# Machine Learning for Child Oral Health: A Scoping Review
## 1 Introduction  
Oral health is essential to overall well-being, influencing daily activities like eating, speaking, social interactions, and appearance [1]. Recognizing its profound effect on individuals' lives, the World Health Organization has listed oral health as a major global public health concern [2]. Dental caries affect 60-90% of children globally and nearly all adults, while about 30% of individuals aged 65-74 have lost all their natural teeth [3]. One of the primary objectives in managing dental caries and other oral diseases is for practitioners to rapidly assess disease severity and select optimal treatments based on individual patient needs. Early intervention is essential to minimize patient discomfort and prevent further oral health deterioration. Traditional medical imaging techniques, such as X-rays for localized examinations and CT scans for panoramic views, are crucial in diagnostic evaluations, offering dentists detailed visual insights into oral structures [4, 5]. In addition to these, modern imaging tools, including intraoral cameras [6, 7] and optical coherence tomography [8, 9], provide real-time, non-invasive views of intraoral surfaces and tissues, aiding in early detection and ongoing monitoring of treatment effectiveness. Artificial intelligence (AI) has become an essential tool in enhancing these diagnostic capabilities [10]. Within AI, machine learning (ML) plays a particularly significant role. ML refers to algorithms that allow computers to analyze large datasets, identify patterns, and continually improve their predictive capabilities through experience, without being explicitly programmed [11]. It generalizes from training data to make predictions on new inputs, adapting as new data become available. In the context of dentistry, ML's ability toprocess and analyze vast amounts of complex data, such as patient records, radiographs, clinical images, and procedural information, makes it an indispensable tool for diagnostic insights, treatment planning, and predictive modeling [12]. In pediatric dentistry, the early detection and management of conditions like early childhood caries (ECC), malocclusion, dental trauma, and periodontal diseases are crucial. ECC, a significant public health issue in children under six years of age, can have long-term impacts on oral and systemic health if left untreated. Malocclusion, a misalignment of teeth or jaws, can affect aesthetics, speech, and function, often requiring early orthodontic intervention. Similarly, dental trauma, prevalent in active young children, necessitates prompt diagnosis and management to prevent complications like tooth loss or pulp necrosis. Periodontal conditions, though less common in children than adults, can manifest as gingivitis or aggressive periodontitis, particularly in specific high-risk groups, and demand early identification to ensure effective management. Traditional diagnostic methods, often relying on visual-tactile examinations and radiography, may not provide comprehensive insights or predictive capabilities for these diverse conditions. ML offers transformative opportunities to enhance diagnostic precision, improve risk stratification, and tailor treatment plans across various pediatric oral health concerns. For instance, ML models can integrate data from intraoral imaging, radiographs, and patient records to predict orthodontic treatment needs, assess trauma severity, and monitor periodontal health [13]. These capabilities enable earlier interventions, personalized care strategies, and better long-term outcomes for pediatric patients. ML applications have expanded to different dental specialties, offering diagnostic and prognostic tools that enhance clinical decision making [14]. Numerous studies [15; 16; 17; 18] have employed ML models to predict risk factors, with some focusing on pediatric populations, while others, like [PERSON] et al. [18], explored root caries prediction in adults.