Bite force and its relationship to jaw shape in domestic dogs.

Previous studies based on two-dimensional methods have suggested that the great morphological variability of cranial shape in domestic dogs has impacted bite performance. Here, we used a three-dimensional biomechanical model based on dissection data to estimate the bite force of 47 dogs of various breeds at several bite points and gape angles. In vivo bite force for three Belgian shepherd dogs was used to validate our model. We then used three-dimensional geometric morphometrics to investigate the drivers of bite force variation and to describe the relationships between the overall shape of the jaws and bite force. The model output shows that bite force is rather variable in dogs and that dogs bite harder on the molar teeth and at lower gape angles. Half of the bite force is determined by the temporal muscle. Bite force also increased with size, and brachycephalic dogs showed higher bite forces for their size than mesocephalic dogs. We obtained significant covariation between the shape of the upper or lower jaw and absolute or residual bite force. Our results demonstrate that domestication has not resulted in a disruption of the functional links in the jaw system in dogs and that mandible shape is a good predictor of bite force.

Dimensional Accuracy in 3D Printed Medical Models: A Follow-Up Study on SLA and SLS Technology.

Background: With the rise of new 3D printers, assessing accuracy is crucial for obtaining the best results in patient care. Previous studies have shown that the highest accuracy is achieved with SLS printing technology; however, SLA printing technology has made significant improvements in recent years. Methods: In this study, a realistic anatomical model of a mandible and skull, a cutting guide for mandibular osteotomy, and a splint for orthognathic surgery were replicated five times each using two different 3D printing technologies: SLA and SLS. Results: The SLA group had a median trueness RMS value of 0.148 mm and a precision RMS value of 0.117 mm. The SLS group had a median trueness RMS value of 0.144 mm and a precision RMS value of 0.096 mm. There was no statistically significant difference in RMS values between SLS and SLA technologies regarding trueness. Regarding precision, however, the RMS values for SLS technology were significantly lower in the splint and cutting guide applications than those printed with SLA technology. Conclusions: Both 3D printing technologies produce modern models and applications with equally high dimensional accuracy. Considering current cost pressures experienced by hospitals, the lower-cost SLA 3D printer is a reliable choice for point-of-care 3D printing.

Case report: One-stage craniectomy and cranioplasty digital workflow for three-dimensional printed polyetheretherketone implant for an extensive skull multilobular osteochondosarcoma in a dog.

Objective: To report a digital workflow for use and long-term outcome of cranioplasty with a 3D-printed patient-specific Polyetheretherketone (PEEK) implant in a 12-y-old German Shepherd dog after surgical removal of an extensive occipital bone multilobular osteochondrosarcoma (MLO). Study design: Retrospective case report. Animal: A 12-year-old neutered female German Shepherd dog was presented with facial deformity, blindness, tetraparesis, and ataxia. Magnetic resonance imaging (MRI) and computed tomography (CT) identified a large skull-based mass extending extra-and intracranially with severe compression of the cerebellum and occipital lobes of the cerebrum. Methods: One-stage decompressive craniectomy using virtual surgical planned 3D-printed craniotomy cutting guides and the Misonix BoneScalpel® and reconstruction with a patient-specific 3D-printed PEEK cranial implant. Results: 3D-printed craniectomy cutting guides allowed an adequate fit of the cranial implant to the original skull. Misonix BoneScalpel® allowed performing a safe and extensive craniectomy. Postoperative CT (8 weeks after surgery) confirmed the PEEK cranial implant to be in place and without implant rejection. Clinically, the neurological examination identified only a right-hind limb delay in proprioception 8 weeks postoperatively, which remained unchanged at 18 months after surgery. Adjunctive treatment included metronomic chemotherapy. Eighteen months after surgery the dog passed away for reasons unrelated to the MLO, no implant-related complications were reported. Conclusion: 3D-printed craniectomy cutting guides, patient-specific PEEK cranial implant, and metronomic chemotherapy can lead to a successful long-term outcome in dogs with extensive skull MLO. Clinical significance: PEEK is an alternative biomaterial that can be used successfully for skull reconstruction.

Evaluation of the Dimensional Accuracy of Robot-Guided Laser Osteotomy in Reconstruction with Patient-Specific Implants-An Accuracy Study of Digital High-Tech Procedures.

Background/Objective: With the rapid advancement in surgical technologies, new workflows for mandibular reconstruction are constantly being evaluated. Cutting guides are extensively employed for defining osteotomy planes but are prone to errors during fabrication and positioning. A virtually defined osteotomy plane and drilling holes in robotic surgery minimize potential sources of error and yield highly accurate outcomes. Methods: Ten mandibular replicas were evaluated after cutting-guided saw osteotomy and robot-guided laser osteotomy following reconstruction with patient-specific implants. The descriptive data analysis summarizes the mean, standard deviation (SD), median, minimum, maximum, and root mean square (RMS) values of the surface comparison for 3D printed models regarding trueness and precision. Results: The saw group had a median trueness RMS value of 2.0 mm (SD ± 1.7) and a precision of 1.6 mm (SD ± 1.4). The laser group had a median trueness RMS value of 1.2 mm (SD ± 1.1) and an equal precision of 1.6 mm (SD ± 1.4). These results indicate that robot-guided laser osteotomies have a comparable accuracy to cutting-guided saw osteotomies, even though there was a lack of statistical significance. Conclusions: Despite the limited sample size, this digital high-tech procedure has been shown to be potentially equivalent to the conventional osteotomy method. Robotic surgery and laser osteotomy offers enormous advantages, as they enable the seamless integration of precise virtual preoperative planning and exact execution in the human body, eliminating the need for surgical guides in the future.

Patient-specific implants made of 3D printed bioresorbable polymers at the point-of-care: material, technology, and scope of surgical application.

BACKGROUND: Bioresorbable patient-specific additive-manufactured bone grafts, meshes, and plates are emerging as a promising alternative that can overcome the challenges associated with conventional off-the-shelf implants. The fabrication of patient-specific implants (PSIs) directly at the point-of-care (POC), such as hospitals, clinics, and surgical centers, allows for more flexible, faster, and more efficient processes, reducing the need for outsourcing to external manufacturers. We want to emphasize the potential advantages of producing bioresorbable polymer implants for cranio-maxillofacial surgery at the POC by highlighting its surgical applications, benefits, and limitations. METHODS: This study describes the workflow of designing and fabricating degradable polymeric PSIs using three-dimensional (3D) printing technology. The cortical bone was segmented from the patient's computed tomography data using Materialise Mimics software, and the PSIs were designed created using Geomagic Freeform and nTopology software. The implants were finally printed via Arburg Plastic Freeforming (APF) of medical-grade poly (L-lactide-co-D, L-lactide) with 30% ß-tricalcium phosphate and evaluated for fit. RESULTS: 3D printed implants using APF technology showed surfaces with highly uniform and well-connected droplets with minimal gap formation between the printed paths. For the plates and meshes, a wall thickness down to 0.8 mm could be achieved. In this study, we successfully printed plates for osteosynthesis, implants for orbital floor fractures, meshes for alveolar bone regeneration, and bone scaffolds with interconnected channels. CONCLUSIONS: This study shows the feasibility of using 3D printing to create degradable polymeric PSIs seamlessly integrated into virtual surgical planning workflows. Implementing POC 3D printing of biodegradable PSI can potentially improve therapeutic outcomes, but regulatory compliance must be addressed.

In Silico Decoding of Parkinson's: Speech & Writing Analysis.

Background: Parkinson's disease (PD) has transitioned from a rare condition in 1817 to the fastest-growing neurological disorder globally. The significant increase in cases from 2.5 million in 1990 to 6.1 million in 2016, coupled with predictions of a further doubling by 2040, underscores an impending healthcare challenge. This escalation aligns with global demographic shifts, including rising life expectancy and a growing global population. The economic impact, notably in the U.S., reached $51.9 billion in 2017, with projections suggesting a 46% increase by 2037, emphasizing the substantial socio-economic implications for both patients and caregivers. Coupled with a worldwide demand for health workers that is expected to rise to 80 million by 2030, we have fertile ground for a pandemic. Methods: Our transdisciplinary research focused on early PD detection through running speech and continuous handwriting analysis, incorporating medical, biomedical engineering, AI, and linguistic expertise. The cohort comprised 30 participants, including 20 PD patients at stages 1-4 on the Hoehn and Yahr scale and 10 healthy controls. We employed advanced AI techniques to analyze correlation plots generated from speech and handwriting features, aiming to identify prodromal PD biomarkers. Results: The study revealed distinct speech and handwriting patterns in PD patients compared to controls. Our ParkinsonNet model demonstrated high predictive accuracy, with F1 scores of 95.74% for speech and 96.72% for handwriting analyses. These findings highlight the potential of speech and handwriting as effective early biomarkers for PD. Conclusions: The integration of AI as a decision support system in analyzing speech and handwriting presents a promising approach for early PD detection. This methodology not only offers a novel diagnostic tool but also contributes to the broader understanding of PD's early manifestations. Further research is required to validate these findings in larger, diverse cohorts and to integrate these tools into clinical practice for timely PD pre-diagnosis and management.

Comparison of Artificial Intelligence-Based Applications for Mandible Segmentation: From Established Platforms to In-House-Developed Software.

Medical image segmentation, whether semi-automatically or manually, is labor-intensive, subjective, and needs specialized personnel. The fully automated segmentation process recently gained importance due to its better design and understanding of CNNs. Considering this, we decided to develop our in-house segmentation software and compare it to the systems of established companies, an inexperienced user, and an expert as ground truth. The companies included in the study have a cloud-based option that performs accurately in clinical routine (dice similarity coefficient of 0.912 to 0.949) with an average segmentation time ranging from 3'54″ to 85'54″. Our in-house model achieved an accuracy of 94.24% compared to the best-performing software and had the shortest mean segmentation time of 2'03″. During the study, developing in-house segmentation software gave us a glimpse into the strenuous work that companies face when offering clinically relevant solutions. All the problems encountered were discussed with the companies and solved, so both parties benefited from this experience. In doing so, we demonstrated that fully automated segmentation needs further research and collaboration between academics and the private sector to achieve full acceptance in clinical routines.

CNN-Based Identification of Parkinson's Disease from Continuous Speech in Noisy Environments.

Parkinson's disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson's disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.