Methods for medical applications

The miniaturization and integration of micro-electromechanical systems (MEMS) have progressively expanded the capabilities of advanced prosthetic hands, enabling not only the replication of human sensory and motor functions but also the implementation of sophisticated mechatronic control, precise manipulation, and adaptive responses to environmental interactions. The aim of this scoping review is to systematically map and evaluate current research on MEMS-integrated prosthetic hands, highlighting how MEMS sensors and mechanical modelling approaches contribute to dynamic control, biomechanical performance and user-centered functionality. Comparative analyses of different modelling techniques and MEMS applications indicate that MEMS-based sensing systems substantially improve the mechanical performance of prosthetic hands by enabling accurate force modulation, enhancing motion stability during dynamic tasks and supporting efficient signal acquisition for real-time control. These features lead to more precise control, smoother movements and enhanced dexterity during activities of daily living (ADL), broadening the functional capabilities of the devices. Microsurgical and neural interface aspects were also examined, including physiological considerations relevant to neural integration and common challenges related to prosthetic implantation, such as potential immunological responses to materials. The increasing role of MEMS in the development of smart, biomimetic prosthetic hands underscores new opportunities for creating highly adaptive devices, optimizing dexterity and environmental interaction and ultimately improving users' quality of life.

Keywords: biomechanical modelling , dynamic control , mechatronic systems , MEMS sensors , prosthetic hand , smart hand prosthesis , smart MEMS

Spinal surgery demands exceptional theoretical knowledge and practical skills, with pedicle screw procedures posing significant risks due to proximity to critical anatomical structures. This study validates a virtual reality (VR) simulation platform for training in pedicle screw arthrodesis using patient-specific vertebral drilling templates. The practical simulation of a specific case study was evaluated with both neurosurgical residents and medical students. Results demonstrate that while experienced residents completed simulated procedures significantly faster than students (p < 0.01), these latter ones showed marked improvement across consecutive training sessions (p < 0.001). The most substantial performance gains occurred between the first and second trials, highlighting the rapid learning curve facilitated by the VR environment. System Usability Scale assessments revealed high satisfaction with the simulation platform, with participants emphasizing the value of risk-free repetitive practice. Qualitative feedback from experienced participants confirmed the precision and realism of the training environment as critical factors contributing to their efficiency. This validation confirms that VR simulation platforms offer an effective training environment for complex spinal procedures, providing a safe space for skill development before clinical application, particularly when incorporating innovative surgical aids such as patient-specific drilling templates.

Keywords: Pedicle screw insertion , Surgical template , Training , Vertebra , Virtual reality

Smell loss is a sensory impairment that has major consequences in many areas of daily life and for which current therapies are insufficient. A prosthesis-type technology enabling patients to sample their olfactory environment has not yet been developed. The aim of our study was, therefore, to test whether stimulation of the intranasal trigeminal system by a device combining an artificial nose with an intranasal electrical stimulator will enable patients to detect and discriminate odorant molecules. Four experiments involving normosmic individuals (n = 13) and patients with olfactory loss (n = 52) showed that individuals were able to detect their olfactory environment using the device. For discrimination, the results are less clear-cut but show that most patients can distinguish between two external stimuli. Although this substitution approach does not allow patients to smell real odors, it is a genuine first substitution solution that we could imagine offering to patients in the future.

Soft tissue simulation is a complex and challenging research field with applications across various domains, including engineering, design, medicine, and biomechanics. By accurately modeling soft tissues, virtual and augmented reality applications—such as medical training simulations, image-guided surgical support systems, and injury analysis—can more effectively replicate the behavior of complex human body structures. While many models have been proposed to simulate soft bodies, a major challenge has always been the heavy computational workload required to model and reproduce elastic and mechanical interactions between structures in the human body, usually not optimal for real-time applications. The aim of this work is to present an innovative workflow to simulate soft tissues in real time, leveraging the computational power offered by graphic processing units (GPUs). The proposed workflow addresses the challenge of improving the real-time performance of soft bodies deformation based on Extended Position Based Dynamics (XPBD) by proposing an original two-fold integrated approach: firstly, we optimized the XPBD model for parallel execution by using graph coloring. This technique allowed us to run the simulation without the use of atomic operations, while retaining the faster convergence speed of the parallel Gauss-Seidel solver. Subsequently, we applied it on a soft body represented as a volumetric regular lattice of particles, instead of the well-known representation based on tetrahedral or hexagonal meshes, allowing us to apply the deformation results even on highly polygonal meshes. The effectiveness of the developed system is evaluated through a series of tests aimed at proving its accuracy and performance, with a focus on providing realistic visual representation alongside computational efficiency.

Keywords: computer graphics , graph coloring , lattice model , soft tissues simulation , XPBD

This study presents a scalable, nontoxic method for fabricating a 3D printable polycaprolactone (PCL)-collagen peptides composite filament via solvent-assisted blending and a customized desktop filament extrusion system. Virgin and recycled PCL feedstocks were used to study the matrix characteristics. Scanning electron microscopy, X-ray diffraction, differential scanning calorimetry, thermogravimetric analysis, and intrinsic fluorescence spectroscopy confirmed that the composite maintains PCL's inherent crystalline and thermal properties. It also exhibits intrinsic bioactive capabilities provided by the collagen peptides. Filaments with diameters suitable for the Fused Filament Fabrication 3D printing were obtained. Tests demonstrated that the recycled matrix and the lab-scale process reduce the tensile modulus of the material. At the same time, collagen peptides enhanced tensile stiffness by creating intermolecular hydrogen bonding with the PCL. The biocompatibility of the composite has also been confirmed, while degradability studies have shown the tunability of the PCL degradation rate. Additionally, examples of 3D-printed scaffolds based on Triply Periodic Minimal Surfaces have been successfully fabricated using the PCL-collagen peptide filament. Therefore, this study demonstrates that integrating collagen peptides into a PCL matrix represents a viable, nontoxic, affordable, and promising approach for developing customized and bioactive implants, scaffolds, and other regenerative medicine applications.

Keywords: 3D printing , Bioactive materials , Collagen peptides , Filament fabrication , Polycaprolactone , Tissue engineering

Purpose: To compare condylar path elements (CPEs) in edentulous patients using fully adjustable (FA) and semiadjustable (MS) digital articulators. Materials and Methods: A total of 10 patients with at least one edentulous arch were included. Arch relation records were digitally set in the articulators using two approaches: The MS group employed standard mean occlusal parameter values, while the FA group used individual values obtained using a digital arch motion-tracking device. Differences in CPEs, represented as Δ-values, were statistically analyzed using nonparametric Wilcoxon signed-rank test and post-hoc Tukey test. These analyses evaluated overall differences between FA and MS articulators, identified the regions with the greatest Δ-errors, and determined the percentage of movement required for statistical significance. Results: CPEs differed significantly between MS and FA articulators. Significant variations were observed in individual CPEs (P < .001), with motion percentage significantly influencing Δ-values (P < .001). Notably, within the first 20% of CPE MS pathways, significant differences were found within the initial 2 mm of movement, a critical range for prosthetic rehabilitation. Conclusions: This study highlights statistically significant differences in CPEs between MS and FA digital articulators, particularly within the initial 2 mm of movement. These findings underscore the importance of precise CPE replication for occlusal design of complete dentures.

Keywords: Aged , Dental Arch , Dental Articulators , Female , Humans , Jaw Relation Record , Jaw, Edentulous , Male , Mandibular Condyle , Middle Aged , Mouth, Edentulous , Movement

The analysis of molecular interactions between antigens and antibodies is crucial for understanding the immunological mechanisms underlying the immune response and for developing effective therapies against various diseases. In this context, the ability to distinguish between protein interfaces that form stable and unstable complexes is a key step in the design of therapeutic antibodies and vaccines. In recent years, deep learning models have provided advanced tools for biomedical research. This work introduces a novel approach to analyzing antibody-antigen interactions, and in particular SARS-CoV-2 spike protein-targeting antibodies, using a Siamese Neural Network specifically designed to integrate depth maps with geometric descriptors of molecular surfaces. By combining these representations, the model captures geometrical shape complementarity to differentiate between stable and unstable protein complexes. The network was trained using image-based representations of antigens and antibodies interfaces enriched with geometric descriptors, using data that include binders and non-binders of the SARS-CoV-2 spike protein receptor-binding domain. The deep learning network operates by comparing feature vectors representing these molecular surfaces; pairs with closer vectors in feature space are associated with stable interactions, while those with more distant vectors suggest instability. Extensive testing with different configurations achieved an accuracy of 90%, demonstrating the robustness of this approach to predict interactions. This innovative integration of artificial intelligence, depth maps and geometric descriptors offers promising applications for designing novel antibodies and vaccines.

Keywords: Antibodies, Viral , COVID-19 , Deep Learning , Humans , Neural Networks, Computer , Protein Binding , SARS-CoV-2 , Spike Glycoprotein, Coronavirus

Optical tool tracking is the process of determining the 6DoF pose of an object in real time using visual sensor streams and image processing algorithms. It enables spatial localization in applications such as robotics, medical imaging, augmented reality, and precision manufacturing. However, existing solutions often involve tight coupling between hardware and software, complicating the management and benchmarking of different tracking systems. This paper presents the Modular Optical Tool Tracking (MOTT) framework, a unified platform for implementing, integrating, and benchmarking optical tracking solutions. A requirement-based design approach was adopted, using Quality Function Deployment (QFD) to systematically derive technical features and identify design drivers that guided the architecture of the framework. The resulting software framework standardizes the concept of an optical tracking method, featuring a flexible and extensible architecture based on object-oriented principles. Two marker-based tracking methods using an RGB camera as video source were evaluated through the developed framework. The presented case studies showcased the use of the framework for method implementation and comparison within a unified pipeline, reporting computational metrics such as frame rate, CPU and memory usage, and providing pose visualizations. The proposed framework enables standardized evaluation and benchmarking of optical tracking systems, and provides a foundation for future extensions involving non-optical tracking modalities and large-scale comparative studies. The implementation is openly available at https://github.com/tooltip-optical-tracking/mott-framework.

Keywords: Benchmarking , Modular framework , Optical tracking , Pose estimation , QFD , Tool tracking

Hand rehabilitation requires consistent, repetitive exercises that can often reduce patient motivation, especially in home-based therapy. This study introduces ReHAb Playground, a deep learning-based system that merges real-time gesture recognition with 3D hand tracking to create an engaging and adaptable rehabilitation experience built in the Unity Game Engine. The system utilizes a YOLOv10n model for hand gesture classification and MediaPipe Hands for 3D hand landmark extraction. Three mini-games were developed to target specific motor and cognitive functions: Cube Grab, Coin Collection, and Simon Says. Key gameplay parameters, namely repetitions, time limits, and gestures, can be tuned according to therapeutic protocols. Experiments with healthy participants were conducted to establish reference performance ranges based on average completion times and standard deviations. The results showed a consistent decrease in both task completion and gesture times across trials, indicating learning effects and improved control of gesture-based interactions. The most pronounced improvement was observed in the more complex Coin Collection task, confirming the system's ability to support skill acquisition and engagement in rehabilitation-oriented activities. ReHAb Playground was conceived with modularity and scalability at its core, enabling the seamless integration of additional exercises, gesture libraries, and adaptive difficulty mechanisms. While preliminary, the findings highlight its promise as an accessible, low-cost rehabilitation platform suitable for home use, capable of monitoring motor progress over time and enhancing patient adherence through engaging, game-based interactions. Future developments will focus on clinical validation with patient populations and the implementation of adaptive feedback strategies to further personalize the rehabilitation process.

Keywords: gesture recognition , hand rehabilitation , home-based rehabilitation , serious game

Extended reality (XR) technologies, including augmented reality (AR), mixed reality (MR), and virtual reality (VR), are increasingly transforming surgical disciplines. Oral and maxillofacial surgery, given the anatomical complexity and precision required, is particularly suited to benefit from these innovations. The aim of this scoping review was to map the clinical applications of XR in oral and maxillofacial surgery and compare the most commonly used commercial headsets. A comprehensive literature search was performed across the PubMed, Scopus, and Web of Science databases, covering studies published between January 2018 and June 2025. The PRISMA-ScR guidelines were followed. Overall, 759 records were identified in the database and subsequent citation search, of which 90 met the predefined inclusion criteria. The studies were categorized based on the type of application: training, preoperative planning, virtual surgical planning (VSP), and intraoperative guidance. The analysis revealed that AR and MR are predominantly employed in intraoperative procedures, enhancing surgical navigation and precision. In contrast, fully immersive VR is primarily employed in training, preoperative simulation, and VSP, while its adoption in the postoperative phase remains a promising area for future exploration. XR technologies hold significant potential in oral and maxillofacial surgery and are evolving rapidly and diversifying in their applications. Further research and regulatory validation are needed to support clinical integration and evaluate long-term outcomes.

Keywords: Augmented reality , Maxillofacial surgery , Mixed reality , Oral surgery , Virtual reality

Background: – We aimed to describe a protocol and related surgical treatment for mentoplasty using virtual surgical planning (VSP) and piezosurgery. Methods: – Twelve patients requiring mentoplasty underwent VSP based on preoperative computed tomography. Virtual osteotomy was defined and the bone fragment was repositioned. Cutting and repositioning guides were exported as STL files and created using a 3-dimensional (3D) printer with biocompatible material. Plates were preblended on the printed model. Postoperatively, morphometric analysis was performed by comparing the planned and postoperative 3D models: distance between the apex of the cusp of 43 and 33 and lower edge of the chin; distance between the lower edge of the right and left mental foramen and osteotomy; and distance between the mandibular lingual fossa and pogonion. Results: – Six cutting guides were made composite and six were monobloc. The repositioning guides were all made monobloc. No adverse intraoperative events were observed. Postoperative transient hypoesthesia was reported in 1 case. The average distance between the planned and postoperative 3D models of the chin was recorded in translation (1.91 mm), rotation (right, 1.38 mm; left, 1.30 mm), and cranial-caudal repositioning (right, 1.39 mm; left, 1.23 mm). VSP and 3D printing have improved surgical precision and reproducibility; they help the surgeon decide the extent of chin movement and the most suitable type of surgical guide in advance. Conclusions: – Mentoplasty using 3D-printed guides and piezosurgery is simple, accurate, and safe. The costs of guide production are reduced using 3D printers by approximately €400–€800 per case compared with industrial printers.

No abstract

Background: Intraosseous (IO) access is a life-saving procedure when intravenous access is impossible in pediatric emergencies. Proficiency in IO technique, particularly drill handling, patient positioning, and site identification, is essential to minimize complications and improve outcomes. Methods: To address the high cost and limited accessibility of commercial simulators, low-cost task trainers were developed using 3D printing and silicone casting. These trainers focused on drill handling, allowing trainees to progressively acquire procedural skills. Results: Initial validation was conducted with experts at Meyer Children's Hospital during a European Pediatric Immediate Life Support (EPILS) course. Structured feedback indicated high satisfaction with tactile realism and educational value. Based on this feedback, a second prototype with a fluid simulation system was implemented to enhance realism further. Nursing professionals, often first responders in emergencies, particularly benefited from the improved version, reporting increased confidence and competence. Conclusion: The cost-effective IO task trainers offer a scalable, realistic alternative to commercial models, expanding access to essential training. Their affordability and usability make them suitable for diverse healthcare settings, enhancing preparedness for pediatric emergencies.

Keywords: 3D printing , Clinical simulation , Intraosseous access , Nursing education , Pediatric emergency care

Shoulder pain is a common complaint in clinical practice, often requiring MRI for diagnosis and treatment planning. Accurate segmentation of shoulder bones from MRI scans is crucial for 3D anatomical assessment. Deep learning (DL) models have shown promise in automating segmentation, yet previous studies have primarily focused on the humerus and scapula, with limited investigation of the clavicle. This study compares DL models for automatic segmentation of the humerus, scapula, and clavicle from MRI. We explore multiple MRI sequences, including PDW SPAIR (coronal and sagittal) and PD-TSE, to assess their impact on segmentation accuracy. DL architectures, including 3D U-Net and 3D Attention U-Net, were trained, and their hyperparameters were optimized. Performance was assessed using the Dice Similarity Coefficient (DSC), Hausdorff Distance (HD), and volume variation. Additionally, morphological measurements were analyzed to quantify segmentation errors. The best segmentation performance was achieved using a combination of coronal and sagittal PDW SPAIR sequences with Attention U-Net, yielding DSC values of 0.878 (clavicle), 0.843 (scapula), and 0.931 (humerus), and minimal HD. Our study highlights the importance of multimodal MRI input and model optimization for shoulder segmentation. The integration of morphological assessments enhances clinical applicability, providing reliable automated segmentation for surgical planning and medical training.

Keywords: Convolutional Neural Network , Deep Learning , Image Segmentation , Magnetic Resonance Imaging , Shoulder

In the recent years, the production of titanium powders has increased considerably due to the increase in uses, especially with additive manufacturing, in various application fields, including aeronautics and biomedical. Consequently, it is necessary to estimate the environmental sustainability of production processes while also considering the rapid technological evolution that characterizes them. This can be done with the prospective life cycle assessment (LCA) methodology, which estimates the future environmental impacts of an emerging technology or a new technology still in the R&D phase. This study generates prospective life cycle inventory (LCI) data of titanium powder production for additive manufacturing made with recent developments of Electrode Induction Gas Atomization (EIGA) reactors, which can be used in the prospective LCA. All data for the prospective LCI relating to argon consumption, water consumption, and electrical energy consumption of the EIGA reactor were extracted from the patents using a partly new approach. These data concern the experimental results (expressed through data ranges) obtained from laboratory-scale tests, qualitative and quantitative information to support the scale-up of the experimental data, and information to evaluate the data reliability.

Keywords: Additive manufacturing , Eco-design , Prospective LCA , Prospective LCI , Technological forecasting

The scarcity of medical imaging data poses a significant challenge for the development of automated segmentation models. This study addresses this limitation by focusing on 3D reconstruction of shoulder bones. We evaluated manual and automatic segmentation methods: DL-based using MONAILabel and commercial software (Materialise Mimics), using only 14 CT scans manually labeled for the humerus, scapula, and clavicle. To mitigate data limitations, we applied data augmentation techniques and trained convolutional neural networks optimized for medical imaging, systematically tuning hyperparameters to assess their impact. Segmentation performance was measured using Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD). Our DL model achieved DSC scores of 0.96 (humerus), 0.90 (scapula), and 0.82 (clavicle). Fractures were preserved, enabling accurate 3D reconstruction and measurement. The commercial software achieved similar accuracy. This study highlights the feasibility of using DL for accurate shoulder segmentation in data-constrained scenarios and supports the use of open-source tools as viable alternatives in clinical workflows. Future work will focus on expanding the dataset and further refining the CNN models.

Keywords: Computed Tomography , Convolutional Neural Network , Deep Learning , Image Segmentation , Shoulder

Purpose: This study investigates the static mechanical behavior of a non-modular metallic hip prosthesis through Finite Element Method (FEM) simulations, assessing compliance with ASTM F2996-13 standards. The analysis specifically evaluates how key geometric parameters, such as trunnion extension and orientation angles (adduction and flexion), affect stress distributions within the prosthesis. Methodology: A three-dimensional finite element model of a Ti6Al4V alloy hip stem was developed. Boundary and loading conditions were defined according to the standard: the distal portion of the stem was fully constrained 90 mm below the head center, and a static load of 2300 N was applied at the head center along the directions defined by the adduction and flexion angles. A mesh sensitivity analysis was conducted to ensure convergence, and stresses were evaluated. Parametric analyses varying trunnion extension and orientation angles were performed to quantify their impact on local stress concentration. Results: The findings revealed that even minor deviations in the adduction and flexion angles significantly impact the stress distribution, with the potting-level region being particularly sensitive. Additionally, the extension of the trunnion led to notably increased stress concentrations, especially at the prosthesis neck, highlighting its critical influence in implant design. Conclusions: Comparison with existing literature and standard reference data exposed discrepancies primarily attributed to variations in FEM model setups and parameter selections. This emphasizes the necessity of clearly specifying trunnion extension and orientation angles in numerical analyses to ensure consistent stress predictions, supporting the development of safer and longer-lasting hip implants. Future research should extend these analyses to different prosthesis geometries, aiming to develop generalized predictive frameworks applicable to diverse biomechanical scenarios.

Keywords: ASTM F2996-13 , finite element analysis , hip prosthesis , prosthesis design , technical standards

Stroke affects approximately fifteen million people worldwide annually, with impaired hand function being one of its most common effects. Hemiparetic post-stroke patients suffer a mild loss of strength involving one side of their body: though not fully debilitating, it still impacts their everyday life activities. To prevent mobility deterioration, patients must perform well-focused and repetitive exercises during chronic rehabilitation. Virtual Reality (VR) emerges as an interesting tool in this framework, offering the possibility of training and measuring the patient's performances in ecologically valid, engaging, and challenging environments. In recent years, there has been an increasing diffusion of accessible head-mounted displays that enhance the sense of realism and immersion in a virtual scene. To explore the feasibility and efficacy of VR immersion and game mechanics in rehabilitation programs, a VR system that allows users to rehabilitate their motor skills in a home-based environment has been designed and tested considering standard measures related to usability, immersion, workload, and simulator sickness, and with the involvement of rehabilitation experts. The results demonstrate how users and experts have received the application positively, highlighting the potential of VR applications for the future development of home-based rehabilitation programs.

Keywords: Exergames , Post-Stroke Rehabilitation , Virtual Reality

Nowadays, universities address topics like CAD modeling and additive manufacturing to teach students important aspects of engineering and design. However, the development of a prototype is an iterative process composed of different phases that range from the definition of the problem to the evaluation of the concept The paper describes how to integrate all these phases for active learning in the course Computer-Aided Design and Mechanical Prototyping, which is part of the Master's Degree in Mechanical Engineering program at Politecnico di Milano. The final project assigned as a case study to students consists in the development of a rehabilitative hand exoskeleton. The paper presents how students faced the different phases of the development process till the final prototype and its evaluation with healthy subjects and some post-stroke patients. The methodology applied to the course was really appreciated by students, in particular, the experience of designing different devices from scratch. They were challenged in different areas, like mechanics, electronics, informatics, and manual work, which allowed them to range widely during the design and manufacturing process and to extend considerably their knowledge.

Keywords: additive manufacturing , CAD modeling , engineering education , hand exoskeleton , interactive applications

Manual handling tasks, both in daily activities and at work, require high dexterity and the ability to move objects of different shapes and sizes. However, musculoskeletal disorders that can arise due to aging, disabilities, overloading, or strenuous work can impact the natural capabilities of the hand with serious repercussions both in working and daily activities. To address this, researchers have been developing and proving the benefits of wrist exoskeletons. This paper, which is Part II of a study on wrist exoskeletons, presents and summarizes wearable wrist exoskeleton devices intended for use in rehabilitation, assistance, and occupational fields. Exoskeletons considered within the study are those available either in a prototyping phase or on the market. These devices can support the human wrist by relieving pain or mitigating fatigue while allowing for at least one movement. Most of them have been designed to be active (80%) for higher force/torque transmission, and soft for better kinematic compliance, ergonomics, and safety (13 devices out of 24, more than 50%). Electric motors and cable transmission (respectively 11 and 9 devices, out of 24, i.e., almost 50% and 40%) are the most common due to their simplicity, controllability, safety, power-to-weight ratio, and the possibility of remote actuation. As sensing technologies, position and force sensors are widely used in all devices (almost 90%). The control strategy depends mainly on the application domain: for rehabilitation, CPM (control passive motion) is preferred (35% of devices), while for assistance and occupational purposes, AAN (assistance-as-needed) is more suitable (38% of the devices). What emerges from this analysis is that, while rehabilitation and training are fields in which exoskeletons have grown more easily and gained some user acceptance (almost 18 devices, of which 4 are available on the market), relatively few devices have been designed for occupational purposes (5, with only 2 available on the market) due to difficulties in meeting the acceptance and needs of users. In this perspective, as a result of the state-of-the-art analysis, the authors propose a conceptual idea for a portable soft wrist exoskeleton for occupational assistance.

Keywords: industrialization issues , occupational sector , rehabilitation field , wearable devices , wrist exoskeletons