Optimization of diagnostics and orthodontic treatment planning in children and adolescents with multiply adentia

[Cephalometric analysis of lateral skull X-ray images using soft computing components in the search for key points]

THE AIM OF THE STUDY

Was to investigate the efficiency of decoding teleradiological studies using an algorithm based on the use of convolutional neural networks - a simple convolutional architecture, as well as an extended U-Net architecture.

MATERIALS AND METHODS

For the experiment, a dataset was prepared by three orthodontists with over 10 years of clinical experience. Each of the orthodontists processed 100 X-ray images of the lateral projection of the head according to 27 parameters, 2700 measurements were made. The coordinates of the control points found by orthodontists in the images were compared with each other and a conclusion was made about the consistency of experts in the data obtained.

RESULTS

The results of convolutional neural network CNN were not satisfactory in 17 (62.96%) features, satisfactory in 10 (37.04%). The assessment of orthodontists resulted in non-satisfactory evaluation in 6 (22.22%), satisfactory in 8 (29.63%), good in 8 (29.63%), and excellent in 5 (18.52%) coordinates. Neural networks with U-Net architecture showed satisfactory results in 9 (33.3%) cases, good in 16 (59.3%) and excellent in 2 (7.4%) cases, with no non-satisfactory results.

CONCLUSION

The neural network of the U-Net architecture is more effective than a simple fully convolutional neural network and its results of determining anatomical reference points on two-dimensional images of the head are relatively comparable with the data obtained by medical specialists.