Artificial intelligence-based algorithm for cervical vertebrae maturation stage assessment.

OBJECTIVE

The aim of this study was to develop an artificial intelligence (AI) algorithm to automatically and accurately determine the stage of cervical vertebra maturation (CVM) with the main purpose being to eliminate the human error factor.

SETTING AND SAMPLE POPULATION

Archives of the cephalometric images were reviewed and the data of 1501 subjects with fully visible cervical vertebras were included in this retrospective study.

MATERIALS AND METHODS

Lateral cephalometric (LC) that met the inclusion criteria were used in the training process, labeling was carried out using a computer vision annotation tool (CVAT), tracing was done by an experienced orthodontist as a gold standard and, in order to limit the effect of the uneven distribution of the training data set, maturation stage was classified with a modified Bachetti method by the operator who labelled them. The labelled data were split randomly into a training set (80%), a testing set (10%) and an validation set (10%), to measure intra-observer, inter-observer reliability, intraclass correlation coefficient (ICC) and weighted Cohen's kappa test was carried out.

RESULTS

The ICC was valued at 0.973, weighted Cohen's kappa standard error was 0.870 ± 0.027 which shows high reliability of the observers and excellent level of agreement between them, the segmentation network achieved a global accuracy of 0.99 and the average dice score overall images was 0.93. The classification network achieved an accuracy of 0.802, class sensitivity of (pre-pubertal 0.78; pubertal 0.45; post-pubertal 0.98), respectively, per class specificity of (pre-pubertal 0.94; pubertal 0.94; post-pubertal 0.75), respectively.

CONCLUSION

The developed algorithm showed the ability to determine the cervical vertebrae maturation stage which might aid in a faster diagnosis process by eliminating human intervention, which might lead to wrong decision-making procedures that might affect the outcome of the treatment plan. The developed algorithm proved reliable in determining the pre-pubertal and post-pubertal growth stages with high accuracy.