Methods for medical applications

Background: Mandibular reconstruction has evolved significantly since its inception in the early 1900s. Currently, the fibula free flap (FFF) is considered the gold standard for mandibular and maxillary reconstructions, particularly for extensive defects, and the introduction of Extended Reality (XR) and virtual surgical planning (VSP) is revolutionizing maxillofacial surgery. Methods: This study focuses on evaluating the accuracy of using in-house cutting guides for mandibular reconstruction with FFF supported by virtual surgical planning (VSP). Planned and intraoperative osteotomies obtained from postoperative CT scans were compared in 17 patients who met the inclusion criteria. The proposed analysis included measurements of deviation angles, thickness at the centre of gravity, and the maximum thickness of the deviation volume. Additionally, a mandibular resection coding including 12 configurations was defined to classify and analyze the precision of mandibular osteotomies and investigate systematic errors. Preoperative, planned, and postoperative models have been inserted in an interactive VR environment, VieweR, to enhance surgical planning and outcome analysis. Results: The results proved the efficiency of adopting customized cutting guides and highlighted the critical role of advanced technologies such as CAD/CAM and VR in modern maxillofacial surgery. A novel coding system including 12 possible configurations was developed to classify and analyze the precision of mandibular osteotomies. This system considers (1) the position of the cutting blade relative to the cutting plane of the mandibular guide; (2) the position of the intersection axis between the planned and intraoperative osteotomy relative to the mandible; (3) the direction of rotation of the intraoperative osteotomy plane around the intersection axis from the upper view of the model. Conclusions: This study demonstrates the accuracy and reliability of in-house cutting guides for mandibular reconstruction using fibula free flaps (FFF) supported by virtual surgical planning (VSP). The comparison between planned and intraoperative osteotomies confirmed the precision of this approach, with minimal deviations observed.

Keywords: fibula free flap , maxillofacial surgery , virtual reality , virtual surgical planning , VR , VSP

Lung sequestration is a rare congenital anomaly characterized by an abnormal mass of lung tissue. It can occur in two forms, either intralobar, where the mass is embedded within the lung tissue, or extralobar, where it develops outside the lung's boundaries. In both cases, removal of this defect involves a complex minimally invasive procedure during which, in brief, the sequestration is isolated and removed. The surgical removal of the pulmonary sequestration represents a complex challenge for surgeons who have to deal with many critical aspects, such as the heartbeat-induced movement of the lung and, in pediatric cases, very limited operating space. For this reason, it is crucial to create a realistic training program that faithfully reproduces the operating conditions and enables surgeons to become familiar with this procedure. The simulator proposed in this work offers a higher level of anatomical realism than existing systems, including critical details such as the visceral pleura and accurately reproduce spatial relationships between anatomical structures. Moreover, the simulation of cardiac motion adds an element of complexity that simulate the challenges of surgery. The simulator was tested in a realistic operating room setup, validating its practical applicability.

Keywords: CAD modeling , Cardiac motion simulation , Minimally invasive thoracic surgery , Pediatric surgical simulator , Pulmonary sequestration

Manufacturing and testing electric propulsion systems can be very expensive both in economic and time terms. One of the main limiting factors is the manufacturing of ceramic components, such as alumina. However, the advent of 3D printing has made it possible to produce complex-shaped components made of technical ceramics at low cost and in reduced time. This paper illustrates the advancements made by the University of Pisa in Fused Filament Fabrication of alumina components for electric propulsion purposes. Rapid prototyping can help not only in manufacturing thrusters in a faster and cheaper way but also in producing more complex geometries, resulting in more efficient thrusters. The alumina produced through this method has achieved near-full density and a flexural strength of 430 MPa. Components manufactured with this technique successfully passed vibration and pyroshock tests.

Keywords: Additive manufacturing , Alumina , Electric propulsion , Fused filament fabrication , Hall thrusters , Hollow cathode

Surgery for Anterior Cruciate Ligament Reconstruction (ACL-R) is common and generally successful. This study investigates whether specific bony morphologies can predict surgical outcomes and patient life quality post-ACL-R. The study involves eighteen patients who underwent ACL-R, with magnetic resonance images used to reconstruct their distal femur and proximal tibia. 3D models are utilized to measure morphological parameters identified in the literature. Knee health is assessed using the KOOS questionnaire, dividing patients into two groups based on scores (≥60 and <60). Statistical analysis, including correlation matrix, t-tests (or Mann-Whitney tests), and logistic regression, is performed to evaluate the impact of bone morphology on surgical outcomes. The results indicate no significant association between bony morphology and surgical outcomes.

Keywords: 3D Modeling , ACL-R , Anterior Cruciate Ligament , Bony Morphology , KOOS questionnaire

Gait training is essential in rehabilitation as it promotes mobility, prevents falls, and increases independence in daily activities. However, accurate gait assessment in the context of telemedicine remains a challenge. Wearable inertial devices offer value by providing a minimally invasive means to accurately capture movement. This study aims to improve telemedicine-based gait analysis by introducing a method utilizing a class II-B medical device equipped with a chest-worn inertial sensor. To enhance the quality of acceleration signals, we designed and 3D printed an 18-face polyhedron for stationary calibration. We validated this method by comparing its performance to a marker-based motion capture system in a cohort of young healthy adults. The results showed an 82% reduction in the root mean square error (RMSE) of acceleration signals. Furthermore, the study showed statistical and practical significant improvements in spatial gait characteristics. Specifically, there was a 14% reduction in root mean square error for gait velocity, a 59% reduction in RMSE for step length, and a 53% reduction in RMSE for stride length.

Keywords: 3D Printing , Gait Analysis , Motion Capture , Telerehabilitation , Wearable Devices

Serious games and telerehabilitation are increasingly significant in the recovery process of neurological patients, and personalization and adaptivity in difficulty levels according to patients' abilities are crucial characteristics. This paper describes a solution for managing the settings of a multiple-serious games platform (N-RehLab) for patients with neurological disorders, avoiding the specific configuration of each serious game separately. To this end, a serious game named Assessment Serious Game (ASG) has been developed. Its outcome is a unique index, with a value between 0 and 1, which assesses the patients' capabilities based on parameters measured with a Leap Motion device. Using this index, the N-RehLab recommends a personalized rehabilitation program to medical personnel, specifying which serious games and the relative levels of difficulty the patient should engage with.

Keywords: Fine Hand Movements Rehabilitation , Leap Motion Device , Neurological Population , Serious Games , Telerehabilitation

Measurements for the functional evaluation of knee joint mobility are essential, e.g., to determine the need for invasive surgery and quantify patient outcomes. While vision-based motion capture systems offer high precision, they require specialized laboratories and trained staff. This paper introduces KARMA (Knee Angle Resistive Measurement Apparatus), an instrumented knee sleeve with six resistive flex sensors to measure knee joint angles. The proposed device features wearability, low cost, and flexibility, enabling pre- and post-operative evaluations for patients eligible for knee surgery. A case study was performed using a marker-based motion capture system as a reference. This demonstrated KARMA's ability to measure flexion-extension angles but highlighted its inability to measure abduction-adduction and internal-external rotation angles.

Keywords: Flex sensors , Joint angles , Knee arthroplasty , Motion analysis , Wearable sensors

Origami, a traditional Japanese art, has influenced engineering and shape memory materials design, enabling complex structures from simple folds. Fused filament fabrication (FFF) using polylactic acid (PLA) presents new landscapes for shape-changing structures. PLA, with its relatively low glass transition temperature (i.e. 55–65 ℃), exhibits an activable shape changing behavior within easily accessible temperature ranges and, even not at the human body temperature, can be a valuable and available material to demonstrate the intent mechanism. In this paper we investigate how PLA's shape memory properties together with tool path extrusion strategies can be harnessed within the framework of origami-inspired structures. The waterbomb origami structure has been implemented to analyze folding and shape memory mechanism. The influence of 3 different activation temperatures of the mechanism as well as 3 different printing toolpaths were evaluated on a single cell. Five elements were arranged on a cylindrical shape to ideally achieve a portion of a tracheal stent with a small diameter during the insertion phase, which increases when in place. The results obtained demonstrate the potential of combining techniques used in origami and shape memory materials for reducing the initial volume of a part, which can be useful in many applications, such as stents in the biomedical field. By identifying a suitable material, the stent can be reduced in size, minimizing the invasiveness of the surgical procedure.

Keywords: 4D printing , biomedical application , CAD design , origami , Shape memory polymer

Purpose: Minimally invasive surgery (MIS) has emerged in clinical practice to minimize surgical trauma, providing patients with faster recovery, reduced pain and complications and enhanced aesthetic results compared to traditional surgery. However, this approach increase the risk of iatrogenic damage, i.e. the accidental injury to sensitive anatomical structures (eg. nerves and vascular strcuteres) not directly visible during a percutaneous access. Augmented reality (AR) can effectively mitigate these drawbacks by overlaying graphical information onto the surgical field and providing real-time feedback, offering support in training settings and clinical practice. Implementation challenges have limited the number of case studies in the scientific literature. This study presents a novel simulation paradigm for orthopedic surgery training, filling a gap in surgical skill development resources for trainees, and demonstrating the effectiveness of this approach. Methods: The proposed methodology provides a framework for building a cost-effective and easily reproducible surgical training simulation environment. To address the challenge of mental spatial navigation during MIS procedures, the framework's rationale is to address the challenge of mental spatial navigation during MIS procedures. A surgical gesture tracking system using a commercial depth camera for comfortable simulation was developed. The principles of the acquisition system, image processing, and spatial computation mechanics are detailed to illustrate the framework's applicability. Digital environments customization with game engines to simulate expensive medical instrumentation, such as the C-arm, is also demonstrated. The simulation platform comprises a Computer Vision (CV) module, an X-ray machine simulation module, and an AR module. Results: System validation involved analysis at different framework levels. From texture analysis of acquired images to application accuracy evaluation, the influence of various parameters on system performance is demonstrated. The simulation system is a valuable tool for learning minimally invasive procedures and for developers building AR systems for medical applications. The implementation is focused on the insertion surgical devices, including screws and K-wires. This is results in real application in minimizing the risk of iatrogenic injury to neural and vascular structures. To demonstrate the effectiveness of highly reproducible accuracy between real and virtual environment an analysis of errors and accuracies is illustrated at level of different subsystems. Measurement between comparative measurement between vernier caliper and simulation system methods shows a R>0.9 with a p<0.01. Application accuracy was evaluated using the following parameters. The relative point-to-point accuracy averaged 1.02mm with a standard deviation of 2.82mm.

Keywords: Augmented reality , Image guided surgery , Minimally invasive surgery , Orthopedic

The “Augmented Humans” term refers to the opportunity to improve human possibilities by using innovative technologies such as Artificial Intelligence (AI) and Extended Reality (XR). Digital therapies, particularly suitable for those treatments requiring multiple sessions, are increasingly being adopted for home-based treatment, enabling continuous monitoring and rehabilitation for patients, thus alleviating the burden on healthcare facilities by facilitating remote therapy sessions and follow-up visits. Among these, the Mirror Therapy (MT) for patients suffering from Phantom Limb Pain (PLP) could benefit greatly. This paper proposes a novel “Augmented Humans” framework for the treatment of PLP through home-based MT; the framework is designed to consider the activities carried on by the therapy center, the patient, and the system supporting the treatment. Moreover, an XR-based solution that integrates a Deep Learning (DL) approach has been developed to provide patients with a self-testing and self-assessment tool for conducting at-home rehabilitation sessions independently, even in the absence of physical medical staff. The DL algorithm enables real-time monitoring of rehabilitation exercises and automatic provision of personalized feedback on the gesture's performance, supporting the progressive improvement of the patient's movements and his ability to adhere to the treatment plan. The technical feasibility and usability of the proposed framework have been evaluated with 23 healthy subjects, highlighting an overall positive user experience. Remarkable results were obtained in terms of automatic gesture evaluation, with macro averaged accuracy and F1-score of 95%, paving the way for the adoption of the “Augmented Humans” approach in the healthcare domain.

Keywords: Augmented humans , Augmented therapy , Deep learning , Digital therapy , Extended reality , Home-based treatment , Phantom limb pain

Designing individualized orthodontic treatments for malocclusions and misaligned teeth is a complex task due to the numerous conditions that must be met. These conditions include personal patient considerations, technical constraints, physiological restrictions, and specific instructions from orthodontists. Achieving the ideal final dental arrangement remains a significant challenge in orthodontic practice. The current process primarily involves collaboration between orthodontists and technicians, using manual procedures and specialized software. This study describes the formalization of a model designed to simplify this orthodontic procedure. The creation of a patient's final dental configuration, necessary for manufacturing all the devices for the treatment plan, is automated using a mathematical model based on current knowledge in the field. The mathematical model has been incorporated into a modeling and animation software tool, and a case study has been tested with a manufacturer specializing in orthodontic devices. The comments received highlights the usefulness of the proposed algorithm and its value as a significant resource for discussing with orthodontists.

Keywords: Design method , Knowledge formalization , Orthodontic treatment , Product customization

Background: Despite its central role in pediatric pre-surgical evaluation of drug-resistant focal epilepsy, conventional analog 18F-fluorodeoxyglucose (18F-FDG) PET/CT (aPET) systems often yield modest epileptogenic zone (EZ) detection rates (~50–60%). Silicon photomultiplier–based digital PET/CT (dPET) promises enhanced image quality, but its performance in pediatric epilepsy remains untested. Methods: We retrospectively analyzed 22 children (mean age 11.5 ± 2.6 years) who underwent interictal brain 18F-FDG PET/CT: 11 on an analog system (Discovery ST, 2018–2019) and 11 on a digital system (Biograph Vision 450, 2020–2021). Three blinded nuclear medicine physicians independently scored EZ localization and image quality (4-point scale); post-surgical histology and ≥1-year clinical follow-up served as reference. Results: The EZ was correctly identified in 8/11 analog scans (72.7%) versus 10/11 digital scans (90.9%). Average image quality was significantly higher with dPET (3.0 ± 0.9 vs. 2.1 ± 0.9; p < 0.05), and inter-reader agreement improved from good (ICC = 0.63) to excellent (ICC = 0.91). Conclusions: Our preliminary findings suggest that dPET enhances image clarity and reader consistency, potentially improving localization accuracy in pediatric epilepsy presurgical workups.

Keywords: 18F-FDG , brain , epilepsy , neuroimaging , neurological disorders , PET/CT

Breast ultrasound imaging (BUS) plays a major role in the diagnosis of breast cancer. Consequently, computerised analysis of BUS images is being intensively investigated as a means to assist physicians in clinical decision making. The traditional approach involves the use of manually-designed (hand-crafted) morphology and texture features, although Deep Learning (DL) by convolutional neural networks (CNN) has received much interest in recent years. Advances in AI research has demonstrated the effectiveness of CNN models for classification tasks; however, this approach requires large datasets, extensive computational resources and solid knowledge of CNN architecture design.Deep feature extraction from pre-trained state-of-the art CNNs is an easy-to-implement approach that allows to exploit the information already learnt on large datasets (e.g., ImageNet) while saving substantial computational time and resources. In this work we compare the effectiveness of deep features from three pre-trained CNN with that of five types of hand-crafted features for differentiating benign vs. malignant lesions on BUS images. The analysis was carried out on 252 lesions (154 benign, 98 malignant) from the recently released BrEaST dataset. The effects of image preprocessing (resizing and denoising) and of the area from which the features are computed (region of interest - ROI or bounding box around the ROI) are also investigated. The results show that the deep features outperformed the handcrafted in terms of accuracy (best accuracy 81.4% vs. 78.6%) hence demonstrating the feasibility of the method.

Keywords: Breast cancer , Computer-assisted diagnosis , Deep learning , Hand-crafted features , Ultrasound images

Breast ultrasound images play a pivotal role in assessing the nature of suspicious breast lesions, particularly in patients with dense tissue. Computerized analysis of breast ultrasound images has the potential to assist the physician in the clinical decision-making and improve subjective interpretation. We assess the performance of conventional features, deep learning features and ensemble schemes for discriminating benign versus malignant breast lesions on ultrasound images. A total of 19 individual feature sets (1 morphological, 2 first-order, 10 texture-based, and 6 CNN-based) were included in the analysis. Furthermore, four combined feature sets (Best by class; Top 3, 5, and 7) and four fusion schemes (feature concatenation, majority voting, sum and product rule) were considered to generate ensemble models. The experiments were carried out on three independent open-access datasets respectively containing 252 (154 benign, 98 malignant), 232 (109 benign, 123 malignant), and 281 (187 benign, 94 malignant) lesions. CNN-based features outperformed the other individual descriptors achieving levels of accuracy between 77.4% and 83.6%, followed by morphological features (71.6%–80.8%) and histograms of oriented gradients (71.4%–77.6%). Ensemble models further improved the accuracy to 80.2% to 87.5%. Fusion schemes based on product and sum rule were generally superior to feature concatenation and majority voting.

Keywords: breast cancer , breast ultrasound images , convolutional neural networks , radiomics , texture

Purpose: To compare PET-derived metrics between digital and analogue PET/CT in hyperparathyroidism, and to assess whether machine learning (ML) applied to quantitative PET parameters can distinguish parathyroid adenoma (PA) from hyperplasia (PH). Methods: From an initial multi-centre cohort of 179 patients, 86 were included, comprising 89 PET-positive lesions confirmed histologically (74 PA, 15 PH). Quantitative PET parameters—maximum standardised uptake value (SUVmax), metabolic tumour volume (MTV), target-to-background ratio (TBR), and maximum diameter—along with serum PTH and calcium levels, were compared between digital and analogue PET scanners using the Mann–Whitney U test. Receiver operating characteristic (ROC) analysis identified optimal threshold values. ML models (LASSO, decision tree, Gaussian naïve Bayes) were trained on harmonised quantitative features to distinguish PA from PH. Results: Digital PET detected significantly smaller lesions than analogue PET, in both metabolic volume (1.32 ± 1.39 vs. 2.36 ± 2.01 cc; p < 0.001) and maximum diameter (8.35 ± 4.32 vs. 11.87 ± 5.29 mm; p < 0.001). PA lesions showed significantly higher SUVmax and TBR compared to PH (SUVmax: 8.58 ± 3.70 vs. 5.27 ± 2.34; TBR: 14.67 ± 6.99 vs. 8.82 ± 5.90; both p < 0.001). The optimal thresholds for identifying PA were SUVmax > 5.89 and TBR > 11.5. The best ML model (LASSO) achieved an AUC of 0.811, with 79.7% accuracy and balanced sensitivity and specificity. Conclusions: Digital PET outperforms analogue system in detecting small parathyroid lesions. Additionally, ML analysis of PET-derived metrics and PTH may support non-invasive distinction between adenoma and hyperplasia.

Keywords: 18F-choline , Artificial intelligence , Automated classification , Digital PET/CT , Hyperparathyroidism , Metabolic imaging

Stroke survivors often face significant upper limb impairments, necessitating frequent therapy and rehabilitation to regain function. However, frequent visits to medical centers for treatment sessions pose challenges to the patient. We present GripDome V2, an enhanced prototype for customizable grip strength measurement and rehabilitation. It enables unsupervised home use, improving accessibility. The device features an inflatable silicone dome connected to a PLA base, with grip strength measured via a pressure transducer and microcontroller, by which the patient's progress can be measured and quantified. The prototype offers adjustable resistance levels, facilitating personalized rehabilitation progress tracking. 3D printing is used for plastic components and silicone molds, enhancing affordability and customization. Experimental tests demonstrate GripDome's ability to provide consistent and reliable measurements even under prolonged use. The pneumatic system allows dynamic adjustment of resistance levels, tailoring rehabilitation paths to individual needs. Additionally, a basic game-like exercise has been integrated to motivate users, making the rehabilitation process more engaging and enjoyable. The device shows promise for reliable quantitative grip strength assessment and at-home rehabilitation for stroke survivors.

Keywords: 3D printing, additive manufacturing, rehabilitation , Hand rehabilitation , Personalized rehabilitation , Pneumatic , Rehabilitation devices , Silicone , Soft actuator

This paper presents a novel workflow for designing and fabricating patient-specific cranial implants and surgical guides using advanced CAD and 3D printing technologies. Developed at the T3Ddy Lab at Meyer Children's Hospital, the methodology includes close collaboration between engineers and neurosurgeons, allowing for rapid prototyping and real-time adjustments. The workflow involves designing a cutting guide for precise bone resection and a custom cranial implant for anatomical reconstruction, both using titanium alloy for its biocompatibility and mechanical strength. The setup enhances efficiency, ensuring precise device fit and improving surgical outcomes. The paper presents the general framework established to design custom made medical devices for skull repairing surgery. The CAD modeling phase of the surgical guide and cranial implant are described in detail, highlighting developed solutions to improve the effectiveness of the technology. The proposed workflow was validated in multiple clinical cases, demonstrating improved surgical precision, reduced operative time, and enhanced recovery.

Keywords: 3D Printing , CAD , Cranial Implant , Cranioplasty , Craniotomy , Surgical Guide

The increase in dental diseases and the high demand for dental aesthetics in developed countries have favoured the growth of demand in the dental market for restorative treatments for damaged teeth and beyond. Today, modern prosthetic dentistry aims to find new materials whose biomechanical behaviour approximates that of natural enamel when subjected to functional loads. Computer-aided design, engineering and manufacturing (CAD-CAE and CAD-CAM) can provide new technologies for the design and production of dental devices. The aim of this research is to analyse the bio-mechanical behaviour of new materials such as zirconia and silica-based ceramics; the effects of functional loads on dental prostheses are examined, starting with the acquisition of a lower first molar with a Cone Beam Computed Tomography (CBCT) scan and the subsequent CAD design of a 3D model for the restoration. These technologies will enable the fabrication of dental structures with solid materials and/or cell structures characterised by variable anisotropy and porosity, providing patients with stable results over time. This will help minimise waiting times and production costs, as well as provide customised solutions of high precision and reduced invasiveness.

Keywords: Basic reconstruction materials , Biomechanical modelling , Dental devices , Mandibular kinematics , Prosthetic crown

Intraosseous access is a critical emergency procedure for introducing fluids and blood components into the systemic circulation, necessitating thorough training for healthcare personnel. In collaboration with Meyer Children's University Hospital IRCCS, the Department of Industrial Engineering at the University of Florence has developed a high-fidelity, low-cost pediatric simulator specifically for intraosseous access in the proximal tibia. Using Materialise Mimics 26® software, anonymized tomographic images of a 5-year-old patient were processed to obtain the three-dimensional geometry of relevant anatomical structures: tibia, fibula, distal femur, muscles, and skin. These models were further refined with Geomagic DesignX® for the final simulator design. The simulator features silicone replicas for soft tissues, with rigid structures and silicone casting molds produced through Fused Deposition Modeling. Preliminary tests were conducted to select suitable materials, which were then evaluated by specialized medical personnel using the EZ-IO® device to ensure accurate haptic feedback. The resulting simulator provides a realistic platform for intraosseous access training, enabling healthcare practitioners to enhance their procedural skills and ensure patient safety.

Keywords: additive manufacturing , intraosseous access , pediatric mannequin

The long-term effectiveness of osseointegrated implants is heavily dependent on the short-term stability, primarily achieved immediately after surgery through a mechanical connection between the bone and the implant. The most common implant designs nowadays are straight and rely on screw or press-fit fixtures. Despite the promising results achieved by current transfemoral implants, the incidence of early failures and complications is still high. Starting from the hypothesis that a patient-specific approach could lead to better primary stability immediately post-surgery, this study aims to investigate the effect of implant design on primary stability. This was performed by analyzing two patient-specific implants, customized according to the medullary canal morphology, and a simple straight implant as the reference standard. To quantitatively assess the primary stability, a comparative computational analysis was conducted to examine the effective contact area, the relative micromotion, and the stress distribution at the interface between the bone and the implant stem during a static load-bearing exercise. The results showed that implants that follow the curvature of the residual femur provide lower micromotion values and a wider contact area, with a reduction of up to 30.4% and an increase of 10.8%, respectively, compared to the straight design, leading to a more homogeneous load distribution.

Keywords: finite element analysis , load-bearing exercise , osseointegration , patient-specific design , primary stability , transfemoral implant