Time and cost of maxillofacial prosthetic treatment: The need for assessment

Ali, Islam E.; Murase, Masatoshi; Yonehara, Yoshiyuki; Sumita, Yuka I.

doi:10.1016/j.jdsr.2022.02.001

Cited by 4 publications

(2 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This system could be designed to customize questions about intra and extraoral conditions such as mouth opening, bone support of abutments, opposing arch condition, inter-arch space, number and distribution of remaining teeth, and others. 39 Based on the difficult level results, it becomes possible to determine which dental clinic to visit, whether a case is suitable for a dentist with limited experience, and which dental clinic a surgeon performing tumor resections should refer their patient to. The functional results provide insight into how much of the desired functionality can be achieved through maxillofacial prosthetic treatment.…”

Section: Discussionmentioning

confidence: 99%

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

Ali,

Sumita,

Wakabayashi

2024

Journal of Prosthodontics

Self Cite

View full text Add to dashboard Cite

PurposeThe 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 methodsSeven 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.ResultsVGG16, 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.ConclusionsWhile 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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Ali,

Sumita,

Wakabayashi

2024

Journal of Prosthodontics

Self Cite

View full text Add to dashboard Cite

show abstract

“…Conducting time-study research to measure the time required for each treatment item in a dental practice can reveal the key factors that contribute to medical reimbursements [10] , [11] . A national survey conducted by the CJDSS collected data mainly from facilities that provide general prosthetic services, and thus the number of maxillofacial prosthetic cases included in the survey was limited [12] .…”

Section: Introductionmentioning

confidence: 99%