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@article{186231,
        author = {Sonika Reddy Pilli and Shrika Reddy Pilli and Utkarsh Gupta and Shivani Shakti Rao},
        title = {Artificial Intelligence in the Prediction of Orthodontic Treatment Outcomes: A Comprehensive Systematic Review and Literature Analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {6},
        pages = {247-260},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=186231},
        abstract = {Background: Artificial intelligence (AI) has emerged as a transformative technology in orthodontics, offering unprecedented capabilities for predicting treatment outcomes and optimizing clinical decision-making. The integration of machine learning algorithms, deep learning networks, and computer vision techniques has revolutionized traditional approaches to treatment planning and outcome prediction.
Objective: To systematically review and analyze the current applications of artificial intelligence in predicting orthodontic treatment outcomes, with specific focus on treatment planning, cephalometric landmark detection, tooth movement prediction, treatment duration estimation, and post-treatment stability assessment.
Methods: A comprehensive systematic review was conducted following PRISMA guidelines across multiple databases including PubMed, Scopus, Web of Science, and IEEE Xplore from 2015 to 2024. Studies were included if they investigated AI applications in orthodontic treatment outcome prediction. Data extraction focused on AI methodologies, clinical applications, performance metrics, and predictive accuracy.
Results: A total of 127 studies met the inclusion criteria, encompassing various AI approaches including convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and ensemble methods. Key applications identified included: (1) Cephalometric landmark detection with accuracy rates of 85-98%, (2) Treatment duration prediction with mean absolute errors ranging from 2.3-8.7 months, (3) Tooth movement prediction achieving correlation coefficients of 0.78-0.94, (4) Treatment planning optimization with success rates of 82-96%, and (5) post-treatment stability assessment with prediction accuracies of 79-91%. Deep learning approaches consistently outperformed traditional statistical methods across all applications.
Conclusions: AI demonstrates significant potential for enhancing orthodontic treatment outcome prediction across multiple clinical domains. While current applications show promising results, standardization of methodologies, larger multicenter datasets, and clinical validation studies are needed for widespread clinical implementation. Future research should focus on developing interpretable AI models, addressing ethical considerations, and establishing regulatory frameworks for clinical deployment.},
        keywords = {Artificial intelligence, machine learning, orthodontics, treatment prediction, cephalometric analysis, tooth movement, treatment planning, deep learning},
        month = {October},
        }