A deep learning approach for masseter muscle segmentation on ultrasonography

The masseter muscle (MM) is a quadrilateral muscle that has both superficial and deep sections. A tendinous aponeurosis, which originates from the zygomatic process of the maxilla and moves downward and backward, connects the lateral surface of the ramus of the mandible to the superficial part, which is the larger region. The deep part, which arises from the posterior bottom border of the zygo-matic arch and runs downward and forward, connecting to the ramus and coronoid process, is much smaller^(1–4).

The thickness of the masseter muscle has been examined extensively in relation to masticatory function and craniofacial functional processes. Occlusal morphology and biting force can impact development, facial morphology, and muscle thickness^(5–8). According to previous studies^(9,10), measuring cross-sectional distances of head and neck muscles can be related to muscle palpation pain, face shape, biting force, and occlusal factors. In addition, there appears to be a relationship between masseter muscle thickness and a variety of dental arch parameters, such as alveolar process thickness and maxillary dental arch width.

The use of ultrasound imaging (US) to examine the superficial tissues of the head and neck is particularly beneficial^(1,5). Ultrasonography is a type of medical imaging that enables exact measurement of the masticatory muscle size. It can be used to diagnose, quantify, and identify changes in the proportions of the face and neck muscles, as pointed out in several articles^(5,11–14). Ultrasound can be used to get well-defined masticatory muscle images, particularly for the masseter and anterior temporal muscles, such that thickness can be estimated with excellent repeatability and speed while avoiding ionizing radiation exposure. Therefore, it is a good method for examining the perioral muscles.

Artificial intelligence (AI) is described as the use of computers or machines to undertake tasks that would typically be performed by people^(15–19). Machine learning and deep learning (DL) are an artificial intelligence field that may be used to educate machines and computers how to analyze various types of data using different techniques. AI algorithms are employed in a range of sectors, including engineering, the stock market, and medicine, among others^(17–21). Artificial intelligence principles and actual potential, as well as its impact on our personal and professional life, are still unknown to many people, including physicians and physicists.

AI algorithms have a lot of potential in dentistry, especially in imaging^(15–20). In recent years, AI applications in dentistry have aroused much interest in fields such as caries diagnostics, pathology detection, orthodontic treatment planning, robotic surgery, and dental implant installation^(22–25). Dental radiology research has been emphasized due to its adaptation of image processing tools. Artificial intelligence (AI)-based computer-aided detection and diagnosis are being utilized to improve the quality, efficiency, and affordability of US imaging, which has led to an increase in US acceptability for musculoskeletal assessments⁽²⁶⁾. As a result, the purpose of this study is to evaluate the effectiveness of a deep convolutional neural network (D-CNN)-based AI system for masseter muscle detection and segmentation on US images.

In our study, the artificial intelligence (AI) deep learning model U-net provided the detection and segmentation of all test images, and when the success rate in the estimation of the images was evaluated, the F1, sensitivity and precision results of the model were 1.0, 1.0 and 1.0, respectively, indicating perfect precision and recall (Tab. 1).

Ultrasound imaging is a powerful diagnostic technique for musculoskeletal examinations. It is noninvasive and delivers real-time imaging without the use of ionizing radiation. It’ is also popular for evaluating ligaments, muscles, and tendons, as well as superficial cancers and peripheral nerves^(26,30,31).

By avoiding the hand-crafted engineering phases that define ML pipelines, DL transformed the field of end-to-end learning. Deep Neural Networks automatically detect patterns in data and perform admirably in a variety of applications, such as radiology^(32,33), dermatology⁽³⁴⁾, and ophthalmology⁽³⁵⁾.

AI-based musculoskeletal imaging improved greatly in the previous decade, boosting anatomical structure visualization and automating quantitative assessments. According to current research on AI-based musculoskeletal US^(26,30), DL methods may become next-generation diagnostic tools for monitoring the state of joints, bones, cartilage, ligaments, and muscles.

Only one study in the literature assessed the effectiveness of a deep convolutional neural network (D-CNN)-based artificial intelligence (AI) system for masseter muscle segmentation using ultrasonography (USG) images. In this investigation, Orhan et al.⁽³⁶⁾ included a total of 195 anonymised US images. U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures were used in the deep learning process. Muscle thickness was measured with the use of US software and manual segmentation. Following automated muscle measurements, a neural network model (CranioCatch, Eskisehir-Turkey) was employed to determine the muscles. The test dataset was used to calculate accuracy, Receiver Operating Characteristic (ROC) area under the curve (AUC), and Precision-Recall Curves (PRC) to compare a human observer with the AI model. AI was statistically compared to manual segmentation and measures (p <0.05). Only two examples of muscles were not recognized by PSPNet, and the AI models detected and segmented all test muscle data for FPN and U-net (false negatives). FPN, PSPNet, and U-net had 0.985, 0.947, and 0.969 accuracy, respectively. Similarly, in our study, the artificial intelligence (AI) deep learning model, U-net, provided the detection and segmentation of all test images, and when the success rate in the estimation of the images was evaluated, the F1, sensitivity and precision results of the model were 1.0, 1.0 and 1.0, respectively.

Although AI-based musculoskeletal US has shown significant promise in terms of overcoming high variability and operator dependency, there are a few limitations to be mindful of. First, there is a distinction between 2D and 3D imaging, which is often utilized in radiology clinics. Due to the complexity of musculoskeletal systems and multiple joints, image preprocessing techniques, such as rigid or non-rigid image registration, are required for the large-scale usage of DL for US. Without a thorough grasp of functional anatomy, even for US professionals, diagnosis based on 2D US is difficult. The narrow 2D US image planes are difficult to duplicate and locate, which is a drawback for creating a large, standardized medical image collection. Recent AI-based 3D US imaging approaches may be able to overcome 2D US limitations^(37,38). As a result, 3D medical US reconstruction, visualization, and segmentation methodologies appear to be promising.

Although the present algorithm showed promising results, this study had a significant limitation. Further studies are needed to determine the estimation success of segmentation using larger data sets, as well as to examine additional types of architectures.

AI has a wide range of functions and applications in the health-care industry. Increased effort and likely doctor’s weariness may risk diagnostic abilities and outcomes. Artificial intelligence components in imaging equipment would reduce this effort and boost efficiency. Our first findings indicate that deep learning has the capability to deal with this significant obstacle. Future AI research should continue to emphasize human interests as its primary purpose, as expertise in processing large amounts of data improves.