Deep learning for automated segmentation of the temporomandibular joint

An automated method for assessing condyle head changes in patients with skeletal class II malocclusion based on Cone-beam CT images

OBJECTIVES

Currently, there is no reliable automated measurement method to study the changes in the condylar process after orthognathic surgery. Therefore, this study proposes an automated method to measure condylar changes in patients with skeletal class II malocclusion following surgical-orthodontic treatment.

METHODS

Cone-beam CT (CBCT) scans from 48 patients were segmented using the nnU-Net network for automated maxillary and mandibular delineation. Regions unaffected by orthognathic surgery were selectively cropped. Automated registration yielded condylar displacement and volume calculations, each repeated three times for precision. Logistic regression and linear regression were used to analyse the correlation between condylar position changes at different time points.

RESULTS

The Dice score for the automated segmentation of the condyle was 0.971. The intraclass correlation coefficients (ICCs) for all repeated measurements ranged from 0.93 to 1.00. The results of the automated measurement showed that 83.33% of patients exhibited condylar resorption occurring six months or more after surgery. Logistic regression and linear regression indicated a positive correlation between counterclockwise rotation in the pitch plane and condylar resorption (P < .01). And a positive correlation between the rotational angles in both three planes and changes in the condylar volume at six months after surgery (P ≤ .04).

CONCLUSIONS

This study's automated method for measuring condylar changes shows excellent repeatability. Skeletal class II malocclusion patients may experience condylar resorption after bimaxillary orthognathic surgery, and this is correlated with counterclockwise rotation in the sagittal plane.

ADVANCES IN KNOWLEDGE

This study proposes an innovative multi-step registration method based on CBCT, and establishes an automated approach for quantitatively measuring condyle changes post-orthognathic surgery. This method opens up new possibilities for studying condylar morphology.

Shaping the 4D frontier in maxillofacial surgery with faceMesh evolution

OBJECTIVES

This work aims to introduce a Python-based algorithm and delve into the recent paradigm shift in Maxillofacial Surgery propelled by technological advancement. The provided code exemplifies the utilization of the MediaPipe library, created by Google in C++, with an additional Python interface available as a binding.

TECHNICAL NOTE

The advent of FaceMesh coupled with artificial intelligence (AI), has brought about a transformative wave in contemporary maxillofacial surgery. This cutting-edge deep neural network, seamlessly integrated with Virtual Surgical Planning (VSP), offers surgeons precise 4D facial mapping capabilities. It accurately identifies facial landmarks, tailoring surgical interventions to individual patients, and streamlining the overall surgical procedure.

CONCLUSION

FaceMesh emerges as a revolutionary tool in modern maxillofacial surgery. This deep neural network empowers surgeons with detailed insights into facial morphology, aiding in personalized interventions and optimizing surgical outcomes. The real-time assessment of facial dynamics contributes to improved aesthetic and functional results, particularly in complex cases like facial asymmetries or reconstructions. Additionally, FaceMesh has the potential for early detection of medical conditions and disease prediction, further enhancing patient care. Ongoing refinement and validation are essential to address limitations and ensure the reliability and effectiveness of FaceMesh in clinical settings.

Automated localization of mandibular landmarks in the construction of mandibular median sagittal plane

OBJECTIVE

To use deep learning to segment the mandible and identify three-dimensional (3D) anatomical landmarks from cone-beam computed tomography (CBCT) images, the planes constructed from the mandibular midline landmarks were compared and analyzed to find the best mandibular midsagittal plane (MMSP).

METHODS

A total of 400 participants were randomly divided into a training group (n = 360) and a validation group (n = 40). Normal individuals were used as the test group (n = 50). The PointRend deep learning mechanism segmented the mandible from CBCT images and accurately identified 27 anatomic landmarks via PoseNet. 3D coordinates of 5 central landmarks and 2 pairs of side landmarks were obtained for the test group. Every 35 combinations of 3 midline landmarks were screened using the template mapping technique. The asymmetry index (AI) was calculated for each of the 35 mirror planes. The template mapping technique plane was used as the reference plane; the top four planes with the smallest AIs were compared through distance, volume difference, and similarity index to find the plane with the fewest errors.

RESULTS

The mandible was segmented automatically in 10 ± 1.5 s with a 0.98 Dice similarity coefficient. The mean landmark localization error for the 27 landmarks was 1.04 ± 0.28 mm. MMSP should use the plane made by B (supramentale), Gn (gnathion), and F (mandibular foramen). The average AI grade was 1.6 (min-max: 0.59-3.61). There was no significant difference in distance or volume (P > 0.05); however, the similarity index was significantly different (P < 0.01).

CONCLUSION

Deep learning can automatically segment the mandible, identify anatomic landmarks, and address medicinal demands in people without mandibular deformities. The most accurate MMSP was the B-Gn-F plane.

Clinical use of digital applications for diagnostic and treatment planning in prosthodontics: A scoping review

AIM

This scoping review aimed to compile and evaluate clinical trials investigating digital applications in prosthetic diagnostics and treatment planning by assessing their clinical relevance and future potential.

METHODS

Following the PCC-framework for scoping reviews and combining the source of analysis (Population/P: "prosthodontics"), the technique of interest (Concept/C: "digital application") and the field of interest (Context/C: "diagnostics"), a three-pronged search strategy was applied in the database PubMed and Web of Science. Clinical trials (≥10 study participants, English/German) were considered until 2023-03-09. Reporting adhered to the PRISMA-ScR statement.

RESULTS

The search identified 520 titles, of which 18 full-texts met the inclusion criteria for data extraction. The trials involved a total of 14,457 study participants and were mapped for prosthetic subdisciplines: fixed (n = 9; 50%) and removable (n = 4; 22%) prosthodontics, reconstructive dentistry in general (n = 3; 17%), and temporo-mandibular joint disorders (n = 2; 11%). Data merging of medical format files, as DICOM+STL, was the dominant digital application (n = 7; 39%); and virtual treatment simulation using digital smile design or digital wax-up represented the most frequent prosthetic diagnostics (n = 6; 33%).

CONCLUSION

This scoping review identified a relatively low number of clinical trials. The future potential of digital diagnostics appears to be mostly related to the subdiscipline of fixed prosthodontics, especially regarding virtual treatment simulation for communication with the patient and among dental professionals. Artificial intelligence emerged as a key technology in many of the identified studies. Further research in this area is needed to explore the capabilities of digital technologies in prosthetic diagnostics and treatment planning.

Virtual Dental Articulation Using Computed Tomography Data and Motion Tracking

Dental articulation holds crucial and fundamental importance in the design of dental restorations and analysis of prosthetic or orthodontic occlusions. However, common traditional and digital articulators are difficult and cumbersome in use to effectively translate the dental cast model to the articulator workspace when using traditional facebows. In this study, we have developed a personalized virtual dental articulator that directly utilizes computed tomography (CT) data to mathematically model the complex jaw movement, providing a more efficient and accurate way of analyzing and designing dental restorations. By utilizing CT data, Frankfurt's horizontal plane was established for the mathematical modeling of virtual articulation, eliminating tedious facebow transfers. After capturing the patients' CT images and tracking their jaw movements prior to dental treatment, the jaw-tracking information was incorporated into the articulation mathematical model. The validation and analysis of the personalized articulation approach were conducted by comparing the jaw movement between simulation data (virtual articulator) and real measurement data. As a result, the proposed virtual articulator achieves two important functions. Firstly, it replaces the traditional facebow transfer process by transferring the digital dental model to the virtual articulator through the anatomical relationship derived from the cranial CT data. Secondly, the jaw movement trajectory provided by optical tracking was incorporated into the mathematical articulation model to create a personalized virtual articulation with a small Fréchet distance of 1.7 mm. This virtual articulator provides a valuable tool that enables dentists to obtain diagnostic information about the temporomandibular joint (TMJ) and configure personalized settings of occlusal analysis for patients.

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

ChatGPT in Dentistry: A Comprehensive Review

Chat generative pre-trained transformer (ChatGPT) is an artificial intelligence chatbot that uses natural language processing that can respond to human input in a conversational manner. ChatGPT has numerous applications in the health care system including dentistry; it is used in diagnoses and for assessing disease risk and scheduling appointments. It also has a role in scientific research. In the dental field, it has provided many benefits such as detecting dental and maxillofacial abnormalities on panoramic radiographs and identifying different dental restorations. Therefore, it helps in decreasing the workload. But even with these benefits, one should take into consideration the risks and limitations of this chatbot. Few articles mentioned the use of ChatGPT in dentistry. This comprehensive review represents data collected from 66 relevant articles using PubMed and Google Scholar as databases. This review aims to discuss all relevant published articles on the use of ChatGPT in dentistry.