Artificial Intelligence Models in Orthodontic Treatment Planning – A Systematic Review and Meta- Analysis

Abstract This review was conducted to evaluate effectiveness artificial intelligence (AI) models compared to traditional or conventional tracing techniques for an overall efficient orthodontic treatment planning. Review was adhered Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines 2020 and registered in PROSPERO – CRD42024618632. Electronic databases were searched for studies having adequate comparative data on effectiveness of AI models for orthodontic treatment planning and reported outcomes as cephalometric landmarks determination and mean segmentation time. Screening and data extraction was done by two independent reviewers. Quality assessment of studies was evaluated through Cochrane risk of bias (ROB)-2 tool. The standardized mean difference (SMD) was used as summary statistic measure with random effect model and p value <0.05 as statistically significant through Review manager (RevMan) version 5.3. Ten studies 1-10 were included in for qualitative synthesis and for meta-analysis. Included studies reported presence of low risk of bias overall. Effectiveness between both AI based models and conventional techniques was evaluated for determination of cephalometric landmarks and mean segmentation time. Meta-analysis revealed that determination of cephalometric landmarks (SMD: 2.28 (1.11 – 3.45, p<0.05) and mean segmentation time (SMD: 1.74 (0.77 – 2.72, p>0.05) was better with AI based models compared to conventional tracing method. Funnel plot did not show presence of publication bias in meta-analysis. It was found that AI based models had shown good efficiency, reliability with increased accuracy and can be used for initial screening and mid-treatment assessment. Results suggested that AI models overall can improve the accuracy of determining cephalometric landmarks and segmentation time and thus have an efficient role in orthodontic treatment planning.