Temporomandibular joint (TMJ) replacement prostheses often face limitations in accommodating translational movements, leading to unnatural kinematics and loading conditions, which affect functionality and longevity. Here, we investigate the potential of functionally graded materials (FGMs) in TMJ prostheses to enhance mandibular kinematics and reduce joint reaction forces. We develop a functionally graded artificial cartilage for the TMJ implant and evaluate five FGM designs: hard, hard-soft, and three FGM gradients with gradual transitions from 90% hard material to 0%, 10%, and 20%. These designs are 3D printed, mechanically tested under quasi-static compression, and simulated under physiological conditions. Results from computational modeling and experiments are compared to an intact mandible during incisal clenching and left group biting. The FGM design with a transition from 90% to 0% hard material improves kinematics by 19% and decreases perfomance by 3%, reduces joint reaction forces by 8% and 10%, and increases mandibular movement by 20% and 88% during incisal clenching and left group biting, respectively. These findings provide valuable insights for next-generation TMJ implants.

Objective: Mandibular reconstruction using patient-specific cage implants is a promising alternative to the vascularized free flap reconstruction for nonirradiated patients with adequate soft tissues, or for patients whose clinical condition is not conducive to microsurgical reconstruction. This study aimed to assess the biomechanical performance of 3D printed patient-specific cage implants designed with a semi-automated workflow in a combined cadaveric and retrospective case series study. Methods: We designed cage implants for two human cadaveric mandibles using our previously developed design workflow. The biomechanical performance of the implants was assessed with the finite element analysis (FEA) and quasi-static biomechanical testing. Digital image correlation (DIC) was used to measure the full-field strains and validate the FE models by comparing the distribution of maximum principal strains within the bone. The retrospective study of a case series involved three patients, each of whom was treated with a cage implant of similar design. The biomechanical performance of these implants was evaluated using the experimentally validated FEA under the scenarios of both mandibular union and nonunion. Results: No implant or screw failure was observed prior to contralateral bone fracture during the quasi-static testing of both cadaveric mandibles. The FEA and DIC strain contour plots indicated a strong linear correlation (r = 0.92) and a low standard error (SE=29.32με), with computational models yielding higher strain values by a factor of 2.7. The overall stresses acting on the case series’ implants stayed well below the yield strength of additively manufactured (AM) commercially pure titanium, when simulated under highly strenuous chewing conditions. Simulating a full union between the graft and remnant mandible yielded a substantial reduction (72.7±1.5%) in local peak stresses within the implants as compared to a non-bonded graft. Conclusions: This study shows the suitability of the developed semi-automated workflow in designing patient-specific cage implants with satisfactory mechanical functioning under demanding chewing conditions. The proposed workflow can aid clinical engineers in creating reconstruction systems and streamlining pre-surgical planning. Nevertheless, more research is still needed to evaluate the osteogenic potential of bone graft insertions.

Augmented reality (AR) has shown potential in computer-aided surgery. It allows for the visualization of hidden anatomical structures as well as assists in navigating and locating surgical instruments at the surgical site. Various modalities (devices and/or visualizations) have been used in the literature, but few studies investigated the adequacy/superiority of one modality over the other. For instance, the use of optical see-through (OST) HMDs has not always been scientifically justified. Our goal is to compare various visualization modalities for catheter insertion in external ventricular drain and ventricular shunt procedures. We investigate two AR approaches: (1) 2D approaches consisting of a smartphone and a 2D window visualized through an OST (Microsoft HoloLens 2), and (2) 3D approaches consisting of a fully aligned patient model and a model that is adjacent to the patient and is rotationally aligned using an OST. 32 participants joined this study. For each visualization approach, participants were asked to perform five insertions after which they filled NASA-TLX and SUS forms. Moreover, the position and orientation of the needle with respect to the planning during the insertion task were collected. The results show that participants achieved a better insertion performance significantly under 3D visualizations, and the NASA-TLX and SUS forms reflected the preference of participants for these approaches compared to 2D approaches.

Optical-based navigation systems are widely used in surgical interventions. However, despite their great utility and accuracy, they are expensive and require time and effort to setup for surgeries. Moreover, traditional navigation systems use 2D screens to display instrument positions causing the surgeons to look away from the operative field. Head mounted displays such as the Microsoft HoloLens may provide an attractive alternative for surgical navigation that also permits augmented reality visualization. The HoloLens is equipped with multiple sensors for tracking and scene understanding. Mono and stereo-vision in the HoloLens have been both reported to be used for marker tracking, but no extensive evaluation on accuracy has been done to compare the two approaches. The objective of our work is to investigate the tracking performance of various camera setups in the HoloLens, and to study the effect of the marker size, marker distance from camera, and camera resolution on marker locating accuracy. We also investigate the speed and stability of marker pose for each camera setup. The tracking approaches are evaluated using ArUco markers. Our results show that mono-vision is more accurate in marker locating than stereo-vision when high resolution is used. However, this comes at the expense of higher frame processing time. Alternatively, we propose a combined low-resolution mono-stereo tracking setup that outperforms each tracking approach individually and is comparable to high resolution mono tracking, with a mean translational error of 1.8 ± 0.6mm for 10cm marker size at 50cm distance. We further discuss our findings and their implications for navigation in surgical interventions.

Augmented reality (AR) permits the visualization of pre-operative data in the surgical field of view of the surgeon. This requires the alignment of the AR device’s coordinate system with the used navigation/tracking system. We propose a multimodal marker approach to align an AR device with a tracking system: in our implementation, an electromagnetic tracking system (EMTS). The solution makes use of a calibration method which determines the relationship between a 2D pattern detected by an RGB camera and an electromagnetic sensor of the EMTS. This allowed the projection of a 3D skull model on its physical counterpart. This projection was evaluated using a monocular camera and an optical see-through device (HoloLens 2) (https://www.microsoft.com/en-us/hololens/) achieving an accuracy of less than 2.5 mm in the image plane of the HoloLens 2 (HL2). Additionally, 10 volunteers participated in a user study consisting of an alignment task of a pointer with 25 projections viewed through the HL2. The participants achieved a mean error of 2.7 1.3 mm and 2.9 2.9^∘ in positional and orientation error. This study showcases the feasibility of the approach, provides an evaluation of the alignment, and finally, discusses its advantages and limitations.

Purpose: In minimally invasive spring-assisted craniectomy, surgeons plan the surgery by manually locating the cranial sutures. However, this approach is prone to error. Augmented reality (AR) could be used to visualize the cranial sutures and assist in the surgery planning. The purpose of our work is to develop an AR-based system to visualize cranial sutures, and to assess the accuracy and usability of using AR-based navigation for surgical guidance in minimally invasive spring-assisted craniectomy. Methods: An AR system was developed that consists of an electromagnetic tracking system linked with a Microsoft HoloLens. The system was used to conduct a study with two skull phantoms. For each phantom, five sutures were annotated and visualized on the skull surface. Twelve participants assessed the system. For each participant, model alignment using six anatomical landmarks was performed, followed by the participant delineation of the visualized sutures. At the end, the participants filled a system usability scale (SUS) questionnaire. For evaluation, an independent optical tracking system was used and the delineated sutures were digitized and compared to the CT-annotated sutures. Results: For a total of 120 delineated sutures, the distance of the annotated sutures to the planning reference was 2.4 ± 1.2 mm. The average delineation time per suture was 13 ± 5 s. For the system usability questionnaire, an average SUS score of 73 was obtained. Conclusion: The developed AR-system has good accuracy (average 2.4 mm distance) and could be used in the OR. The system can assist in the pre-planning of minimally invasive craniosynostosis surgeries to locate cranial sutures accurately instead of the traditional approach of manual palpation. Although the conducted phantom study was designed to closely reflect the clinical setup in the OR, further clinical validation of the developed system is needed and will be addressed in a future work.

Confounding bias is a crucial problem when applying machine learning to practice, especially in clinical practice. We consider the problem of learning representations independent to multiple biases. In literature, this is mostly solved by purging the bias information from learned representations. We however expect this strategy to harm the diversity of information in the representation, and thus limiting its prospective usage (e.g., interpretation). Therefore, we propose to mitigate the bias while keeping almost all information in the latent representations, which enables us to observe and interpret them as well. To achieve this, we project latent features onto a learned vector direction, and enforce the independence between biases and projected features rather than all learned features. To interpret the mapping between projected features and input data, we propose projection-wise disentangling: a sampling and reconstruction along the learned vector direction. The proposed method was evaluated on the analysis of 3D facial shape and patient characteristics (N = 5011). Experiments showed that this conceptually simple method achieved state-of-the-art fair prediction performance and interpretability, showing its great potential for clinical applications.

Three-dimensional (3D) stereophotogrammetry is a novel imaging technique that has gained popularity in the medical field as a reliable, non-invasive, and radiation-free imaging modality. It uses optical sensors to acquire multiple 2D images from different angles which are reconstructed into a 3D digital model of the subject's surface. The technique proved to be especially useful in craniofacial applications, where it serves as a tool to overcome the limitations imposed by conventional imaging modalities and subjective evaluation methods. The capability to acquire high-dimensional data in a quick and safe manner and archive them for retrospective longitudinal analyses, provides the field with a methodology to increase the understanding of the morphological development of the cranium, its growth patterns and the effect of different treatments over time.This review describes the role of 3D stereophotogrammetry in the evaluation of craniosynostosis, including reliability studies, current and potential clinical use cases, and practical challenges. Finally, developments within the research field are analyzed by means of bibliometric networks, depicting prominent research topics, authors, and institutions, to stimulate new ideas and collaborations in the field of craniofacial 3D stereophotogrammetry.We anticipate that utilization of this modality's full potential requires a global effort in terms of collaborations, data sharing, standardization, and harmonization. Such developments can facilitate larger studies and novel deep learning methods that can aid in reaching an objective consensus regarding the most effective treatments for patients with craniosynostosis and other craniofacial anomalies, and to increase our understanding of these complex dysmorphologies and associated phenotypes.

Landmarking of 3D facial surface scans is an important analysis step in medical and biological applications, such as genome-wide association studies (GWAS). Manual landmarking is often employed with considerable cost and rater dependent variability. Landmarking automatically with minimal training is therefore desirable. We apply statistical ensemble methods to improve automated landmarking of 3D facial surface scans. Base landmarking algorithms using features derived from 3D surface scans are combined using either bagging or stacking. A focus is on low training complexity of maximal 40 training samples with template based landmarking algorithms that have proved successful in such applications. Additionally, we use correlations between landmark coordinates by introducing a search strategy guided by principal components (PCs) of training landmarks. We found that bagging has no useful impact, while stacking strongly improves accuracy to an average error of 1.7 mm across all 21 landmarks in this study, a 22% improvement as compared to a previous, comparable algorithm. Heritability estimates in twin pairs also show improvements when using facial distances from landmarks. Ensemble methods allow improvement of automatic, accurate landmarking of 3D facial images with minimal training which is advantageous in large cohort studies for GWAS and when landmarking needs change or data quality varies.

In this paper, we present a novel approach to automatic 3D facial landmarking using 2D Gabor wavelets. Our algorithm considers the face to be a surface and uses map projections to derive 2D features from raw data. Extracted features include texture, relief map, and transformations thereof. We extend an established 2D landmarking method for simultaneous evaluation of these data. The method is validated by performing landmarking experiments on two data sets using 21 landmarks and compared with an active shape model implementation. On average, landmarking error for our method was 1.9 mm, whereas the active shape model resulted in an average landmarking error of 2.3 mm. A second study investigating facial shape heritability in related individuals concludes that automatic landmarking is on par with manual landmarking for some landmarks. Our algorithm can be trained in 30 min to automatically landmark 3D facial data sets of any size, and allows for fast and robust landmarking of 3D faces.