ArticleClinical and experimental dental research2026
Applying Automated Artificial Intelligence Models on Lateral Cephalometric Parameters to Accurately Classify Arab Orthodontic Patient Patterns.
Article in Clinical and experimental dental research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectivesMalocclusion and the treatment process can affect the person's quality of life, which includes physical and psychological effects. Classifying the patient accurately is critical to achieving the desired output. This study aimed to improve the current diagnostic process with artificial-intelligence algorithms using the lateral cephalogram. MATERIALS AND
methodsThe study sample consisted of 1014 Arab patients diagnosed with skeletal class I, II, or III. In this study, we used linear discriminant analysis (LDA), random forest (RF), decision tree (DT), K-nearest neighbors (KNN), support vector machine (SVM), and naive Bayes (NB) as classification models. In addition, we calculated the parameters' importance using two techniques-the impurity decreases and the leave-one-feature-out (LOFO) technique. Finally, we applied an artificial neural network (ANN) to classify the patients accurately.
resultsOne of the influential models presented in this study was the model that included only the parameters Wits appraisal, SNB, SNA, and ML-NSL angles. It could classify the patients with an accuracy of 0.98. In addition, we applied the leave-one-feature-out technique (LOFO) for multiple random forest models and found that the Calculated_ANB (ANB angle-individualized ANB) and Wits appraisal were the most important parameters in the random forest models. Besides, age and gender were in 8th and 21st places (out of 26 variables). Furthermore, the decision tree results demonstrated the distinct characteristics of this ethnic group, which were presented by different ranges of ANB angles that define every skeletal class. The results showed that the tree's root classified the patient as skeletal class III when the ANB angle is less than 0.084 degrees, and skeletal class II if the ANB angle is greater than 1.23.
conclusionsIn summary, this research presented a model enabling orthodontists to precisely classify orthodontic patients. Further research should include different ethnic groups to validate our findings.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.