Quantitative tooth crowding analysis in occlusal intra-oral photographs using a convolutional neural network.

Abstract

Background: Dental crowding is a primary concern in orthodontic treatment and significantly impacts therapy choices. Accurate quantification of crowding requires time-intensive cast- or scan-based measurements. The aim was to develop an automated deep-learning model capable of assessing anterior crowding and calculating the Little Irregularity Index using single occlusal intra-oral photographs.

Methods: A dataset of 125 untreated individuals (100 from Zurich, Switzerland, and 25 from Nijmegen, the Netherlands) comprised of annotated intra-oral scans and corresponding intra-oral photographs were used to train a dedicated convolutional neural network (CNN). The CNN was modeled to detect teeth boundaries, contact points and contact point displacements on photographs. The model's performance to determine anterior crowding and the Little Irregularity Index score was compared to consensus measurements based on intra-oral scans in terms of intra-class correlation (ICC) and mean absolute difference (MAD).

Results: The model correlated well with the consensus measurement, and proved to be reliable (ICC = 0.900) and accurate (MAD = 0.36 mm) for anterior crowding assessment and Little Irregularity Index alike (ICC = 0.930; MAD = 0.74 mm).

Limitation: The model was not trained on cases with interdental spacing, and its reliability for cases with crowding severity outside the tested sample has not been established.

Conclusion: The presented CNN-based model was able to quantify the crowding in the anterior segment of the lower dental arch and score the Little Irregularity Index from a single intra-oral photograph with a satisfactory reliability and accuracy. Application of this model may lead to more efficient and convenient orthodontic diagnostics.