Artificial Intelligence in Orthodontics: Evaluating Diagnostic Accuracy and Treatment Planning Efficiency
DOI:
https://doi.org/10.52783/jns.v14.2297Keywords:
AI model, diagnostic accuracy, Treatment Planning, OrthodonticsAbstract
Background: The integration of artificial intelligence (AI) in orthodontics promises to enhance diagnostic accuracy and optimize treatment planning. However, its clinical efficacy and reliability remain areas of active investigation. This study evaluates the diagnostic accuracy and treatment planning efficiency of an AI model compared to experienced orthodontists.
Objective: To assess the diagnostic accuracy, treatment consistency, and practical applicability of an AI-powered orthodontic tool in identifying malocclusions and formulating treatment plans.
Methods: A dataset of 200 orthodontic cases, including panoramic radiographs and cephalometric images, was analyzed. The AI model's diagnostic performance was compared against a panel of three orthodontic experts. Sensitivity, specificity, and inter-rater reliability (Cohen’s kappa coefficient) were measured. AI-generated treatment plans were evaluated for adherence to clinical guidelines and validated through a preliminary follow-up of 50 patients. Qualitative feedback from orthodontists and patients was also collected.
Results: Diagnostic Accuracy Sensitivity: 92%, Specificity: 88%.Cohen’s kappa coefficient: 0.85, indicating strong agreement with orthodontists.
Treatment Planning: AI recommendations adhered to clinical guidelines in 94% of cases. A follow-up study showed a 90% success rate for AI-guided treatments.
Qualitative Findings: Orthodontists appreciated AI’s efficiency but raised concerns about its limitations in managing complex cases. Patients viewed AI as a beneficial supplementary tool, emphasizing the importance of clinician involvement.
Conclusion: The AI model demonstrated high diagnostic accuracy and efficiency in treatment planning, matching expert-level performance in standard cases. While promising, the tool's reliance on structured cases and its limitations in complex scenarios highlight the continued need for clinician oversight. Future research should focus on refining AI for broader clinical applicability and developing frameworks for seamless integration into orthodontic practice.
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