Artificial Intelligence for Digital Tooth Wear Progression Analytics

Abstract

Tooth wear represents a significant and growing clinical challenge in modern dental practice, with implications for oral function, esthetics, and overall health. The advent of artificial intelligence (AI) has provided new opportunities for the digital analysis of tooth wear progression, enabling more precise, objective, and efficient assessment compared to traditional visual and manual techniques. This review examines the mechanisms, clinical relevance, and practical application of AI-driven digital analytics for tooth wear progression, highlighting recent advances, current evidence, and guideline recommendations for healthcare professionals.

Introduction

Tooth wear encompasses the non-carious loss of dental hard tissues through processes such as attrition, abrasion, and erosion. The accurate assessment and monitoring of tooth wear progression are essential for timely intervention and prevention of further dental morbidity. Conventional methods of tooth wear analysis are often subjective and lack reproducibility. In recent years, AI-powered digital technologies—leveraging machine learning, computer vision, and 3D image analysis—have emerged as transformative tools for enhancing the objectivity and reliability of tooth wear assessment. This article discusses the epidemiology, mechanisms, risk factors, clinical features, diagnosis, management, and future directions of AI-based digital tooth wear progression analytics.

Epidemiology / Disease Burden

Tooth wear is a prevalent condition affecting populations worldwide, with studies reporting a prevalence ranging from 10% to 30% in adults and up to 50% in elderly cohorts. The disease burden is anticipated to rise due to increasing life expectancy, changes in dietary habits, and greater retention of natural teeth into older age. Severe tooth wear can result in significant functional impairment, hypersensitivity, and esthetic concerns, with corresponding impacts on quality of life. Epidemiological data underscore the importance of early detection and individualized management strategies, which are greatly enhanced by the precision of AI-driven analytics.

Pathophysiology

The pathophysiology of tooth wear involves a multifactorial interplay between mechanical forces (attrition and abrasion) and chemical insults (erosion). Attrition refers to the wear caused by tooth-to-tooth contact, commonly seen in bruxism or malocclusion. Abrasion results from exogenous mechanical forces, such as aggressive toothbrushing or abrasive dental products. Erosion is mediated by acidic challenges from dietary sources or gastroesophageal reflux, leading to demineralization and softening of enamel and dentin. The cumulative effect of these factors leads to progressive loss of tooth structure, which can be systematically quantified using AI-based digital technologies.

Risk Factors

Risk factors for tooth wear include intrinsic factors (e.g., bruxism, malocclusion, salivary hypofunction, genetic susceptibility) and extrinsic factors (e.g., acidic diet, occupational exposures, poor oral hygiene, frequent consumption of carbonated beverages). Systemic conditions such as gastroesophageal reflux disease (GERD) and eating disorders may exacerbate erosive tooth wear. Recognition of these risk factors is critical for targeted prevention and intervention, and AI-based analytics can aid in risk stratification by analyzing patient-specific digital records and wear patterns.

Clinical Features

Clinically, tooth wear presents as progressive loss of enamel and dentin, often manifesting as flattened occlusal surfaces, incisal edge shortening, cupping, grooving, and increased tooth sensitivity. Advanced wear may result in exposure of dentin, pulpal irritation, compromised mastication, and esthetic deterioration. AI-powered digital tools can enhance the identification of subtle morphological changes through high-resolution 3D imaging and automated measurement of wear facets, providing clinicians with precise data for monitoring progression over time.

Diagnosis

The diagnosis of tooth wear traditionally relies on clinical examination, patient history, study casts, and photographic documentation. Digital intraoral scanners and cone-beam computed tomography (CBCT) now allow for detailed 3D reconstructions of dental arches. AI algorithms, particularly those employing deep learning and convolutional neural networks (CNNs), can automatically segment, quantify, and track changes in tooth structure with high accuracy. Comparative studies have demonstrated that AI-assisted digital assessments improve inter- and intra-examiner reliability and reduce diagnostic subjectivity, paving the way for evidence-based clinical decision-making.

Treatment & Management

The management of tooth wear is multifaceted, encompassing etiological identification, risk factor modification, preventive counseling, and restorative intervention when appropriate. Early-stage wear may be managed conservatively through dietary advice, occlusal splints, and behavioral modification. In more advanced cases, restorative procedures such as direct or indirect composite resins, onlays, or crowns may be required to restore function and esthetics. AI-driven analytics provide real-time feedback on wear progression, supporting personalized treatment planning and outcome prediction.

Recent Advances / Emerging Therapies

Recent advances in AI and digital dentistry have enabled the development of fully automated tooth wear progression analytics platforms. These systems integrate intraoral scanning, time-lapsed 3D imaging, and machine learning models for continuous monitoring and early detection of accelerated wear. AI can also be utilized for virtual simulation of restorative interventions and prediction of long-term outcomes based on individual risk profiles. Emerging research highlights the potential of AI to identify wear patterns associated with specific etiologies, facilitating precision medicine approaches in dental care.

Guideline Recommendations

Contemporary clinical guidelines advocate for the use of digital technologies in the assessment and management of tooth wear, with expert panels recommending integration of AI-based tools to enhance diagnostic accuracy and individualized care. Guidelines emphasize the importance of multidisciplinary collaboration, patient education, and regular follow-up utilizing objective digital metrics. Ongoing updates to dental practice standards are expected as further evidence on the utility of AI in tooth wear analytics emerges.

Conclusion

The incorporation of artificial intelligence into digital tooth wear progression analytics marks a significant advancement in dental diagnostics and patient care. AI-driven systems offer unprecedented precision, objectivity, and efficiency in the evaluation of tooth wear, supporting timely intervention and improved clinical outcomes. As technology continues to evolve, ongoing research and guideline development will be essential to maximize the benefits of AI while ensuring safe and ethical integration into routine dental practice.