9.1 Introduction

9.1.1 Temporomandibular Disorders (TMD)

These are a prevailing group of musculoskeletal and neuromuscular disorders that affect the muscular, soft tissue and osseous components of the Temporomandibular Joint. TMD have a multifactorial origin, where the predisposing, trigger, and perpetuating factors may include trauma, psychosocial, and physiopathological aspects. TMD have been reported as the most common origin of non-odontogenic oro-facial pain. In addition to pain, patients may present with joint sounds, limited or asymmetric mandibular movements, and different forms of disabilities which may severely affect the quality of life. The yearly cost for TMD treatment in the USA has been estimated to be $4 billion, not including imaging, thus TMD is regarded as a significant public health problem [1]. The management of TMD is often complicated, mainly due to the overlap between different medical specialties like dentistry, otology, neurology, orthopedic surgery, psychiatry, and others [2].

9.1.1.1 Classification of Temporomandibular Disorders

Clinicians and researchers benefit from the use of standardized, well-defined, and clinically relevant taxonomy for diseases to facilitate the transfer of information in both research and clinical practice. Among the pioneers in classifying TMD was Helkimo, who developed the Helkimo’s clinical dysfunction index (Di) in 1974. Di is a functional assessment of the TMD-related signs including the TMJ functional impairment, muscle tenderness, TMJ pain during palpation and mandibular movement, and range of mandibular mobility. Since Di is a purely clinical evaluation, it can be considered a useful tool to qualify patients for radiographic assessment and justify X-ray exposure [3]. Later, the International Headache Society (IHS) offered a classification for headache including 13 categories, among which the 11th was related to TMD.

The American Academy of Orofacial Pain provided a more detailed description of the 11th category of the IHS classification by introducing subcategories and their diagnostic criteria [4, 5]. The Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) has been the most widely accepted and commonly used classification for TMD since its publication in 1992 [6]. The strength of RDC/TMD lies in recognizing the psychosocial component and pain-related disabilities of TMD. The RDC/TMD has two evaluation Axis:

Axis I includes a clinical assessment of signs and symptoms augmented with radiographic evaluation, leading to the classification of three groups of TMD: Group I, muscular disorders (Group Ia, myofascial pain; Group Ib, myofascial pain with limited mouth opening); Group II, disk displacement (Group IIa, disk displacement with reduction; Group IIb, disk displacement without reduction with limited opening; group IIc, disk displacement without reduction without limited opening); and Group III, arthralgia, osteoarthritis, and osteoarthrosis (Group IIIa, arthralgia; Group IIIb, osteoarthritis; Group IIIc, osteoarthrosis).

Axis II comprises different instruments for the biobehavioral assessment, specifically the psychosocial status and pain-related disabilities as mentioned.

A new evidence-based protocol was published in 2014 to improve the sensitivity and specificity of the original RDC/TMD and add new comprehensive instruments for Axis I and Axis II to allow the diagnosis of simple and complex forms of TMD [7]. The modified protocol was called diagnostic criteria for temporomandibular disorders (DC/TMD) and included new Axis I instruments and assessment algorithms and new Axis II instruments. The new Axis I and II instruments proposed new terminology for several TMD diagnoses using more concise algorithms which are suitable for clinical application.

9.1.1.2 Prevalence of Temporomandibular Disorders

TMD are prevalent and divergent admixture of disorders affecting the osseous and soft tissue components of the TMJ. The reported prevalence of TMD differed greatly between different studies, not only due to the differences in populations but also due to the lack of standardization in the examination procedures, taxonomy, and diagnostic criteria. Patients might ignore the early signs of TMD or lose their way between different medical specialties before their diagnosis is confirmed. The prevalence of non-self-reported TMD which were discovered incidentally during the routine dental examination was found to be 10.8% in one study [8]. A questionnaire-based study in Brazil showed that 37.5% of the population had at least one TMD symptom [9], whereas epidemiologic surveys reported at least one TMD sign to be present in 40–75% of the population and at least one TMD symptom to be present in 33% of the population [10]. Normal joints were found only in 50% of boys and in 23–29% of girls in another study, whereas the rest of the study population showed partial to full disc displacement on magnetic resonance images (MRI) [11]. It has been confirmed that TMD are common disorders and have a negative impact on the quality of patients’ life, thus a costly effect on the nation’s economy.

9.2 Diagnosis of Temporomandibular Disorders

The diagnosis of TMD is a complicated process, mainly due to the multifactorial origin of TMD and the overlap of signs and symptoms between the different disorders. Many of the symptoms of TMD appear to arise from outside the TMJ, for example, ear pain, dizziness, neck pain, tinnitus, and headache. Accordingly, patients may lose their way between different medical specialties before reaching a proper TMD diagnosis. TMD has been declared to be within the scope of practice of dentistry, so dental graduates are considered the primary care providers for TMD [12]. It’s the duty of each dentist to screen patients for TMD, even when no signs and symptoms are reported by the patient.

The diagnosis of TMD involves history taking, clinical examination, and imaging. In addition, biobehavioral and psychosocial assessments are essential components of the diagnosis process as indicated by the DC/TMD [7]. According to the DC/TMD, the following disorders represent the latest disease entities: myalgia, local myalgia, myofascial pain, myofascial pain with referral, four-disc displacement disorders, arthralgia, degenerative joint disorder, subluxation, and headache. Different diagnostic algorithms for most disorders were created that map the patient’s history, clinical, and radiographic findings to a final diagnosis using a decision tree. The Axis I diagnostic criteria include specific clinical examination procedures and instruments to assess pain, joint noises, jaw locking, and headache. Axis II assessment instruments include pain intensity and disability, jaw function and para-function, and psychosocial distress [7].

9.2.1 The Conventional Diagnostic Approach

Although the treatment guidelines for TMD have been well established, however, TMD diagnosis is still considered a dilemma. Imaging is a crucial component of the diagnostic process. It has been noted that TMD prevalence was overestimated in studies that used questionnaires and clinical examination only, without confirming the primary diagnosis with imaging [8]. Several imaging modalities have been used to diagnose TMD, including panoramic radiography, linear and complex motion tomography, computed tomography (CT) to evaluate the osseous components, and magnetic resonance imaging to evaluate the soft tissue components of the joint [13].

The transcranial view, a two-dimensional projection, has been used in the past for TMJ evaluation; however, its use is currently limited due to the superimposition of nearby structures, resulting in inaccurate assessment of the pathologic changes. Panoramic radiography may be useful for initial screening for TMD and for detecting the gross osseous changes. However, several limitations are associated with the use of panoramic radiography like the inability to show the entire articular surface, superimposition of the zygoma, and the possible image distortion. Accordingly, panoramic radiography has been reported to have poor reliability and low sensitivity in the assessment of bony changes associated with TMD [14]. Linear and complex motion tomography have been utilized for TMJ assessment for years, with a reported sensitivity range of 53–90% in spotting bony changes associated with TMD and a specificity range of 73–95%. Nevertheless, the diagnostic accuracy of tomography is deficient, due to the inadequacy in detecting small osseous changes [15].

9.3 Digitization in the Diagnosis of Temporomandibular Disorders

9.3.1 Cone Beam Computed Tomography

Osteoarthritis of the TMJ is the most common TMD to be seen in the dental clinic. The reason is the association between osteoarthritis, severe pain, and disability. Osteoarthritis is linked to TMJ inflammation and degenerative changes in the different components of the TMJ [16]. In addition to history and clinical examination, different imaging modalities have been used to diagnose osteoarthritis definitely. Panoramic radiography and linear and complex tomography have serious limitations as discussed before. CT has been used successfully for the assessment of the bony changes associated with osteoarthritis. However, the use of CT in dentistry, in general, has been limited due to high cost, high level of radiation, and limited access to equipment in the dental clinics.

The introduction of cone beam computed tomography has led to a dramatic change in the process of TMD diagnosis. CBCT uses a cone-shaped source of radiation incorporating the entire field of view and enables quick data acquirement. Compared to CT, CBCT has been reported to have similar accuracy but with lower cost, lower doses of radiation, and easier access to equipment and accordingly is present in many dental clinics nowadays. In addition, CBCT allows for a shorter exposure time, sharper images, and less image distortion [17]. In a medium field of view, CBCT warrants 35% less dose of radiation compared to multidetector CT. CBCT allows the evaluation of every aspect of the bony components of the TMJ, unlike panoramic and sagittal radiography. Compared to CT, CBCT was reported to have similar specificity in assessing condylar osseous abnormalities (1.0); however, CBCT had higher diagnostic accuracy (0.9) compared to CT (0.86) and higher sensitivity (0.8) compared to CT (0.7) [18].

To evaluate the value of CBCT in the diagnosis of TMD, the association between the CBCT findings and the clinical TMD diagnosis was investigated.

In one study which evaluated patients diagnosed with osteoarthritis and closed lock, the following radiographic criteria were assessed in sagittal, coronal, and axial planes; flattening of the articular surfaces, osteophyte formation, Ely’s cyst (subcortical cyst), condylar surface irregularities, and joint space measurements. This study showed that condylar superior surface irregularities were detected by CBCT in 80% of joints with osteoarthritis and in 11.54% of joints with closed lock, whereas osteophytes were found in 40% of joints with osteoarthritis and in 3.85% of joints with closed lock. CBCT also showed the flattening of the condylar surfaces in 30% of osteoarthritic joints and in 7.69% of closed lock joints, whereas Ely’s cysts were found in 10% of osteoarthritic joints and in 3.85% of closed lock joints. Joint space measurements done by CBCT showed that normal patients have significantly more joint space (5.64 ± 1.88) compared to patients with osteoarthritic joints (4.57 ± 1.97) (Fig. 9.1). This study concluded that CBCT diagnosis was significantly associated with the clinical findings in cases with osteoarthritis and closed lock [19].

Another study also found a strong correlation between the condyle and glenoid fossa CBCT findings and the score and degree of Helkimo’s clinical dysfunction index [20]. Interestingly, the use of CBCT led to a change in the primary TMD diagnosis in 26.08% of cases in one study [8] and in 65% of cases in another study [21]. CBCT also led to changes in management decisions in 40% of cases in the latter study.

9.3.2 Analysis of Occlusion and Occlusal Forces

The question of whether dental occlusion is a contributing factor for TMD or not has been debatable for years. There is still no clear answer to this controversy; however, with the introduction of more objective evidence-based approaches for the diagnosis of TMD and with the use of modern digital analysis technologies which are able to quantify occlusal forces, we might come closer to an answer for this question soon. In one study, 11 common occlusal features were analyzed as possible risk factors for TMD. In this study, five TMD classes were analyzed: disc displacement with reduction, disc displacement without reduction, myalgia, primary osteoarthrosis, and secondary osteoarthrosis. This study concluded that the contribution of occlusion to the different TMD categories could not be proven; however, out of the 11 occlusal features, the following showed a high risk: anterior open bite, unilateral maxillary lingual crossbite, overjets greater than 6–7 mm, more than 5–6 missing posterior teeth, and retruded contact position to intercuspal position greater than 2 mm. The authors of this study stated that occlusion should not be regarded as an etiologic factor for TMD; nevertheless, anterior open bite in osteoarthrosis cases can be regarded as a result rather than a cause for TMD [22].

On the other hand, studies which support the effects of occlusion on TMD describe the consequences of premature contacts which lead to condyle displacement, resulting in friction and increased pressure inside the joint. This will result in pathologic intra-articular changes if the adaptation capacity of the joint is exceeded [23, 24]. The modern digital occlusal analysis technologies provide detailed information about occlusion time, the center of occlusal forces, and the arrangement of occlusal forces on both sides. An example of this technology is the T-Scan computerized occlusal analysis system (Tekscan Inc., South Boston, MA, USA). T-Scan, which is composed of a digital pressure mapping sensor, a headpiece, and software, provides a comprehensive occlusal analysis which overcomes many of the limitations of the traditional occlusal analysis practices (Fig. 9.2). The system provides information on the level and timing of force on each tooth, quadrant and side, the stability of the bite, the center of force target and trajectory, and contact time sequencing and enables the comparison between the pre- and posttreatment records. The system also enables the assessment of the dynamics of chewing movements and muscles’ stress on teeth. Using T-scan, one study was able to correlate TMD symptoms with group function occlusion, whereas the canine-guided occlusion was significantly associated with normal joints [25].

It is well-known that canine-guided occlusion is favorable for the TMJ due to the disclosure of posterior teeth during lateral excursions, better tolerance to horizontal forces, and less muscular activity during movements. In the same study, balancing side interferences were found to have a significant correlation to TMD compared with poor correlations with working side and protrusive interferences. Balancing side interferences were shown to be associated with high muscular activity and destructive forces using electromyography [26]. T-scan showed that the occlusion and disocclusion times for TMD patients were longer than control, leading to an increase in muscular activity and an increase in stresses on joint components [25]. In another study, T-scan has proved that in intracapsular disorders, there is a change in condylar position from the normal centric relation position associated with an increase in the percentage of force due to occlusal interferences [27].

9.3.3 Real-Time Jaw Movement Tracking

Measuring the 3D in vivo kinematics of the mandible during different movements would potentially enhance the diagnostic process of TMD. Attempts to achieve this goal started with the use of two mechanical face bows [28] and progressed to the use of more compact and refined systems [29–31]. Stereophotogrammetric systems were developed for this purpose and were depending on skin marks; however, these systems had limitations due to the artifacts associated with the skin movements relative to the underlying bones. Transoral devices were developed to overcome these limitations, but these devices had also their own limitations, as they were interfering with the natural movements of the mandible [32].

Subsequently, a fluoroscopic 3D imaging modality enabled the registration of TMJ rigid-body kinematics in three dimensions. This system depended on recording the data obtained from the CT images on single-plane fluoroscopic images. The system was then modified to use CBCT data instead of CT as the former is more widely used in dental practice. The system was capable of recording the midpoint of the interincisal edge during opening and closing, protrusion and retrusion, chewing, and lateral gliding movements. Thus, this system was able to quantify the different complicated movement patterns of the TMJ and provided a complete analysis of the mandibular motion using different reference points [33]. Such systems allow for better understanding of the biokinetics of the TMJ and the etiology of TMD and would facilitate the refinement of management approaches as well. The CBCT-based jaw motion tracking systems allow measuring the movement paths of different points of interest in different planes and can be exported to other software for further analysis, patient education, and treatment planning.

9.3.4 Real-Time Magnetic Resonance Imaging (MRI) of the TMJ

When the diagnosis of TMJ soft tissue-related disorders are considered, MRI is definitely regarded as the gold standard [34]. The location of the disc in opened and closed mouth positions and its configuration are required for a definite diagnosis of certain TMD classes. However, the traditional MRI assesses the soft tissue components of the TMJ only at static positions. Recently, high-speed real-time MRI was introduced in order to augment the diagnosis of TMD with dynamic information concerned with the movement of the TMJ [35].

Researchers were able to transform the single-slice real-time MRI records to movie recordings in several layers with improved contrast and superior image quality during movement [36]. The protocol used in this study involved triple-slice recordings for both joints, followed by dual-slice recordings and quadruple-slice recordings on an average slice thickness of 6–8 mm. The method used achieved thorough volume coverage of the entire jaw enabling precise dynamic characterization for both joints. Another approach combined fast low-angle exposures MRI sequences with radial data sampling and view sharing of consecutive acquisitions. The resulting reconstructions allowed for dynamic images visualizing the movements of the TMJ which were free from motion artifacts [37]. Such technologies are extremely beneficial for the diagnosis of TMD, especially internal derangements, due to the merit achieved by visualizing the TMJ disc during the movements of the jaw.

9.3.5 Artificial Intelligence (AI)

Many of the recent advances in the healthcare sector have been influenced by AI. AI is geared toward improving the accuracy of the diagnosis of different diseases, saving the patient’s and medical practitioner’s time, and improving work efficiency in general. In dentistry, AI has been used to develop programs that can assist in the diagnosis of diseases, identification of pathologies, detection of radiographic landmarks, and segmentation of radiographic structures. To achieve such goals, several programming approaches were utilized, including machine learning techniques and computer vision algorithms [38–40]. From this perspective, neural networks were developed to provide highly efficient and sound mathematic models that are able to classify, predict, and recognize patterns in different fields including medicine. Neural networks assist medical practitioners in the process of decision-making for the diagnosis of different disease and treatment planning [41].

In the field of oral and maxillofacial surgery, neural networks have been used to identify patients with risk of oral cancer [42], to assist in treatment planning for lower third molars [43], and to detect cervical lymph node metastasis of squamous cell carcinoma [44]. As pertaining to TMD, an artificial neural network was trained to diagnose disc displacement with acceptable sensitivity and specificity using frontal chewing patterns of normal subjects and of TMD patients. The neural network training module in this study used data related to clinical symptoms and diagnoses of 161 patients, whereas 58 patients were used for testing. This study reported a low accuracy in the diagnosis of disc displacement without reduction compared to a high accuracy achieved in the diagnosis of disc displacement with reduction. The authors contributed this discrepancy to the variability of the symptoms associated with disc displacement without reduction [45].

Another neural network application was used in a study to diagnose TMJ osteoarthritis through a minimally invasive approach. This study analyzed the serum and salivary levels of 17 different biomarkers associated with osteoarthritis and then assessed the correlations between the morphological changes in different surfaces of the condyle with the biomarkers through a neural network. The neural network was trained using data from 154 condyles with osteoarthritis and 105 controls and then tested using 34 condyles. The network was trained to calculate the condyles’ geometric characteristics and dysmorphology and to detect different degrees of deformations in the osteoarthritic condyles. In this study, AI was used to stage the condylar morphology in TMJ osteoarthritis into six stages ranging from normal to severely degenerated condyles. Although the deep learning design used in this study analyzed complex condylar morphologies, significant agreements were found between clinician experts’ classifications and the neural network classifications. The study recommended the use of larger datasets in future studies to overcome any discrepancies in clinician experts’ visual perception and any inaccuracies in the computation of features in the neural networks. The study concluded that the used neural network provided a high degree of accuracy in classifying the stages of osteoarthritis of the TMJ depending on the condylar morphologies [46].

Another example of the application of the neural network deep learning approach in the field of TMJ used the “Data Storage for Computation and Integration” web-based system to remotely compute a neural network classifier for the diagnosis of TMJ osteoarthritis. This study focused on improving the accuracy of the radiographic interpretations of TMJ osteoarthritis and refining the classifications of osteoarthritis which depend on data from biological markers and clinical variables. The approach used in this study was to detect the hidden patterns which are present in the big data obtained during the diagnostic process using machine learning algorithms to improve the diagnostic decision-making. The study used radiographic, clinical, and biological data obtained from patients with TMJ osteoarthritis. Two clinician experts classified subjects into five subgroups of osteoarthritis with variable degrees of degeneration and compared their classifications to the results obtained from the neural network. The neural network utilized a slicer plug-in (shape variation analyzer) to evaluate average morphologies and classify morphological changes. The results of this study defined eight groups of morphological variability in the condyles after excluding bone proliferations and overgrowth as they do not seem to follow a pattern. The authors recommended using larger databases in future research to accurately train the neural networks to recognize different patterns of TMJ degeneration and thus improve the precision of the classifications [47].

AI is expected to improve the performance of medical practitioners in the near future and thus improve productivity by eliminating subjective interpretations and allowing for smart clinical decisions. Recently, many algorithms have been tested and showed excellent results in mining medical data.

9.4 Treatment of Temporomandibular Disorders

The success of TMD treatment depends on the choice of the correct treatment approach at the correct time. TMD are progressive disorders, which may deteriorate to more advanced forms over time if not treated. With the advances in the diagnostic approaches for TMD and the development of universally accepted guidelines for treatment, it was expected that the high prevalence of advanced forms will decrease. However, TMD treatment still suffers from the lack of training in undergraduate dental education, the inconsistency in the offered treatment modalities between different medical and dental specialties, and the scarcity of the recommended multidisciplinary approach which is essential for the success of treatment. The refinement of the treatment modalities using the recent advancement in technology is expected to improve the treatment outcomes and reduce the high prevalence of TMD.

9.4.1 The Conventional Treatment Approach for Temporomandibular Disorders

Conservative treatment has been recommended as the starting point in the management of TMD, which may include rest, soft diet, drug therapy, physiotherapy, and several types of occlusal guards. The goals of TMD treatment are to reduce pain and restore normal function. Contradicting results in the success rates of nonsurgical treatment have been reported in the literature, ranging from 36% [48] to 88% [49]. Patients refractory to conservative treatments are usually candidates for more invasive forms of treatment.

In the past, open joint surgeries were dominating the treatment approaches for patients not responding to conservative treatment. Currently, open joint surgeries are only recommended if minimally invasive surgeries fail. Procedures like synovectomy, discectomy, eminoplasty, eminectomy, and disc plication were utilized to manage advanced TMD cases and were reported to be successful in reducing pain and improving function, but a high rate of complications was reported as well [50, 51]. If no improvement was achieved following minimally invasive surgeries and open joint surgeries, then total joint replacements are recommended. The minimally invasive procedures, like arthroscopy and arthrocentesis, are nowadays the treatment modalities of choice for managing TMD cases not responding to conservative treatment. The use of the arthroscope in the TMJ was first described by Onishi in 1975 [52].

Since then, a lot of progress has been achieved in the field of TMJ arthroscopy [53, 54]. Arthroscopic lysis and lavage were shown to be effective in treating osteoarthritis and disc displacement without reduction of the TMJ by releasing the fibrous adhesions inside the joint, washing out the inflammatory mediators and preventing the suction effect of the TMJ disk to the fossa [54]. Operative arthroscopy is a different technique which allows instrumentation to carry out strategies more advanced than lysis and lavage as debridement, miotomy, disc reduction, and disc fixation [55]. In 1987, Murakami et al. applied the same concepts of lysis and lavage without the visualization in arthrocentesis of the TMJ [56]. The double-puncture arthrocentesis technique as used today was first described by Nitzan et al. [57], while later several proposals for single-puncture arthrocentesis were introduced [58–60].

9.5 Digitization in the Treatment of Temporomandibular Disorders

9.5.1 Computer-Assisted Arthroscopy of the TMJ

Refinement of the technique of TMJ arthroscopy is an ongoing process [61]. Among the technological advances in this field was the introduction of a system that facilitated the navigation of the arthroscope between the different anatomical structures. The system allowed the visualization of digital data and videos simultaneously on independent channels. The digital data can be obtained from CT scans, MRI, and radiographic records. The system then allowed the superimposition of projected computer graphics on online pictures to define the anatomical structures more clearly on video monitors. This was done through the consolidation of the digital data sets with real-time position data through different tracking technologies. This system facilitated accurate navigation of the arthroscope inside the TMJ between the anatomical structures during the different surgical procedures in a composite-reality environment.

The system also allowed the recording of the 3D data of the surgical procedure. The system used an optoelectronic tracking technology with an orthotopic matching technology allowing the registration of any movement of the equipment attached to the tracking devices. Accordingly, the puncturing locations and the working channels were identified using the preoperative CT data, and the surgical access path was followed in a real-time superimposition in the live video image on a head display or monitor. A safe pathway for the arthroscope was thus displayed using virtual rectangles superimposed on the real patient and leading the surgeon to the correct puncture point and target anatomical structure. This study concluded that computer-assisted arthroscopy can reduce the possible complications associated with the surgery and reduce the operative time as well. In addition, it allows a remote expert to observe the surgical procedure and even interfere in the intraoperative decision-making process. The authors recommended that further studies should concentrate on integrating MRI records with computer-assisted technology to allow for the interpretation of soft tissues of the joint [62].

Another study evaluated a TMJ computer-assisted surgical navigation system for the treatment of ankylosis. The authors of this study performed computer-assisted gap arthroplasty in one group of patients and compared the results to another group where the procedure was done without a computer-assisted approach. CT was used postoperatively to assess the accuracy of gap arthroplasty in both groups. The results of the study showed that using a computer-assisted approach the authors were able to achieve a more extensive removal of the ankylosed bone while ensuring a proper safety distance [63].

A technical report, published in 2017, introduced a surgical template for TMJ access during minimally invasive surgeries. The template was based on the use of CBCT and computer-aided design/computer-aided manufacturing (CAD/CAM). The workflow in this report was composed of obtaining CBCT, CT, or MRI data, followed by optical scanning of the face, and then the conversion of DICOM data to surface data which was aligned to the optical scan data using an algorithm. This workflow allowed for the designing of a printable biocompatible template with two working channels for the scope and manipulation instruments that were aligned to the anatomy of the patient. The template was used to direct the surgeon to the correct puncture points and direct the arthroscope to the correct working site inside the TMJ. The puncture point on the template was supposed to be chosen so that it allows all planned movements during the surgical procedure. The advantages of this approach have been identified as making the minimally invasive surgeries easier and safer for the inexperienced surgeons and allowing the identification of the correct puncturing point, which might be tricky even for the experienced surgeon. The procedure has been described as being affordable; however, planning was time-consuming, which is expected to improve with further training [64].

9.5.2 Customized Design: Total TMJ Prosthesis

The indications for total TMJ prosthesis include end-stage osteoarthritis of the TMJ, comminuted condylar fractures, tumors, and idiopathic resorption of the joint. The two main total joint prosthesis systems available at the market are Zimmer Biomet (Biomet Microfixation, Jacksonville, FL, USA) [65] and TMJ Concepts (TMJ Concepts Inc., Camarillo, CA, USA) [66]. Despite the fact that the available stock prostheses have been reported to be highly efficient, they are not essentially suitable for every case, partially due to the unmatching TMJ anatomy and partially due to the high cost. This was the drive for the development of customized TMJ prosthesis using 3D printing and CAD/CAM technologies.

This approach was used in a study to evaluate the safety and efficacy of a customized TMJ prosthesis for a Chinese population. The study was conducted on patients with unilateral end-stage osteoarthritis. The procedure started by taking a CT scan and creating a 3D craniomaxillofacial model using DICOM files. Using the software, the lower part of the eminence and the whole condyle were cut, followed by designing the prosthesis using another software. The designed prosthesis had three components; a glenoid fossa, a condylar head, and the mandibular handle component, which was fixed on the ramus of the mandible (Fig. 9.3).

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