with an emphasis on practical relevance for healthcare professionals.

Epidemiology / Disease Burden

Dental wear is highly prevalent globally, affecting populations across all age groups, though the severity and pattern may vary with age, diet, and lifestyle. Epidemiological studies estimate that up to 30% of young adults and 80% of older adults exhibit some degree of dental wear. Increased longevity, dietary shifts towards acidic foods and beverages, and parafunctional habits have contributed to rising incidence rates. The disease burden is further amplified by the functional and psychosocial impacts of tooth surface loss, particularly in advanced cases. Early detection and preventive strategies are thus pivotal in reducing the long-term morbidity associated with dental wear.

Pathophysiology

Dental wear is a multifactorial process involving the physical and chemical breakdown of dental hard tissues. Attrition results from tooth-to-tooth contact, commonly associated with bruxism and malocclusion. Abrasion is caused by external mechanical forces, such as aggressive toothbrushing or abrasive dental products. Erosion arises from chemical dissolution of enamel and dentin by non-bacterial acids, often related to dietary habits or gastroesophageal reflux. These mechanisms often co-exist, creating complex wear patterns that can be challenging to differentiate clinically, underscoring the need for advanced diagnostic modalities such as AI-based analysis.

Risk Factors

Key risk factors for dental wear include parafunctional habits (e.g., bruxism), dietary acids (citrus fruits, carbonated drinks), consumption of abrasive foods, poor oral hygiene techniques, xerostomia, and systemic conditions such as gastroesophageal reflux disease. In addition, occupational exposures (e.g., dust, chemicals), certain medications, and genetic predispositions may contribute. AI algorithms can be trained to recognize risk patterns from patient data, supporting risk stratification and targeted prevention.

Clinical Features

Early dental wear is often asymptomatic and characterized by subtle changes such as incisal translucency, flattening of cusps, and loss of surface texture. As the process progresses, teeth may exhibit cupping, dentin exposure, increased sensitivity, and in severe cases, compromised function or aesthetics. Accurate documentation and quantification of these features are essential for monitoring disease progression and guiding management. AI tools have demonstrated promise in detecting these nuanced changes on digital scans and clinical photographs, offering superior sensitivity compared to visual-tactile examination alone.

Diagnosis

Conventional diagnosis of dental wear relies on clinical examination, study models, and photographic records, often subject to operator variability and lack of standardization. AI-based detection leverages advanced image processing, deep learning, and pattern recognition to identify and quantify early wear lesions from intraoral scans, radiographs, and photographs. Convolutional neural networks (CNNs) and other machine learning architectures have shown high accuracy in differentiating wear types and grading severity, with recent studies reporting sensitivity and specificity rates exceeding 90%. Integrating AI systems into routine dental workflows can enhance early diagnosis, facilitate longitudinal monitoring, and enable personalized care planning.

Treatment & Management

Management of dental wear is guided by the extent, etiology, and patient-specific factors. Early intervention focuses on risk factor modification, dietary counseling, occlusal splints for bruxism, and preventive measures such as fluoride application. Restorative options including direct composite resins, onlays, or crowns may be indicated for advanced cases to restore function and aesthetics. AI-driven diagnostic insights can support clinicians in selecting optimal treatment modalities, monitoring efficacy, and predicting long-term outcomes.

Recent Advances / Emerging Therapies

The integration of AI into dental diagnostics is accelerating, with recent advances including the development of cloud-based platforms, real-time chairside analysis, and self-learning algorithms that continuously improve with larger datasets. Emerging research highlights the potential for AI to not only detect wear but also predict progression and recommend tailored interventions, thereby enabling precision dentistry. Additionally, AI-enabled tele-dentistry platforms facilitate remote screening and monitoring, expanding access to care and supporting population-level preventive strategies. Future directions include multi-modal AI systems that integrate clinical, radiographic, and behavioral data for comprehensive risk assessment and management.

Guideline Recommendations

While formal guidelines for AI-based dental wear detection are still evolving, leading dental associations advocate for the adoption of digital technologies to enhance diagnostic accuracy and patient outcomes. Recommendations emphasize the importance of clinician oversight, rigorous validation of AI tools, interoperability with electronic health records, and adherence to data privacy standards. Ongoing education and training are essential to ensure clinicians can effectively integrate AI systems into practice and interpret their outputs within the broader clinical context.

Conclusion

AI-based detection of early dental wear patterns represents a transformative advancement in dental diagnostics, offering unparalleled accuracy, objectivity, and efficiency. By enabling timely identification and personalized management, AI has the potential to reduce the burden of dental wear and improve patient outcomes. Continued research, interdisciplinary collaboration, and guideline development are crucial to realize the full potential of AI in clinical dentistry, ensuring safe, ethical, and evidence-based implementation.