Accuracy and Efficiency of Artificial Intelligence-Driven Treatment Planning in Clear Aligner Therapy: A Comparative Study with Conventional Methods in Bandung, Indonesia

Abstract

Introduction: Clear aligner therapy (CAT) has gained popularity as an esthetic alternative to traditional braces. Artificial intelligence (AI) is increasingly being integrated into CAT treatment planning, promising improved accuracy and efficiency. This study aimed to compare the accuracy and efficiency of AI-driven treatment planning with conventional methods in Bandung, Indonesia.

Methods: A retrospective study was conducted involving 100 patients treated with CAT in Bandung. Fifty patients were treated using conventional methods (CM) by experienced orthodontists, while the other 50 were planned with AI-driven software. Accuracy was assessed by comparing the planned tooth movement with the actual outcome using Little's Irregularity Index (LII) and Peer Assessment Rating (PAR) scores at the end of treatment. Efficiency was evaluated by comparing the time required for treatment planning and the number of refinements needed.

Results: The AI-driven group demonstrated significantly lower LII scores (p<0.05) and higher PAR scores (p<0.05) compared to the CM group, indicating greater accuracy in achieving the planned tooth movement. Additionally, the AI-driven group showed a significant reduction in treatment planning time (p<0.05) and fewer refinement aligners required (p<0.05) compared to the CM group.

Conclusion: AI-driven treatment planning in CAT demonstrated superior accuracy and efficiency compared to conventional methods in Bandung, Indonesia. AI has the potential to optimize treatment outcomes and reduce treatment time, offering a valuable tool for orthodontists.