Maxillary crowding and spacing: validation of an artificial intelligence model vs. digitally assisted human observer

The aim of this study was to develop an artificial intelligence (AI) model capable of quantifying crowding and spacing in the upper arch and to validate its accuracy by comparing the model's results with those of manual interactive digital space analysis.

This study included intraoral photographs and occlusal scans of the upper dental arch of orthodontic patients treated at the University of Sharjah (2022-2024). The YOLO (You Only Look Once) 8 Pose Model was generated using a training and validation dataset (832 images). The AI model performed tooth segmentation and tooth point detection on the occlusal images, followed by automated quantification of tooth size-arch length discrepancy (TSALD). Manual space analysis was conducted using OrthoCAD (Cadent, Fairview, NJ, USA) software and the data were compared with the results of the AI model using a testing dataset (300 images). TSALD was categorized based on the index of treatment complexity, outcome, and need (ICON). Qualitative data were presented as frequency and distribution, and comparisons were performed by using Fisher's exact test. Correlation between manual and AI-measured TSALD was evaluated using Pearson's correlation coefficient.

The model achieved an overall accuracy of 90%. The largest discrepancies were found in cases presenting mild crowding (< 2 mm, 7%), severe spacing (5.1-9 mm, 5%), and moderate spacing (2.1-5 mm, 3.3%). A strong correlation (> 0.92) between manual and AI TSALD measurements indicated high reliability and potential interchangeability.

The AI model was successfully developed and validated, achieving 90% accuracy, demonstrating its potential as a reliable tool for quantifying TSALD in orthodontic diagnostics.