Purpose
This study developed a convolutional neural network (CNN) model to diagnose maxillary sinusitis on panoramic radiographs (PRs) and cone-beam computed tomographic (CBCT) images and evaluated its performance.
Materials and Methods
A CNN model, which is an artificial intelligence method, was utilized. The model was trained and tested by applying 5-fold cross-validation to a dataset of 148 healthy and 148 inflamed sinus images. The CNN model was implemented using the PyTorch library of the Python programming language. A receiver operating characteristic curve was plotted, and the area under the curve, accuracy, sensitivity, specificity, positive predictive value, and negative predictive values for both imaging techniques were calculated to evaluate the model.
Results
The average accuracy, sensitivity, and specificity of the model in diagnosing sinusitis from PRs were 75.7%, 75.7%, and 75.7%, respectively. The accuracy, sensitivity, and specificity of the deep-learning system in diagnosing sinusitis from CBCT images were 99.7%, 100%, and 99.3%, respectively.
Conclusion
The diagnostic performance of the CNN for maxillary sinusitis from PRs was moderately high, whereas it was clearly higher with CBCT images. Three-dimensional images are accepted as the “gold standard” for diagnosis; therefore, this was not an unexpected result. Based on these results, deep-learning systems could be used as an effective guide in assisting with diagnoses, especially for less experienced practitioners.
Oral and dental health are vital parts of general baby health, and early dental visits provide significant prevention-focused intervention and parental counseling regarding oral health. Evaluating the age and main complaints of children is therefore important during their first dental visit (FDV). The purposes of this study were to determine the age, reason for the visit, behavioral response, and caries status at the FDV and to evaluate the factors affecting these parameters. Parents of 325 pediatric patients (159 males; 166 females; mean age 7.20±2.78 years) at their FDV were asked to fill out a questionnaire requesting sociodemographic information and their child’s medical history, brushing habits, and reasons for attending dental consultation. The decayed-missing-filled-teeth (dmft/DMFT) scores were also recorded. The child’s behavioral responses during the FDV were evaluated according to Frankl’s Behavior Rating Scale (FBRS). Higher maternal education level and dmft/DMFT score were associated with earlier FDV age. The most common reason for the FDV was dental caries in 33.5% of patients, followed by toothache (29.5%). Most of the children showed positive behavior (46.7%), with positive behavior affected by age and negative behavior affected by the dmft/DMFT score and distance from home. The mean dmft/DMFT score was 8.1±4.4 and was negatively affected by toothbrushing frequency and family income. These study results indicate that Turkish children living in Edirne present at a late age for the FDV. Raising awareness in terms of dental health care among parents is important to ensure that children attend their FDV at an early age.
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