Prediction of Fishman's skeletal maturity indicators using artificial intelligence

Determination of growth and developmental stages in hand-wrist radiographs : Can fractal analysis in combination with artificial intelligence be used?

PURPOSE

The goal of this work was to assess the classification of maturation stage using artificial intelligence (AI) classifiers.

METHODS

Hand-wrist radiographs (HWRs) from 1067 individuals aged between 7 and 18 years were included. Fifteen regions of interest were selected for fractal dimension (FD) analysis. Five predictive models with different inputs were created (model 1: only FD; model 2: FD and Chapman sesamoid stage; model 3: FD, age, and sex; model 4: FD, Chapman sesamoid stage, age, and sex; model 5: Chapman sesamoid stage, age, and sex). The target diagnoses were accelerating growth velocity, very high growth velocity, and decreasing growth velocity. Four AI algorithms were applied: multilayer perceptron (MLP), support vector machine (SVM), gradient boosting machine (GBM) and C 5.0 decision tree classifier.

RESULTS

All AI algorithms except for C 5.0 yielded similar overall predictive accuracies for the five models. In order from lowest to highest, the predictive accuracies of the models were as follows: model 1 < model 3 < model 2 < model 5 < model 4. The highest overall F1 score, which was used instead of accuracy especially for models with unbalanced data, was obtained for models 1, 2, and 3 based on SVM, for model 4 based on MLP, and for model 5 based on C 5.0. Adding Chapman sesamoid stage, chronologic age, and sex as additional inputs to the FD values significantly increased the F1 score.

CONCLUSION

Applying FD analysis to HWRs is not sufficient to predict maturation stage in growing patients but can be considered a growth rate prediction method if combined with the Chapman sesamoid stage, age, and sex.

Artificial Intelligence and Its Clinical Applications in Orthodontics: A Systematic Review

This review aims to analyze different strategies that make use of artificial intelligence to enhance diagnosis, treatment planning, and monitoring in orthodontics. Orthodontics has seen significant technological advancements with the introduction of digital equipment, including cone beam computed tomography, intraoral scanners, and software coupled to these devices. The use of deep learning in software has sped up image processing processes. Deep learning is an artificial intelligence technology that trains computers to analyze data like the human brain does. Deep learning models are capable of recognizing complex patterns in photos, text, audio, and other data to generate accurate information and predictions.

MATERIALS AND METHODS

Pubmed, Scopus, and Web of Science were used to discover publications from 1 January 2013 to 18 October 2023 that matched our topic. A comparison of various artificial intelligence applications in orthodontics was generated.

RESULTS

A final number of 33 studies were included in the review for qualitative analysis.

CONCLUSIONS

These studies demonstrate the effectiveness of AI in enhancing orthodontic diagnosis, treatment planning, and assessment. A lot of articles emphasize the integration of artificial intelligence into orthodontics and its potential to revolutionize treatment monitoring, evaluation, and patient outcomes.

Long-Term Assessment of Treatment Timing for Rapid Maxillary Expansion and Facemask Therapy Followed by Fixed Appliances: A Multicenter Retro-Prospective Study

BACKGROUND

to determine the role of treatment timing in the long-term effects produced by rapid maxillary expansion and facemask therapy (RME/FM) in Class III patients.

METHODS

This study compared two sample groups treated with RME/FM followed by fixed appliances: the early prepubertal group (EPG) (17 patients; mean age before treatment (T0), 5.8 ± 0.7 years; range, 4.3-6.9 years) and the late prepubertal group (LPG) (17 patients; mean age at T0, 10.1 ± 0.8 years; range, 9.0-11.1 years). Lateral cephalograms for the two groups were examined before treatment (T0) and at a long-term observation (T1) (EPG, 19.8 ± 1.0 years; LPG, 21.0 ± 2.1 years). Independent sample t-tests were performed to compare the two groups at T0 and T1.

RESULTS

No statistically significant differences were found for any of the cephalometric variables at T0, except for the total mandibular length, overjet, and inclination of the maxillary incisors to the palatal plane, which were greater in the LPG. At T1, no statistically significant differences were detected for any of the cephalometric variables.

CONCLUSIONS

There were no significant long-term differences when treating Class III patients with RME/FM, either during an early prepubertal phase (≤7 years of age) or during a late prepubertal phase (≥9 years of age).

Application of Artificial Intelligence in Orthodontics: Current State and Future Perspectives

In recent years, there has been the notable emergency of artificial intelligence (AI) as a transformative force in multiple domains, including orthodontics. This review aims to provide a comprehensive overview of the present state of AI applications in orthodontics, which can be categorized into the following domains: (1) diagnosis, including cephalometric analysis, dental analysis, facial analysis, skeletal-maturation-stage determination and upper-airway obstruction assessment; (2) treatment planning, including decision making for extractions and orthognathic surgery, and treatment outcome prediction; and (3) clinical practice, including practice guidance, remote care, and clinical documentation. We have witnessed a broadening of the application of AI in orthodontics, accompanied by advancements in its performance. Additionally, this review outlines the existing limitations within the field and offers future perspectives.