AI-driven approaches in the management of early childhood caries: A path toward global oral health

Q1 Medicine

Journal of oral biology and craniofacial research Pub Date : 2025-07-29 DOI:10.1016/j.jobcr.2025.07.022

Prajna P. Nayak, Vabitha Shetty, Shreya S, Liza Zacharias, Isha Gore

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Abstract

Background

The integration of Artificial Intelligence (AI) with paediatric dentistry has unveiled transformative possibilities, particularly in mitigating the global burden of a prevalent yet preventable oral health issue, namely early childhood caries (ECC). ECC affects millions of children worldwide, leading to significant health, developmental, and economic challenges. This paper explores the application of AI-driven technologies, including machine learning and deep learning, in the detection, diagnosis, risk assessment, and management of ECC.

Brief summary

AI models leveraging dental radiographs and intraoral photographs have demonstrated high accuracy in caries detection, while predictive algorithms facilitate the identification of high-risk groups using patient demographics, behavioural data, and even genetic markers. Smartphone applications equipped with AI capabilities, such as AICaries, empower caregivers with tools for at-home caries screening, enhancing accessibility and fostering preventive care.

Today, AI's role extends to optimizing healthcare utilization patterns and advancing personalized treatment strategies, particularly in underserved regions where traditional resources are scarce. Efforts to develop diverse training datasets have not eliminated biases leading to concerns about fairness, discrimination and privacy. Further, unregulated AI applications may worsen rather than reduce health disparities.

Implications for future research

This review underscores the potential of AI to revolutionize ECC prevention and management, paving the way for equitable oral healthcare globally. It advocates for further interdisciplinary research to refine AI tools, address practical challenges, and support the development of evidence-based policies for widespread implementation. Ultimately, AI emerges as a pivotal advancement in transitioning from disease management to proactive oral health care strategies.

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人工智能驱动的早期儿童龋齿管理方法：实现全球口腔健康的途径

人工智能（AI）与儿科牙科的结合揭示了变革性的可能性，特别是在减轻儿童早期龋齿（ECC）这一普遍但可预防的口腔健康问题的全球负担方面。ECC影响着全世界数百万儿童，导致重大的健康、发展和经济挑战。本文探讨了人工智能驱动技术在ECC检测、诊断、风险评估和管理中的应用，包括机器学习和深度学习。利用牙科x光片和口腔内照片的人工智能模型在龋齿检测方面显示出很高的准确性，而预测算法则利用患者人口统计学、行为数据甚至遗传标记促进识别高风险人群。配备人工智能功能的智能手机应用程序，如icaries，为护理人员提供了在家进行龋齿筛查的工具，提高了可及性并促进了预防保健。今天，人工智能的作用扩展到优化医疗保健利用模式和推进个性化治疗策略，特别是在传统资源稀缺的服务不足地区。开发多样化训练数据集的努力并没有消除导致对公平、歧视和隐私的担忧的偏见。此外，不受管制的人工智能应用可能会加剧而不是减少健康差距。本综述强调了人工智能在改变ECC预防和管理方面的潜力，为全球公平的口腔保健铺平了道路。它倡导进一步开展跨学科研究，以完善人工智能工具，应对实际挑战，并支持制定以证据为基础的政策，以便广泛实施。最终，人工智能成为从疾病管理向主动口腔保健战略过渡的关键进步。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of oral biology and craniofacial research Medicine-Otorhinolaryngology

CiteScore

4.90

自引率

0.00%

发文量

133

审稿时长

167 days

期刊介绍： Journal of Oral Biology and Craniofacial Research (JOBCR)is the official journal of the Craniofacial Research Foundation (CRF). The journal aims to provide a common platform for both clinical and translational research and to promote interdisciplinary sciences in craniofacial region. JOBCR publishes content that includes diseases, injuries and defects in the head, neck, face, jaws and the hard and soft tissues of the mouth and jaws and face region; diagnosis and medical management of diseases specific to the orofacial tissues and of oral manifestations of systemic diseases; studies on identifying populations at risk of oral disease or in need of specific care, and comparing regional, environmental, social, and access similarities and differences in dental care between populations; diseases of the mouth and related structures like salivary glands, temporomandibular joints, facial muscles and perioral skin; biomedical engineering, tissue engineering and stem cells. The journal publishes reviews, commentaries, peer-reviewed original research articles, short communication, and case reports.