Prediction of patient cooperation before orthodontic treatment: Handwriting and artificial intelligence.

Abstract

Background: The purpose of this study was to compare the success of various convolutional neural network (CNN) models trained with handwriting samples in predicting patient cooperation.

Methods: A total of 237 (147 female and 90 male, mean age 14.94 ± 2.4) patients undergoing fixed orthodontic treatment were included in the study. In the 12th month of treatment, participants were divided into two groups based on the patient cooperation scale: cooperative or noncooperative. Then, for each patient, handwriting samples were obtained. Artificial neural network models were used to classify the patients as cooperative or noncooperative using the collected data. The accuracy, precision, recall, and F1-score values of nine different CNN models were compared.

Results: By overall success rate, InceptionResNetV2 (Accuracy: 72.0%, F1-score: 0.649) and NasNetMobil (Accuracy: 70.0%, F1-score: 0.417) were the two most effective CNN models. The two models with the lowest success rate were DenseNet121 (Accuracy: 59.0%, F1-score: 0.424) and ResNet50V2 (Accuracy: 46.0%, F1-score: 0.286). The success rates of the other five models were comparable.

Conclusions: The artificial intelligence models trained with handwriting samples are not sufficiently accurate for clinical application in cooperation prediction.