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.

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.

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.

Background: Three-dimensional facial scan technologies, such as stereophotogrammetry, are frequently used in oral and maxillofacial surgery, dentistry, and plastic surgery to assess patient outcomes and plan surgical procedures. Most facial scanners rely on stationary clinical systems, which provide high accuracy and reliability in generating 3D representations of the human face; however, they are cost-intensive and immobile. Recently, smartphone-based facial scan applications that use stereophotogrammetry have been developed. These applications are cost-effective and more accessible than existing stationary systems. Methods: In this study, we compared the accuracy of three different smartphone applications—EM3D, Polycam, and ScandyPro—on an iPhone 14 Pro, which utilizes Apple’s LiDAR (Light Detection and Ranging) technology, to a stationary system by 3DMD, which is considered a gold standard in many studies. Results: The applications included in the study have demonstrated the capability to perform accurately in clinical settings, with mean surface distances of 1.46 mm for EM3D, 1.66 mm for Polycam, and 1.61 mm for ScandyPro. Conclusions: These results suggest that smartphone-based facial scan systems could be a viable alternative, particularly in resource-limited settings. However, further research and collaboration between academia and industry are necessary to fully integrate these technologies into clinical practice.

Background: Three-dimensional (3D) printing technologies such as Stereolithography (SLA) and Digital Light Processing (DLP) are widely used in dental implantology for the fabrication of surgical guides. While both methods offer clinical viability, their comparative accuracy, efficiency, and material consumption remain subjects of debate. Objectives: To compare the dimensional accuracy, printing time, and material consumption of dental surgical guides fabricated using an SLA printer (Formlabs Form 3B) and a DLP printer (NextDent 5100) at various printing orientations. Methods: A standardized surgical guide was designed and printed on both printers across seven orientations (0–90°). Five guides per angle were fabricated per technology (n = 35 per printer), scanned, and compared with the CAD reference to evaluate dimensional accuracy. Printing time and resin consumption were recorded. Statistical analyses included the Shapiro–Wilk test and Mann–Whitney U test (α = 0.05). Results: Within the evaluated printers and resins, SLA-printed guides demonstrated slightly lower Root Mean Square (RMS) values in most regions, especially in occlusal and drill hole surfaces, while DLP guides tended to undersize Optimal accuracy was observed at 45° for SLA and 60° for DLP. Material consumption was lower for the SLA printer compared with the DLP printer, but SLA required longer printing time (90–200 min vs. 25–75 min for DLP). Conclusions: Both technologies produced clinically acceptable guides under the tested conditions. The tested SLA printer tended to offer slightly higher accuracy and material efficiency, whereas the DLP printer achieved shorter printing times, supporting its use in high-throughput workflows. Printing orientation significantly influenced accuracy and resource use.

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.