BackgroundIn recent years, the growing adoption of Virtual Reality (VR) and 3D printing technologies has revolutionized surgical training by providing innovative opportunities for hands-on education. This study investigates the combined use of VR and 3D printed personalized anatomical models and cutting guides within the field of oral and maxillofacial oncologic surgery.Materials and methods: A mandibular tumour case was developed using the proposed approach, integrating both virtual and physical tools. Feedback was gathered from twelve surgical residents regarding their understanding of the case, the effectiveness of the immersive and three-dimensional technologies, and their overall satisfaction with the training experience.ResultsParticipants reported enhanced comprehension of complex surgical scenarios and valued the practical utility of the VR simulator combined with 3D printed models. The immersive environment facilitated skill acquisition in a risk-free setting.ConclusionThe findings underscore the significant added value of integrating VR and 3D printing technologies in surgical training, preparation, and simulation. This approach offers a safe, effective training platform that improves readiness for complex procedures in oncologic surgery and has the potential to be extended to other branches of maxillofacial surgery.

Background and Objective: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for approximately 20.5 million deaths annually, nearly one-third of all global deaths. Despite its central role in cardiology, echocardiographic assessment remains subject to significant inter- and intra-observer variability, particularly based on manual frame selection and segmentation. This limitation has driven increasing interest in Deep Learning (DL) solutions capable of enabling more objective, reproducible, and efficient analyses. Methods: This paper introduces CardioSmartAssist, a deep learning framework for automatic left ventricular segmentation and multi-cycle ejection fraction estimation relying solely on echocardiographic videos. The system integrates frame-by-frame segmentation visualisation, anomaly detection, and volume tracking to enhance clinical usability. Moreover, a key feature is its continuous learning mechanism, which allows clinician-corrected segmentations to be stored and used for progressive model refinement. Results: The framework is based on a MultiResUNet architecture, trained on public (EchoNet-Dynamic) and proprietary (CardioSmartSet) datasets, achieving Dice Coefficient Scores of 0.9328 and 0.9189, respectively. On the held-out test set, the EF estimated by the system showed a mean absolute difference of 10% compared with clinically reported EF values, which is lower than the typical inter-operator variability of approximately 13%. Conclusion: CardioSmartAssist resulted to be a promising tool for consistent cardiac evaluations, improving access to diagnostics, and enhancing clinical decision-making through smart assistance.

Musculoskeletal disorders are frequent workplace injuries, especially during manual lifting activities. They are influenced by posture, lifting technique, and repetitive movements. Various ergonomic assessment methods exist, but each has limitations: observational methods can be slow and prone to error, while contact sensor-based methods, although more accurate, tend to be invasive and expensive. Recent developments have focused on non-contact sensors, such as RGB and RGB-D cameras, combined with Deep Learning algorithms and observational methods, to improve efficiency and reliability. This study proposes a solution combining a skeleton-based Deep Learning algorithm for Human Pose Estimation with an observational method for postural assessment. Using an RGB camera, four lifting techniques (stoop, squat, semi-squat, and weightlifter) were analyzed, evaluating their impact on worker posture through the REBA score. Among handle-assisted lifts, the stoop and weightlifter techniques showed the lowest average maximum REBA scores (5.375 and 6.125), while the squat and semi-squat techniques scored highest at 7. The semi-squat without handles showed the greatest postural risk (7.875). Future work will integrate 3D data and validate the approach with a larger, more diverse population.

Graf’s Developmental dysplasia of the hip (DDH) ultrasound screening is a validated method for early diagnosis. This study aimed to validate a sequential artificial-intelligence-assisted workflow following the Graf method and to determine whether computer-aided diagnosis (CAD) improves clinicians’ Checklist I assessment. This retrospective single-center study included 1803 images from 679 examinations. ResTransUNet segmented the eight Checklist I structures and classified image reportability. 15 clinicians assessed a separate independent set of 100 images without and, four weeks later, with CAD support. Checklist I classification achieved 95.61% accuracy, 96.65% precision, 97.74% recall, and an F1-score of 97.19%. Of the 1024 downstream images, 846 completed the workflow. Binary DDH classification achieved 81.09% accuracy, 82.67% sensitivity, and 80.59% specificity; exact Graf-type agreement was 40.07%. Mean absolute errors were 3.59∘ for the α angle and 11.91∘ for the β angle. Mean clinician accuracy increased from 72.3% to 90.2% with CAD, a paired improvement of 17.9 percentage points (95% CI, 10.3–25.4; p<0.001; Cohen’s dz=1.31); all 15 clinicians improved. The workflow provided accurate Checklist I reportability assessment and substantially improved clinician performance, although β-angle estimation and exact Graf classification require further refinement before prospective clinical implementation.

OBJECTIVE:Joint replacement surgery, also known as arthroplasty, is a common procedure that restores mobility and relieves pain in patients with severe joint pathologies. Despite being considered routine, arthroplasties are complex interventions with potential complications and variable clinical outcomes. Accurate evaluation of replaced joint mobility to ensure implant stability within the patient's functional range of motion (ROM) is a major challenge in postoperative care. However, the reliability of current assessment methods is limited due to their lack of standardized and quantitative tools. This study presents a patient-specific Mixed Reality (MR) framework designed to enhance postoperative evaluation in joint replacement with a focus on total hip arthroplasty (THA). METHODS:The proposed system enables objective quantification and MR visualization of prosthesis biomechanics by integrating ROM simulation and 3D modeling, promoting explainability and interpretability of surgery outcomes. A retrospective analysis of 67 THAs was performed to compare simulated ROM results with clinical assessments and literature benchmarks. Additionally, surgeons evaluated the system's clinical relevance and usability through a preliminary study, including completion of the System Usability Scale (SUS). RESULTS:Simulated ROM measurements showed good agreement with both clinical assessments and established literature reference values across ten movements commonly examined in orthopedic practice. The MR tool demonstrated high accuracy, repeatability, and potential to support postoperative decision-making, with usability testing yielding a favorable median SUS score of 82.5, indicating strong acceptance among clinicians. CONCLUSION:The patient-specific MR framework provides a reliable, quantitative, and interpretable method for assessing prosthetic joint performance after replacement, supporting its integration into postoperative workflows for improved surgical outcome assessment.

Cutting guides are widely used in cranio-maxillofacial surgery by providing mechanical references for precise bone resections and reducing intraoperative variability. Nevertheless, their rigid and patient-specific design requires dedicated CAD modeling and fabrication, making them time-consuming to produce and difficult to adapt when anatomical conditions or bone surfaces change. This work presents a hybrid surgical cutting guide that combines a physically adjustable device with Augmented Reality (AR) feedback to support intraoperative alignment in maxillofacial osteotomies. The concept merges the tactile reliability of conventional guides with the adaptability of digital visualization, enabling surgeons to fine-tune the cutting plane directly through AR tracking. Registration was performed using cephalometric landmarks and an inside-out tracking approach with HoloLens 2, allowing precise superimposition of virtual cutting planes onto 3D-printed mandibular models. The system was evaluated by both expert and novice operators under three feedback conditions: no AR, holographic overlay, and real-time distance guidance. Results showed that AR feedback was associated with improved positional accuracy, with mean linear deviations of 1.27±0.71mm and angular errors of 4.46±3.27°. Operator experience influenced overall performance, yet enhanced feedback compensated part of this variability. Combining physical and digital guidance can yield more adaptable, precise, and reusable osteotomy tools, paving the way for flexible surgical assistance in clinical settings.

Augmented Reality (AR) is increasingly being adopted in surgical procedures. Identifying accurate and reliable tracking systems can enhance the effectiveness of AR assisted surgery. The study presents a benchmarking platform to evaluate the performance of different optical surgical tools tracking systems for AR applications, addressing the need for standardized comparison of tracking systems’ accuracy, repeatability, and reliability. A custom-built, cost-effective benchmarking platform was developed, and different quantitative evaluation metrics were employed to assess performances of Marker-Based (MB) tracking systems. Two types of measurements were performed: static, where metrics such as Target Registration Error (TRE) and tip stability as a measure of jitter were estimated, and dynamic, involving a measure of the deviation between the tracked tooltip trajectory and its GT. Three AR MB-tracking methods were tested: method 1, mono-RGB sensor tracking a planar image target; method 2, RGB-depth sensor tracking passive spherical markers; and method 3, mono-IR sensor tracking IR active markers. Significant performance differences were observed. Method 3 achieved the lowest TRE value of 3.578 ± 2.836 mm. Method 1 exhibited the best tip stability with a jitter value of 1.435 ± 0.824 mm. Method 2 obtained the minimum trajectory distance in dynamic tests. The benchmarking platform demonstrated its effectiveness in evaluating and comparing different AR tracking methods. This study enables the optimization of tracking system selection for use in the operating room, and facilitating the integration of AR as a supportive technology in surgical practice.

Objectives: To develop and validate a fully digital, surgeon-oriented interactive framework for final dental occlusion alignment in orthognathic patients. Methods: A digital framework integrating automatic alignment, using a two-dimensional Iterative Closest Point (2D ICP) algorithm with geometric corrections and optimization-based refinement, and an interactive graphical user interface (GUI) for standardized manual refinement were implemented in MATLAB. Validation was performed on preoperative digital models from 21 orthognathic patients. Obtained digital occlusions were compared with manually articulated physical models, considered the reference standard. Translational and rotational discrepancies were assessed before and after manual refinement. Results: The workflow was successfully completed in all 21 patients. Automatic alignment showed the largest translational discrepancy along the Y-axis (−2.26 ± 2.10 mm), which significantly improved after manual refinement (−1.17 ± 0.92 mm; p = 0.02). A small but significant increase in Z-axis discrepancy was observed (−0.91 ± 0.58 mm vs −1.19 ± 0.44 mm; p = 0.04), whereas X-axis differences were not significant (p = 0.22). Overall translational RMSE decreased significantly (1.83 ± 0.91 mm vs 1.09 ± 0.39 mm; p = 0.001). Individual rotational errors were unchanged (p = 0.08; p = 0.39; p = 0.43 for X-, Y- and Z-axes). Rotational RMSE significantly decreased from 2.00° ± 1.01° to 1.35° ± 0.25° (p = 0.01). Manual refinement reduced error variability, with standard deviations decreasing after standardized refinement. Conclusions: The proposed framework achieved high agreement with the reference manual occlusion. Automatic alignment provided a reliable starting point, but standardized manual refinement remained essential.

Artificial intelligence (AI), and specifically deep learning (DL) models, are rapidly gaining traction in healthcare to analyze complex medical images and support clinical decision-making. However, DL models are often considered black boxes due to the lack of a clear explanation when providing predictions. Explainable artificial intelligence (XAI) methods are emerging as an effective way to make models explainable for developers and provide interpretable outputs for clinicians. This review presents a taxonomy of the most widely used XAI methods for image classification, with related benefits and drawbacks. Furthermore, it examines whether the type of classifier affects the choice of an explainability technique and investigates the impact of black boxes on the healthcare environment. The analysis considered papers published between January 2020 and July 2025 in Scopus and Google Scholar, utilizing the PRISMA guidelines to enhance reporting. Sixty-nine papers were identified as suitable for classifying XAI methods in four categories based on backpropagation, perturbation, attention, and concept. The results show increased use of backpropagation-based techniques, which offer simple and intuitive heatmaps. Perturbation-based methods are frequently employed to validate model robustness, but they are computationally expensive. Finally, concept-based and attention-based approaches are less widespread but represent a promising solution towards explanations that align with human semantics and reflect the intrinsic model behavior. Future research should focus on combined approaches and concept methods that generate explanations in the same semantic field as clinicians and are computationally suitable for healthcare environments, paving the way for transparent and clinically reliable DL systems.

Objectives: To develop and validate a fully digital, surgeon-oriented interactive framework for final dental occlusion alignment in orthognathic patients. Methods: A digital framework integrating automatic alignment, using a two-dimensional Iterative Closest Point (2D ICP) algorithm with geometric corrections and optimization-based refinement, and an interactive graphical user interface (GUI) for standardized manual refinement were implemented in MATLAB. Validation was performed on preoperative digital models from 21 orthognathic patients. Obtained digital occlusions were compared with manually articulated physical models, considered the reference standard. Translational and rotational discrepancies were assessed before and after manual refinement. Results: Twenty-one patients were included. Manual refinement reduced the greatest translational directional bias along the Y-axis (MV: -2.26 ± 2.1 to -1.17 ± 0.92 mm) and significantly decreased the magnitude of corresponding error (MAV: 2.54 ± 1.73 to 1.26 ± 0.78 mm; p = 0.002). Error magnitude also decreased along the X-axis (1.12 ± 0.84 to 0.38 ± 0.31 mm; p = 0.002), whereas a small increase was observed along the Z-axis (0.93 to 1.21 mm; p = 0.02). Overall translational RMSE improved significantly (1.83 ± 0.91 to 1.09 ± 0.39 mm; p = 0.001). Among rotational components, yaw showed the greatest reduction in error magnitude (MAV: 2.61 ± 1.91° to 0.83 ± 0.71°; p = 0.001), while no statistically significant changes were detected for pitch (p = 0.09) or roll (p = 0.13). Rotational RMSE decreased from 2.00 ± 1.01° to 1.35 ± 0.25° (p = 0.01). Conclusions: The proposed pipeline was completed for all cases. Automatic alignment provided a reliable starting point, but standardized manual refinement remained essential.

Cutting guides are widely used in cranio-maxillofacial surgery by providing mechanical references for precise bone resections and reducing intraoperative variability. Nevertheless, their rigid and patient-specific design requires dedicated CAD modeling and fabrication, making them time-consuming to produce and difficult to adapt when anatomical conditions or bone surfaces change. This work presents a hybrid surgical cutting guide that combines a physically adjustable device with Augmented Reality (AR) feedback to support intraoperative alignment in maxillofacial osteotomies. The concept merges the tactile reliability of conventional guides with the adaptability of digital visualization, enabling surgeons to fine-tune the cutting plane directly through AR tracking. Registration was performed using cephalometric landmarks and an inside-out tracking approach with HoloLens 2, allowing precise superimposition of virtual cutting planes onto 3D-printed mandibular models. The system was evaluated by both expert and novice operators under three feedback conditions: no AR, holographic overlay, and real-time distance guidance. Results showed that AR feedback was associated with improved positional accuracy, with mean linear deviations of 1.27 +/- 0.71 mm and angular errors of 4.46 +/- 3.27 degrees. Operator experience influenced overall performance, yet enhanced feedback compensated part of this variability. Combining physical and digital guidance can yield more adaptable, precise, and reusable osteotomy tools, paving the way for flexible surgical assistance in clinical settings.

Tracking systems are essential in various fields, such as health and manufacturing industries, enabling mapping between the real and digital worlds. Amongst others, Augmented Reality Tracking Systems (ARTS) are more recent and less explored. This work proposes a quantitative metrological methodology to evaluate ARTS tooltip tracking performance, facilitating benchmarking, parameter optimization, and system selection for specific tasks. A specific 3D-printed measuring artifact is proposed to guide tooltip positioning. Tracking accuracy and precision are estimated, highlighting the effects of influence factors. The methodology was tested with two commercial state-of-the-art ARTSs using marker-based tooltips, i.e., a Microsoft HoloLens 2 and a stereo camera system equipped with Intel RealSense SR305 cameras. Metrological characteristics are evaluated, and the Euclidean distance expanded uncertainty at a conventional 95% confidence level is estimated as 5.071mm for the HoloLens 2 and 6.800mm for the stereo system, resulting in a superior metrological performance of HoloLens 2 under the specified conditions. This study provides a standardized approach for quantitatively comparing AR tracking systems, offering valuable insights for optimizing their use in specific applications and, innovatively in the context of ARTS, associates measurement uncertainty with tracked distance values.

The integration of emotional monitoring technologies in logistics represents a significant leap in enhancing operational efficiency and worker well-being. As the logistics evolves, understanding the acceptance and adoption of such technologies becomes crucial. This study employs the extended Technology Acceptance Model (TAM) as a framework to investigate the determinants influencing logistics workers' acceptance of emotional monitoring technologies, with a focus on facial expression recognition (FER), electroencephalography (EEG) and a bundle of physiological measures including Electromyography (EMG), Electrocardiography (ECG), and Galvanic Skin Conductance (GSC). We aim to explore how perceived usefulness (PU) and perceived ease of use (PEOU), core constructs of TAM, along with external variables belonging to social, individual and system levels, contribute to this acceptance. The questionnaire was administered to 45 warehouse operators from a logistics company in Italy. Results show that TAM models are able to explain the acceptance of these technologies in the examined working environment.

Patient-specific dressings for diabetic foot ulcers (DFUs) must follow the individual lesion, yet pub- lished image-to-fabrication pipelines rely on depth sensing and seldom report the accuracy of the fabricated object. We present IMTOP, a low-cost pipeline that turns a single calibrated RGB photograph into a printable patient-specific patch, with a validation framework centred on the patch itself. Abso- lute scale is recovered from two operator-placed points on a reference of known length; the wound contour is offset by a perilesional margin and extruded to a watertight STL solid. The patch error is decomposed into segmentation, scale and printing terms, measured on 149 public clinical DFU images (uncalibrated, in pixel units) with three segmenters and on 18 printed phantoms of known geometry, the scale term coming from a Monte Carlo simulation of calibration-point placement. By mean magnitude segmentation was the largest term (6.4% for the Segment Anything Model, against 5.0% for scale at an assumed 3 px placement noise and 0.69% for printing; combined 8.1%), whereas by variance scale prevailed for two of three segmenters; phantoms were reconstructed to 0.32% area error, pointing to wound-border ambiguity as the main clinical error source. Mask overlap correlated with, but did not determine, the patch error, and the residual under-coverage sized a segmenter-specific perilesional offset covering the wound in 95% of the evaluated cases.

BACKGROUND AND OBJECTIVE:Despite the availability of several commercial solutions for predicting the soft tissue outcomes of maxillofacial surgeries, none have proven sufficiently reliable for routine clinical use. This study proposes a 3D methodology for predicting soft tissue displacement following maxillofacial surgery without relying on mechanical modeling, unlike most existing approaches. METHODS:Pre- and post-operative Cone Beam Computed Tomography scans of patients with class III malocclusion were collected. Tailored image processing and volume reconstruction techniques were applied to semi-automatically generate 3D soft tissue models. Cephalometric landmarks were identified to perform a geometrical similarity analysis among patients with the same malocclusion class undergoing the same surgical procedure. Vectorial displacement maps were generated to capture the soft tissue changes from pre- to post-operative and were then applied to the pre-operative of test patients to predict soft tissue outcomes. Euclidean distances were calculated between predicted and real post-operative positions, and the Wilcoxon signed-rank test was conducted to assess statistical differences between predicted and real landmark coordinates. RESULTS:Error maps indicated that approximately 70 % of predicted facial points had errors below 2.5 mm, while around 10 % ranged between 2.5 mm and 3 mm. Statistically significant differences (p < 0.05) were observed only for the gonion and cheilion. CONCLUSION:. The findings support the validity of the geometrical similarity analysis and the vectorial displacement map approach. The simplicity and promising accuracy of the proposed method encourage further investigations across different surgical procedures. Additionally, integrating this methodology into surgical planning could offer a viable alternative to commercial solutions. This low-cost, computationally efficient prediction method is designed to improve as more patient data become available. The proposed method is patent pending.

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 .

Stress is a reaction that occurs when a person perceives, with or without awareness, an imbalance between requests and available resources. Relying on this definition, we have carried out an experiment in a Virtual Reality environment to elicit (light) stress in the user and analyze the emotional responses with electroencephalography (EEG). The virtual environment is divided in eight parts; in each of them a stressor has been put in action, meaning that in every part the participants perform a task, but a specific resource is missing (time, knowledge, control, salvation, no or too many alternatives, engagement, self-confidence). EEG is used to assess the emotional response with the aid of Valence/Arousal/Dominance/Stress indicators presented in previous literature. Nine indicators calculated for 87 participants, labeled according to self-assessment replies (post-experimental questionnaires), were classified with eXtreme Gradient Boosting, k-Nearest Neighbor, Support Vector Machine and Random Forest classifiers. The lowest results in terms of accuracy were obtained with kNearest Neighbor (around 70 %), whilst the highest ones were obtained with eXtreme Gradient Boosting and Random Forest (above 98 %), showing that EEG could be a valuable tool to assess the emotional response in stressful situations, with a particular focus on the Stress indicators.

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. These findings highlight the critical role of CAD/CAM and XR technologies in modern maxillofacial surgery, offering improved surgical precision and optimizing patient outcomes.

The use of CAD and 3D printing of surgical guides (SGs) for osteotomies is a widely developed practice in orthopaedic surgery, and particularly in maxillo-facial interventions, but validation studies rarely occur in literature. The present study defines a methodology to validate SGs dimensionally and mechanically through geometrical analysis, tensile testing, contact simulations, and abrasion testing. Distortions between the 3D printed SGs and the CAD model are quantified and an average deviation error for each production process step is obtained. Mechanical analysis identifies a way of applying the load on the SG to measure their equivalent linear stiffness (N/mm), maximum displacement (mm) and corresponding tolerable load (N) by varying some dimensional parameters. The stress state was assessed by finite element method (FEM) analysis, then the numerical results were compared with experimental ones using tensile tests: stiffness, maximum displacement and the corresponding loads were evaluated. The distribution of contact pressure on soft tissues was obtained numerically by FEM analysis. Finally, an ad hoc machine has been specially built to engrave discoidal specimens with typical operating room conditions. The methodology has been validated using 11 SG fibular and mandibular specimens and reporting the obtained results of each procedure step.

The central role that the human operator assumed in industrial design and development processes, driven by Industry 5.0 principles, has paved the way for the creation of new human-centric system architectures. This paper discusses the practical implications regarding the deployment of a Human Digital Twin-based system in the manufacturing environment. It provides a proof-of-concept of the architecture for the implementation of the twinning process, starting from the investigation of the most suitable hardware selection based on the desired outcomes to build up the human monitoring phase. We analyze three management decision-making levels to determine the scalability of the proposed architecture for strategical, technical, and operational managerial strategies. This research aims to propose some technological selection criteria, based on the main characteristics of the available technological acquisition devices, to determine the most suitable sensors for the creation of the physical twinning monitoring process. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)