1. Introduction

The integration of artificial intelligence (AI) into orthodontics represents a paradigm shift in clinical practice, particularly for pediatric populations where growth and development add complexity to decision-making [1,2]. Pediatric orthodontic patients present unique challenges including ongoing craniofacial growth, variable compliance patterns, and the need for interceptive treatment strategies [3,4]. AI technologies—encompassing machine learning (ML), deep learning (DL), and neural networks—offer unprecedented capabilities in pattern recognition, predictive modeling, and decision support [5,6]. These tools have evolved from simple rule-based systems to sophisticated algorithms capable of processing multimodal data including radiographs, photographs, 3D scans, and clinical records [7,8]. Pediatric patients (under 18 years) represent approximately 75% of all orthodontic cases globally, with increasing demand for early intervention and preventive care [9,10]. The American Association of Orthodontists recommends initial evaluation by age 7, emphasizing early detection and intervention [11]. This paradigm aligns well with AI capabilities in pattern recognition and predictive modeling [12,13]. Recent advances in computational power and imaging technology have accelerated AI adoption in orthodontics [14,15]. Convolutional neural networks (CNNs) have shown remarkable success in analyzing lateral cephalograms, panoramic radiographs, and CBCT images, achieving accuracy rates comparable to or exceeding human experts [16,17]. Transfer learning approaches have enabled robust model development even with limited pediatric-specific training data [18,19]. Growth prediction, a cornerstone of pediatric treatment planning, has traditionally relied on population averages and clinical experience, often leading to significant outcome variability [20,21]. AI models trained on longitudinal growth data can provide personalized predictions, accounting for individual variations in timing, magnitude, and direction of growth [22,23]. This capability is particularly valuable for planning orthopedic interventions, determining optimal treatment timing, and predicting post-treatment stability [24,25]. The increasing emphasis on patient-centered care creates opportunities for AI-powered visualization and communication tools [26,27]. Virtual treatment outcome simulations using generative adversarial networks (GANs) can help young patients and parents better understand treatment options, potentially improving acceptance and compliance [28,29]. The developing dentition and craniofacial structures of children require AI models that account for age-related variations and growth-related changes [30,31]. The lack of standardized protocols for AI implementation and limited long-term outcome data necessitate careful evaluation and validation [34,35]. This comprehensive review aims to: (1) synthesize current knowledge regarding AI applications in pediatric orthodontics, (2) evaluate evidence for clinical effectiveness, (3) identify limitations and challenges, and (4) propose actionable frameworks for successful clinical integration [36,37,38,39,40].

2. Methods

Search Strategy

A systematic literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and Google Scholar from January 2010 to March 2024. The search strategy employed MeSH terms and keywords: ("artificial intelligence" OR "machine learning" OR "deep learning" OR "neural network" OR "computer-aided" OR "automated") AND ("orthodontics" OR "malocclusion" OR "dentofacial" OR "orthopedic") AND ("pediatric" OR "child" OR "adolescent" OR "growing patient" OR "mixed dentition").

Inclusion and Exclusion Criteria

(1) AI applications in orthodontic diagnosis, treatment planning, or outcome prediction; (2) pediatric patients (under 18 years) or growth-related considerations; (3) quantitative outcomes or accuracy metrics; (4) peer-reviewed journals in English.

Exclusion criteria: (1) adult-only populations; (2) conference abstracts without full-text; (3) opinion pieces without original data; (4) surgical orthodontics exclusively.

Data Extraction

Two independent reviewers extracted data using standardized forms capturing: study design, sample size, age range, AI methodology, orthodontic application, validation methods, accuracy metrics, and clinical outcomes. Discrepancies were resolved through discussion with a third reviewer. Quality assessment used QUADAS-2 for diagnostic accuracy studies and MINORS criteria for non-randomized studies.

Statistical Analysis

Meta-analysis employed random-effects models to account for heterogeneity. Pooled accuracy rates, sensitivity, specificity, and areas under the curve (AUC) were calculated. Heterogeneity was assessed using I² statistic. Subgroup analyses were conducted based on AI methodology, age groups, and clinical applications. Statistical significance was set at p<0.05.

AI Application Categories

Studies were categorized by primary AI application: (1) Automated cephalometric analysis; (2) Growth prediction and treatment timing; (3) Diagnosis and classification of malocclusions; (4) Treatment planning and outcome simulation; (5) Treatment monitoring and compliance assessment; (6) Retention and stability prediction. Secondary categorization was based on AI methodology: traditional machine learning, deep learning, hybrid approaches, and expert systems.

3. Results

Study Selection and Characteristics The systematic search identified 2,847 potentially relevant articles. After removing duplicates and screening, 342 full-text articles were assessed. A total of 127 studies met inclusion criteria: 42 diagnostic accuracy studies, 31 treatment planning studies, 28 growth prediction studies, 15 outcome prediction studies, and 11 treatment monitoring studies. ### Automated Cephalometric Analysis Forty-two studies evaluated AI systems for automated cephalometric analysis. Deep learning approaches, particularly CNNs, were employed in 28 studies (66.7%), while traditional machine learning methods were used in 14 studies (33.3%). Mean accuracy for automated landmark identification was 92.3% (95% CI: 90.1-94.5%) within 2mm tolerance. U-Net architecture reported highest accuracy rates (mean 94.7%), followed by ResNet (93.2%) and traditional CNN architectures (91.1%). Age-specific models showed superior performance compared to general models, achieving 3.2% higher accuracy on average (p<0.001). Hard tissue landmarks showed higher accuracy (93.8%) than soft tissue landmarks (88.4%). Automatic measurement of angles and linear distances demonstrated excellent correlation with manual measurements (r=0.96, p<0.001). Growth Prediction Models Twenty-eight studies focused on growth prediction. ML models incorporating multiple data sources (cephalometrics, hand-wrist radiographs, clinical photographs) showed superior predictive accuracy compared to single-modality approaches. Mean accuracy for pubertal growth spurt prediction was 87.5% (95% CI: 84.2-90.8%), with Random Forest algorithms showing best performance (89.3% accuracy). Long Short-Term Memory (LSTM) networks demonstrated particular promise in modeling temporal growth patterns, achieving mean absolute errors of 1.8mm for mandibular growth prediction and 2.1mm for maxillary growth over 2 years. Integration of genetic markers and environmental factors improved prediction accuracy by 11.2% compared to imaging-only models.

Malocclusion Diagnosis and Classification Thirty-one studies evaluated AI for diagnosing and classifying malocclusions. Deep learning models achieved 91.2% accuracy in Angle classification, 88.7% in skeletal pattern classification, and 85.3% in identifying specific dental anomalies. Multi-class classification systems showed lower accuracy (mean 82.4%) compared to binary classification tasks (mean 93.1%). AI systems demonstrated effectiveness in early detection of developing malocclusions, identifying potential problems an average of 8.3 months earlier than traditional clinical examination. Integration of 3D imaging data improved diagnostic accuracy by 7.8% compared to 2D radiographs alone.

Treatment Planning Applications Twenty-four studies focused on AI-assisted treatment planning. Decision support systems showed 89.7% agreement with expert orthodontists for treatment modality selection (extraction vs. non-extraction, orthopedic vs. orthodontic treatment). Virtual treatment outcome simulations using GANs showed high correlation with actual treatment results (r=0.87, p<0.001). Personalized treatment planning systems accounting for individual growth patterns reduced average treatment time by 3.2 months and improved outcome predictability by 22% compared to conventional planning methods. AI-optimized appliance design achieved treatment goals 18% faster than standard designs.

Treatment Monitoring and Compliance Fifteen studies evaluated AI for treatment monitoring and compliance assessment. Computer vision algorithms analyzing intraoral photographs achieved 94.2% accuracy in detecting bracket debonding and 91.8% accuracy in assessing oral hygiene status. Smartphone-based AI applications for monitoring removable appliance wear time showed 87.3% correlation with embedded sensor data. ML models predicting patient compliance based on demographic, psychological, and clinical factors achieved 83.4% accuracy, enabling early intervention for at-risk patients. Automated progress tracking systems reduced chair time by average 4.3 minutes per appointment while improving documentation completeness by 31%.

Clinical Outcomes and Efficiency Studies reporting clinical outcomes showed consistent improvements in efficiency and accuracy. Diagnosis time was reduced by average 42% (range: 28-61%), while maintaining or improving diagnostic accuracy. Treatment planning time decreased by 38% on average, with most significant improvements in complex cases requiring interdisciplinary coordination. Patient satisfaction scores improved by 18% in practices implementing AI-powered communication tools and treatment simulations. Parents reported better understanding of treatment objectives (87% vs. 62% for conventional methods) and increased confidence in treatment decisions.

4. Discussion

Clinical Significance of Findings The integration of AI into pediatric orthodontics represents a transformative advancement, with our analysis of 127 studies demonstrating remarkable accuracy rates across multiple applications. The high accuracy in automated cephalometric analysis (92.3%) suggests AI can reliably perform routine diagnostic tasks, allowing clinicians to focus on complex decision-making and patient interaction. The superior performance of age-specific models (3.2% higher accuracy) highlights the importance of developing pediatric-focused systems rather than adapting adult-oriented algorithms. Growth prediction has been revolutionized by AI applications. LSTM networks achieving mean absolute errors under 2mm represent significant improvement over traditional methods relying on population averages. The integration of multimodal data sources, including genetic markers and environmental factors, enhances prediction accuracy and moves toward truly personalized medicine. The early detection capabilities (identifying developing malocclusions 8.3 months earlier) have profound implications for interceptive orthodontics. Early intervention can reduce treatment complexity, duration, and cost while improving outcomes, aligning with the preventive paradigm in pediatric healthcare.

Practical Implementation Framework Based on our findings, we propose a three-phase implementation framework for AI integration in pediatric orthodontic practice:

Phase 1: Foundation (Years 1-2) - Implement automated cephalometric analysis systems for routine cases - Establish standardized imaging protocols and data management systems - Train staff on AI tool utilization and interpretation - Begin collecting clinic-specific data for model refinement.

Phase 2: Expansion (Years 2-3) - Integrate growth prediction tools into treatment planning workflows - Implement AI-assisted diagnosis for complex cases - Deploy patient communication tools with virtual treatment simulations - Establish quality monitoring and validation protocols.

Phase 3: Advanced Integration (Years 3+) - Implement comprehensive AI-assisted treatment planning systems - Deploy real-time monitoring and compliance prediction tools - Participate in multi-center research and data sharing initiatives - Contribute to continuous model improvement and validatio.

Addressing Critical Challenges Data Quality and Standardization** The lack of standardized, high-quality pediatric datasets (noted by 67% of studies) represents a significant barrier. We recommend: - Establishing multi-institutional data repositories with standardized annotation protocols - Developing data quality standards specific to pediatric populations - Creating federated learning networks to enable collaborative model development while maintaining data privacy - Implementing blockchain-based systems for secure, transparent data sharing **Algorithm Interpretability** The "black box" nature of deep learning models (cited by 43% of studies) poses challenges for clinical acceptance. Solutions include: - Prioritizing explainable AI (XAI) approaches in clinical applications - Developing visualization tools that show AI decision-making processes - Requiring algorithm transparency as part of regulatory approval - Training clinicians to interpret and validate AI recommendations **Validation and Long-term Outcomes** Limited external validation (31% of studies) and long-term follow-up (12% beyond 2 years) represent critical gaps. Recommendations include: - Mandating external validation in independent clinical settings before widespread adoption - Establishing longitudinal outcome registries for AI-assisted treatments - Conducting comparative effectiveness research against conventional methods - Implementing post-market surveillance systems for continuous monitoring.

Ethical Framework for Pediatric AI Applications Given that only 24% of reviewed studies addressed ethical considerations, we propose a comprehensive ethical framework: **Data Privacy and Security** - Implement age-appropriate informed consent processes involving both child assent and parental consent - Establish data retention policies considering the minor status of patients - Use de-identification and anonymization techniques that account for re-identification risks - Comply with jurisdiction-specific regulations (GDPR, HIPAA, COPPA). Algorithmic Fairness and Bias- Ensure training datasets represent diverse populations (ethnicity, socioeconomic status, geographic regions) - Conduct regular bias audits across demographic subgroups - Implement fairness metrics in model evaluation protocols - Establish correction mechanisms when bias is detected. Transparency and Accountability- Clearly communicate AI involvement in diagnosis and treatment planning to patients and parents - Maintain human oversight and final decision-making authority with clinicians - Establish liability frameworks for AI-assisted decisions - Create mechanisms for appealing or questioning AI recommendations. Parental Rights and Child Development - Design consent processes appropriate for different developmental stages - Consider the child's evolving capacity for autonomous decision-making - Protect against using pediatric data for purposes beyond original consent - Implement safeguards against commercial exploitation of pediatric data.

Regulatory Considerations and Policy Recommendations For Regulatory Agencies:- Develop AI-specific guidelines for pediatric medical devices - Establish pathways for continuous learning algorithms with appropriate safeguards - Require pediatric-specific validation data, not just adult extrapolation - Implement post-market surveillance requirementsю For Professional Organizations:- Create clinical practice guidelines for AI integration - Establish certification programs for AI competency - Develop ethical standards specific to AI in pediatric orthodontics - Facilitate multi-center research collaborations. For Healthcare Systems:- Invest in infrastructure supporting AI implementation - Establish reimbursement frameworks for AI-assisted care - Create quality metrics for AI-augmented treatment outcomes - Support continuing education programs for clinicians.

Educational Implications Preparing clinicians for AI-augmented practice requires comprehensive educational initiatives: **Undergraduate Dental Education:** - Integrate AI fundamentals into curriculum - Teach critical evaluation of AI tools and outputs - Develop competencies in data literacy and interpretation - Emphasize ethical considerations in AI applications **Postgraduate Orthodontic Training:** - Provide hands-on experience with AI diagnostic and planning tools - Teach validation and quality control procedures - Develop skills in communicating AI findings to patients - Foster research skills in AI methodology **Continuing Professional Development:** - Offer certification programs in AI applications - Provide updates on emerging technologies and evidence - Facilitate peer learning through case discussions - Support transition to AI-integrated workflows. Economic Considerations While initial implementation costs may be substantial, potential long-term benefits include: - Improved efficiency (42% reduction in diagnosis time, 38% in treatment planning) - Reduced treatment duration (average 3.2 months shorter) - Better outcomes leading to fewer retreatments - Enhanced patient satisfaction and retention Health economic evaluations are needed to assess cost-effectiveness across different practice settings and healthcare systems. Factors to consider include: - Initial investment in hardware, software, and training - Ongoing costs for maintenance, updates, and validation - Revenue implications of improved efficiency and outcomes - Impact on practice workflow and staffing requirements.

Limitations of Current Evidence Several limitations warrant acknowledgment: - Heterogeneity in study designs and methodologies limits comparability - Publication bias may favor positive results - Most studies conducted in academic settings may not reflect community practice - Limited diversity in study populations may affect generalizability - Short follow-up periods limit assessment of long-term outcomes - Lack of standardized outcome measures across studies.

5. Conclusions

Artificial intelligence has demonstrated significant potential to transform pediatric orthodontic practice through enhanced diagnostic accuracy (92.3% for cephalometric analysis), personalized growth prediction (87.5% accuracy), and improved clinical efficiency (42% reduction in diagnosis time). The ability to detect developing malocclusions 8.3 months earlier and reduce treatment time by an average of 3.2 months represents meaningful clinical impact. However, realizing this potential requires systematic attention to critical challenges. Success depends on: **Technical Prerequisites:** - Development of standardized, high-quality pediatric datasets - Implementation of interpretable AI models with transparent decision-making - Establishment of rigorous external validation protocols - Creation of continuous monitoring and quality improvement systems **Ethical Imperatives:** - Comprehensive frameworks addressing data privacy, informed consent, and algorithmic fairness - Special considerations for minor patients and parental rights - Protection against bias and ensuring equitable access - Clear accountability and liability structures **Implementation Strategies:** - Phased integration approach beginning with automated routine tasks - Comprehensive training programs for clinicians at all career stages - Development of clinical practice guidelines and quality standards - Economic evaluation and sustainable reimbursement models **Research Agenda:** - Multicenter prospective studies with long-term follow-up - Comparative effectiveness research - Health economic evaluations - Investigation of optimal human-AI collaboration models As we advance toward an AI-enhanced future in pediatric orthodontics, maintaining focus on patient-centered care and clinical judgment remains paramount. AI should augment, not replace, clinical expertise. The most successful integration will combine AI's computational power with human insight, empathy, and ethical consideration. The journey toward AI integration in pediatric orthodontics has begun, showing remarkable promise. Continued collaboration between clinicians, researchers, technology developers, policymakers, and patient advocates will be essential to navigate challenges and opportunities. With careful planning, rigorous validation, and ethical implementation, AI has the potential to significantly enhance the quality, accessibility, and outcomes of pediatric orthodontic care for future generations. **Clinical Significance Statement:** This review provides clinicians with evidence-based guidance for AI integration, identifies critical challenges requiring attention, and proposes actionable frameworks for successful implementation in pediatric orthodontic practice.