Clinical survey over the past 35 years at the clinic for maxillofacial prosthetics Tokyo Medical and Dental University

Advancing maxillofacial prosthodontics by using pre-trained convolutional neural networks: Image-based classification of the maxilla

PURPOSE

The study aimed to compare the performance of four pre-trained convolutional neural networks in recognizing seven distinct prosthodontic scenarios involving the maxilla, as a preliminary step in developing an artificial intelligence (AI)-powered prosthesis design system.

MATERIALS AND METHODS

Seven distinct classes, including cleft palate, dentulous maxillectomy, edentulous maxillectomy, reconstructed maxillectomy, completely dentulous, partially edentulous, and completely edentulous, were considered for recognition. Utilizing transfer learning and fine-tuned hyperparameters, four AI models (VGG16, Inception-ResNet-V2, DenseNet-201, and Xception) were employed. The dataset, consisting of 3541 preprocessed intraoral occlusal images, was divided into training, validation, and test sets. Model performance metrics encompassed accuracy, precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), and confusion matrix.

RESULTS

VGG16, Inception-ResNet-V2, DenseNet-201, and Xception demonstrated comparable performance, with maximum test accuracies of 0.92, 0.90, 0.94, and 0.95, respectively. Xception and DenseNet-201 slightly outperformed the other models, particularly compared with InceptionResNet-V2. Precision, recall, and F1 scores exceeded 90% for most classes in Xception and DenseNet-201 and the average AUC values for all models ranged between 0.98 and 1.00.

CONCLUSIONS

While DenseNet-201 and Xception demonstrated superior performance, all models consistently achieved diagnostic accuracy exceeding 90%, highlighting their potential in dental image analysis. This AI application could help work assignments based on difficulty levels and enable the development of an automated diagnosis system at patient admission. It also facilitates prosthesis designing by integrating necessary prosthesis morphology, oral function, and treatment difficulty. Furthermore, it tackles dataset size challenges in model optimization, providing valuable insights for future research.

Prevalence of Possible Dementia in Patients with Maxillofacial Defects and Difficulty of Inserting Obturator in Maxillectomy Patients: Toward Better Provision of Supportive Care

As society ages, it is important to understand the prevalence of dementia and the difficulties of inserting prostheses in patients with maxillofacial defects in order to clarify issues in supportive care. We screened 183 patients for dementia using the revised Hasegawa's dementia scale (HDS-R) at the Clinic for Maxillofacial prosthetics, Tokyo Medical and Dental University, and investigated age and sex differences in HDS-R score. We asked 47 of the 183 participants about the difficulty of inserting a maxillofacial obturator prosthesis and collected subjective comments, information about the prosthesis, and data from five assessments. Multiple regression analysis was used to reveal factors associated with insertion difficulty. Overall, 8.7% of the participants were judged to have possible dementia. Men were more likely than women to have possible dementia, and the risk increased with age. Of the 47 participants, 26 reported difficulty inserting their prosthesis, 12 of whom attributed it to their oral defect. Fourteen patients advised following doctor's instructions to practice insertion in order to become accustomed to it. A lower HDS-R score had a significant impact on insertion difficulty. Cognitive function and difficulty inserting maxillary obturator prostheses should be considered in the provision of continuous supportive care to patients with maxillary defects.