An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

Le résumé fourni par la source

Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images. Materials and Methods: This retrospective study was carried out by using the radiology archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry in Ankara University. A total of 195 anonymized USG images were used in this retrospective study. The deep learning process was performed using U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures. Muscle thickness was assessed using USG by manual segmentation and measurements using USG’s software. The neural network model (CranioCatch, Eskisehir-Turkey) was then used to determine the muscles, following automatic measurements of the muscles. Accuracy, ROC area under the curve (AUC), and Precision-Recall Curves (PRC) AUC were calculated in the test dataset and compare a human observer and the AI model. Manual segmentation and measurements were compared statistically with AI ( P < .05). The Mann–Whitney U test was used to analyze whether there is a statistically significant difference between the predicted values and the actual values. Results: The AI models detected and segmented all test muscle data for FPN and U-net, while only two cases of muscles were not detected by PSPNet (false negatives). Accuracies of FPN, PSPNet, and U-net were estimated as 0.985, 0.947, and 0.969, respectively. Receiver operating characteristic scores of FPN, PSPNet, and U-net were estimated as 0.977, 0.934, and 0.969, respectively. The D-CNN measurements of the muscles were similar to manual measurements. There was no significant difference between the two measurement methods in three groups ( P > .05). Conclusion: The proposed AI system approach for the analysis of USG images seems to be promising for automatic masseter muscle segmentation and measurement of thickness. This method can help surgeons, radiologists, and other professionals such as physical therapists in evaluating the time correctly and saving time for diagnosis.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.