• Developed new DT Strategy Schedule & Document to provide pragmatic development guidance • Developed a unified DT architecture to integrate sustainability assessments • Demonstrated through a case study of a university building • Created an immersive 3D visualization to enable actionable decision support This paper investigates the application of Digital Twin (DT) technology to support sustainability assessments in the built environment. While DTs are increasingly adopted in building contexts, three key fundamental challenges persist: (1) lack of pragmatic guidance for DT development, (2) limited integration of multiple sustainability assessments, and (3) insufficient support for decision-making through contextualized visualization. To address the scientific gaps, this study introduced the Digital Twin Strategy Schedule and Digital Twin Strategy Document, which provide guidance for defining DT objectives, analytical scope, and data requirements. These instruments were derived by interpreting and adapting general DT strategy guidelines to the specific needs of sustainability-oriented DTs for buildings and are iteratively refined through application to a real-world case study. The proposed framework integrated energy modeling, Material Flow Analysis, and Life Cycle Assessment within a unified architecture. An automated workflow was developed to link Building Information Modeling, the analytical models, and Building Automation System data, enabling consistent data exchange, validation, and traceability. The proposed approach was demonstrated through a case study of a university building equipped with smart sensors. Sustainability indicators and operational performance metrics were visualized within an immersive, interactive 3D environment, supporting anomaly detection and alert-based communication. Results highlighted the potential of DTs to enhance sustainability-informed decision-making and challenges associated with data completeness, semantic alignment, and geometric interoperability. Overall, this work formalizes and demonstrates a DT architecture to connect sustainability analytics with spatially contextualized visualization, moving beyond static dashboards toward actionable decision support for building operation and maintenance.

Wind turbines are increasingly being installed in earthquake active regions, where seismic loads can substantially influence their structural integrity and operational reliability. This study presented a novel wind-structure-soil interactive (WSSI) analysis framework for dynamic responses of monopile-supported wind turbines (MWTs), integrating multibody dynamics (MBD) and finite element methods (FEM). The rotor system is modeled through MBD based on fundamental dynamic principles, while wind loads are accurately determined using blade element momentum (BEM) theory. The tower is modeled via FEM, considering both geometric and material nonlinearities to realistically capture its response under seismic excitation. Furthermore, a bounding surface p-y model is incorporated to consider the soil-structure interaction. A dedicated rotor-nacelle interface program is developed to facilitate real-time data exchange between the MBD and FEM subsystems, ensuring fully coupled dynamic interaction. The proposed framework is validated against experimental data from seismic response tests of operating MWTs, demonstrating strong agreement with measurements. Overall, this study provides a systematic and efficient approach for investigating the seismic response of wind turbines, offering valuable insights for their seismic design and safety assessment. • Novel integration of multibody dynamics and finite element methods for modelling the seismic responses of MWTs. • A multibody dynamics model of the rotor was proposed to consider the wind-structure coupling effects. • A finite element model of the supporting structure was established to consider the nonlinearity of the structure and soil. • The integrated method was validated against the seismic test results of MWTs under operational conditions.

Wind power plants (WPPs) rely significantly on extensive communication networks and data exchange for their operation and control. Thus, various cybersecurity issues can be created by their rapid integration into modern power grids. On this basis, this paper introduces novel denial-of-service (DoS), false data injection (FDI), and hybrid cyberattack models targeting rotor speed sensors of doubly-fed induction generator (DFIG)-based WPPs. The attacks are designed so that they excite lightly-damped oscillatory modes of the connected power grid while originating from a set of sensors in the WPP's turbines. Then, to counter the developed attacks, a graph deviation network (GDN) integrated with a physics-informed neural network (PINN) is developed for real-time cyberattack detection in a realistic noisy environment while maintaining compatibility with IEC-61400-25. Finally, a well-tailored robust <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula>-based controller is designed to mitigate the impact of the sophisticated attacks and stabilize the power grid. The impact of the cyberattacks and the effectiveness of the proposed detection and mitigation framework are demonstrated on a modified New England 39-bus system, including practical deployment considerations and robustness under extended attack scenarios.

The increasing prevalence of roadworks poses significant challenges to maintaining accurate and up-to-date high-definition (HD) maps, crucial for autonomous vehicle (AV) safety and efficiency. Current methods for updating HD maps are expensive, time-consuming, and not responsive to real-time changes. This paper proposes a real-time, low-cost pipeline for updating HD maps with roadworks information using a monocular camera and a Contrastive Language–Image Pre-Training-based Vision Language Model (VLM), achieving robust few-shot sign recognition with minimal annotated data. The workflow is designed for low-cost, real-time deployment using only monocular camera input, and supports rapid, incremental HD map updates directly in OpenDRIVE format. Extensive experiments demonstrate that our system outperforms conventional baselines (e.g., fine-tuned You Only Look Once (YOLO) v11) not only in data-scarce settings but also across challenging environmental conditions. The recognition model is trained on a diverse dataset of 3752 real and virtual images, enhanced through data augmentation techniques. The model achieves a 97.12% recognition rate on a test dataset of 752 images and a root mean square error (RMSE) of less than 1.2 m for positional accuracy, processing single image inputs in 1.54 s. By leveraging the OpenDRIVE format, this approach ensures seamless data exchange between different HD map systems, facilitating real-time updates that accurately reflect current road conditions. The methodology demonstrates significant benefits in terms of responsiveness, cost and time efficiency, enhanced safety, and flexibility. Trials on the United Kingdom motorways validate the pipeline's effectiveness, offering a robust solution to dynamic road conditions and enabling safer, more efficient AV navigation.

Lung diseases like COVID-19 and Pneumonia represent a significant global health challenge which need the accurate and timely diagnostic system. Even traditional machine learning and deep learning methods provides a solution using medical images, it depends on consolidating large amounts of data into a centralized location. The centralized data collection process deals different problems such as privacy concern, data cracks and unauthorized access of data due to the medical data are more sensitive. In this proposed work, these problems are addressed by integrating federated learning framework to provide a privacy-preserving distributed learning environment that allows the model to train without sharing medical data. This proposed work introduces a federated learning framework utilizing existing deep learning architectures such as InceptionV3, ResNet50, and DenseNet121. To simulate the distributed environment, the dataset is distributed across three clients and every model is trained in each client. After local models are trained independently, the weights of global model are updated using FedAvg algorithm. Finally, the performance of the three proposed models is evaluated with various metrics such as accuracy, precision, recall, and F1-score. Experimental results shows that DenseNet121 achieves highest performance with the highest classification accuracy as 90%, owing to its dense connectivity and efficient feature reuse capability.

Genomic data can advance precision medicine; however, to continue developing more targeted treatments, genomic datasets need to be integrated with health care data and become more disease-focused. This integration, in turn, amplifies existing challenges in health care data management, such as handling large data volumes, adhering to data standards, and protecting sensitive information. Addressing these challenges calls for unified digital ecosystems that combine data collection, standardization, analysis, and governance within a single platform, thereby reducing the technical burden for users. Currently, a clear set of indications about functional and nonfunctional requirements to help designers translate stakeholder needs into actionable design specifications is missing. This scoping review aimed to identify the functional and nonfunctional requirements most frequently discussed in the literature from the perspective of end users (eg, clinicians and data analysts) to inform the design of a health and genomic data management platform that supports data sharing and analysis in clinical settings by conducting a PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Review) review. We searched for peer-reviewed English studies that focused on platforms for managing genomic data from a user-centered perspective. We considered studies from 2014 to 2024 that were extracted from Scopus, PubMed, Web of Science, and Google Scholar for the scoping review. Insights were extrapolated for a thematic analysis to develop an initial set of requirements. We charted the functional and nonfunctional requirements according to their frequency of occurrence in the literature to provide a structured overview of the most commonly reported requirements. From 410 initial items, 210 items were preliminarily selected, and 53 items were included in the final analysis. Three primary groups of 26 interface functional requirements emerged: (1) general data management (acquisition, standardization, and sharing), (2) data processing and analysis (preprocessing and analysis pipelines), and (3) data visualization and reporting. Twenty nonfunctional requirements were identified and organized in 4 groups: (1) communication and support, (2) platform technical infrastructure, (3) user experience and user interface characteristics, and (4) security and compliance. We also investigated the issues that need to be resolved to develop an ideal platform. We identified and mapped the most frequently reported functional and nonfunctional requirements of clinical and data professionals when discussing a health and genomic data management platform. The 3 key functional requirements should be supported by nonfunctional requirements such as secure technical infrastructure and governance mechanisms that enable compliant data processing and sharing. Designers may use these insights and mapping to develop standardized data platforms that promote efficient data exchange between institutions and experts while ensuring regulatory compliance and secure access, as proposed by the European Health Data Space.

The problem of quantifying information security risks for a specific class of systems ‒ geographically distributed information systems of law enforcement agencies is considered. The key methodological difficulty in applying traditional approaches in this subject area is associated with a systemic lack of reliable incident statistics, caused by both the confidential nature of operations and the uniqueness of many threats. As a solution, a mathematical model based on the apparatus of fuzzy set theory and fuzzy logic inference, adapted to operate under conditions of high uncertainty, is proposed. The model operates with qualitative expert judgments, formalized through the linguistic variables “Asset value”, “Threat probability”, and “Vulnerability degree”. The inference mechanism is implemented based on a complete knowledge base of twenty-seven production rules and the Mamdani algorithm, the output of which is a quantitative assessment of the integral “Risk level”. Model verification was conducted through a computational experiment simulating three characteristic system operation scenarios: mobile access through potentially hostile networks, secure data exchange via dedicated channels, and an internal insider threat. The experiment results demonstrate the model's adequate and logically consistent response, correctly identifying critical and acceptable states. Visualization in the form of a response surface confirms the nonlinear nature of the dependence of the resulting risk on the input parameters. The practical significance of the research, lies in the possibility of integrating the developed model into decision support systems for well-founded planning of protective measures and optimal resource allocation under conditions of incomplete initial data.

Over the past two decades, Citizen Science (CS) has experienced rapid growth, driven by technological advancements and the rise of digital platforms. This work examines the necessity for standardization in Citizen Science data management and discusses how existing data standards can enhance the impact of citizen-generated data. CS standardization ensures data quality, comparability, reusability, and interoperability, making data suitable for contributing to the Marine Strategy Framework Directive (MSFD) and the United Nations Sustainable Development Goals (SDGs). This paper examined 130 Citizen Science publications and found that most collected data referred to the MSFD Descriptor 1 (Biodiversity—44.96%) and Descriptor 10 (Marine Litter—20.93%), followed by the alien species distribution (D2—11.63%), hydrography (D7—6.20%), eutrophication (D5—6.20%), and marine pollution (D8—3.10%). Analysis of 108 publications on SDG alignment revealed that the majority (35.58%) focused on reducing marine pollution. This paper reviews the best practices for effective Citizen Science data management, including standards for data structures, content, values, and exchange. Based on this review, Darwin Core, Ecological Metadata Language (EML), and the OGC SensorThings API appear to be the most suitable standards for MSFD-relevant CS data. Therefore, policymakers could enable the formal integration of standardized CS datasets into MSFD monitoring workflows.

This study presents the design and implementation of a standalone mobile surveillance robot that enables real-time visual monitoring and remote navigation through wireless communication. The system is built around the ESP32-CAM Microcontroller Unit (MCU – Microcontroller Unit), which integrates image capture, processing, and communication within a compact embedded platform. The onboard camera captures visual data in Joint Photographic Experts Group (JPEG – Joint Photographic Experts Group) format at Video Graphics Array (VGA – Video Graphics Array) resolution, while efficient buffering is achieved using internal memory supported by Pseudo-Static Random Access Memory (PSRAM – Pseudo-Static Random Access Memory) to ensure continuous frame transmission. A persistent communication channel is established using the WebSocket protocol (WebSocket – FullDuplex Communication Protocol), enabling low-latency bidirectional exchange of control commands and video data. The robot’s movement is controlled using dual Direct Current motors (DC – Direct Current), interfaced through General Purpose Input Output pins (GPIO – General Purpose Input Output), with Pulse Width Modulation (PWM – Pulse Width Modulation) applied for precise speed regulation and smooth navigation. The software architecture follows an asynchronous event-driven model implemented using ESPAsyncWebServer and AsyncWebSocket libraries, allowing simultaneous execution of streaming and control operations without delay. The system operates via a Wireless Fidelity Access Point (Wi-Fi – Wireless Fidelity, AP – Access Point), enabling direct device connectivity without relying on external internet infrastructure. Additionally, a safety mechanism is incorporated to automatically stop the robot in case of connection loss, preventing unintended motion. Overall, the system provides a compact, efficient, and cost-effective solution for real-time surveillance and remote robotic control applications.

In a context of increasing efforts towards the establishment of a Regional Health Data Hub for the African Region, the 2024 West African Policy Dialogue brought together researchers and policymakers from seven West African countries in a two-day meeting in Aburi, Ghana. This report provides a high-level summary of the discussions at the meeting. The forum emphasized that the use of poor, incomplete, or inaccurate data will have negative consequences, regardless of the sophistication of the analytic tools used. New technologies have emerged that can support the generation and effective use of data. Yet, governments in West-Africa struggle to maximize the benefits of these technologies, including genomic surveillance, real-time data generation, and supranational data integration and exchange. Policies are needed that support and regulate new technologies and contribute to greater capabilities for better data.

The increasing adoption of distributed systems has created a strong demand for secure and efficient data exchange, exposing the limitations of traditional centralized architectures. Conventional file-sharing systems rely on a single server for authentication and storage, which leads to issues such as single-point failure, identity exposure, and limited scalability. These systems often lack robust identity verification across distributed nodes, making them vulnerable to impersonation, interception, and unauthorized access. Additionally, their dependence on static credential mechanisms and inefficient communication models results in higher latency and reduced system performance. Such limitations, including centralized identity management, increased processing overhead, and restricted collaboration between nodes, highlight the need for a decentralized and lightweight secure framework. To overcome these challenges, a system is developed using a Django-based web platform integrated with a Peer-to-Peer (P2P) architecture and Elliptic Curve Cryptography (ECC). The proposed system utilizes Elliptic Curve Diffie–Hellman (ECDH) as a key agreement mechanism to securely establish a shared secret between peers. When a requested file is not available locally, the system initiates secure ECC-based authentication with another peer and retrieves the file without relying on a central authority, ensuring confidentiality, integrity, and anonymity during communication. The Django framework effectively manages user interactions, database validation, file processing, and performance visualization, while the use of ECDH enables strong security with smaller key sizes, resulting in faster execution and improved overall efficiency.

Rescuing victims trapped in deep and narrow borewells is highly challenging due to confined space, lack of visibility, and the inability of humans to access such environments directly. To overcome these limitations, this work proposes a compact and remotely operated Borewell Rescue Robot (BRR) developed using an Espressif Systems Processor (ESP32-CAM) Microcontroller Unit (MCU), designed to provide real-time monitoring and precise control during rescue operations. The system integrates live video streaming with mechanical actuation through a wireless interface, allowing operators to guide the robot safely from a remote location. A 32-bit embedded processor supported by Pseudo-Static Random Access Memory (PSRAM) enables smooth Video Graphics Array (VGA) image transmission, while WebSocket-based communication ensures continuous and low-latency data exchange. The robotic mechanism utilizes dual high-torque servo motors for gripping and positioning, enabling accurate manipulation within confined vertical shafts. To address poor lighting conditions, a Pulse Width Modulation (PWM) controlled Light Emitting Diode (LED) system is incorporated for effective illumination. The software follows an asynchronous, event-driven architecture using the ESPAsyncWebServer framework, allowing simultaneous handling of video streaming and control commands without delays. Additionally, a fail-safe mechanism is implemented to reset actuators and disable critical components in case of communication loss, ensuring safety and reliability. The system operates through a Wireless Fidelity (Wi-Fi) Access Point (AP), eliminating dependency on external infrastructure and enabling deployment in remote locations, making it a scalable and efficient solution for emergency rescue scenarios.

Objectives Emergency Medical Services (EMS) are central to acute care, disaster response, and public health. Yet prehospital data in many systems remain disconnected from hospital and follow-up outcomes. This paper examines how fragmented, unidirectional data flows limit quality assurance, system learning, and crisis preparedness, using Switzerland as an illustrative case. Methods We analyze data flows across the rescue chain based on regulatory context, current handover practices, and international reference models. The analysis is supported by existing registry initiatives and a conceptual systems framework. Results Across EMS systems, information is generated in silos and transferred through brief handovers without systematic outcome feedback. Evaluation is therefore reduced to operational metrics such as response times, obscuring the clinical impact of prehospital care. In Switzerland, decentralized governance and the absence of national standards reinforce these dynamics. Existing registries demonstrate that outcome tracking is feasible using minimal standardized datasets. Conclusion Bidirectional EMS data exchange is essential to transform linear rescue chains into learning health systems. A national EMS minimum dataset with mandatory reporting and outcome feedback would enable transparency, quality improvement, and resilient emergency care.

Rescue operations in deep and narrow borewells are extremely challenging due to restricted access, poor visibility, and the inability of humans to directly reach victims in such confined vertical environments. To address these limitations, this work presents a compact and remotely operated Borewell Rescue Robot (BRR) developed using an Espressif Systems Processor (ESP32-CAM) Microcontroller Unit (MCU), designed to enable real-time monitoring and precise control during emergency situations. The system integrates live video streaming with mechanical actuation through a wireless interface, allowing operators to guide and control the robot safely from a remote location. A 32-bit embedded processor supported by Pseudo-Static Random Access Memory (PSRAM) ensures smooth and continuous Video Graphics Array (VGA) image transmission, providing clear visualization of the borewell interior. Communication is established using a Wireless Fidelity (Wi-Fi) Access Point (AP), while WebSocket-based protocols facilitate low-latency, bidirectional data exchange for synchronized control and monitoring. The robotic mechanism incorporates dual high-torque servo motors for accurate gripping and positioning, enabling effective manipulation within narrow shafts. To overcome low-light conditions, a Pulse Width Modulation (PWM)-controlled Light Emitting Diode (LED) system is integrated for enhanced illumination. The software architecture follows an asynchronous, event-driven model using the ESPAsyncWebServer framework, allowing concurrent handling of video streaming and control commands without delay. Additionally, a fail-safe mechanism is implemented to reset actuators and disable critical components in case of communication loss, ensuring system safety, reliability, and efficient deployment in real-world rescue scenarios.

According to epidemiological data, the global burden of cancer remains high. Hereditary cancer syndromes (HCS) account for up to 20 % of all cancer cases, and their early diagnosis represents a crucial opportunity for prevention and timely intervention. This article reviews current approaches to identifying and stratifying individuals at high genetic risk, considering population-specific patterns and clinical–family criteria. Risk stratification and personalized surveillance strategies — such as early MRI screening in BRCA1/2 carriers and the prioritization of non-ionizing imaging modalities in TP53 carriers — shift detection toward earlier stages and reduce the overall therapeutic burden. The paper discusses key components of surveillance programs, including cascade testing within families, the establishment of registries, and the development of structured patient care pathways. Practical steps are proposed for scalable implementation of oncogenetic services at institutional and regional levels, emphasizing centralized data interpretation, registry-based reporting, IT integration, reimbursement mechanisms for MRI-based modalities, and risk-adapted escalation or de-escalation of follow-up intensity.

Cloud computing has emerged as a fundamental technology for delivering scalable, flexible, and costefficient services, with REST (Representational State Transfer) APIs acting as the primary communication interface between clients and cloud platforms. Despite their advantages, REST APIs are increasingly targeted by cyber threats such as unauthorized access, data breaches, injection attacks, and denial-of-service attacks. These vulnerabilities are often caused by improper implementation of security mechanisms, lack of standardization, and the dynamic nature of cloud environments. This paper focuses on evaluating and checking the security properties of cloud service REST APIs to ensure secure data exchange and reliable system performance. The key security properties analyzed include authentication, authorization, confidentiality, integrity, and availability. The study reviews existing systems and identifies critical limitations such as weak authentication methods, insufficient monitoring, and lack of automated security testing. To overcome these challenges, a comprehensive security framework is proposed that integrates modern security techniques such as OAuth 2.0, JSON Web Tokens (JWT), HTTPS/TLS encryption, API gateways, and automated vulnerability assessment tools. Additionally, machine learning-based anomaly detection is incorporated to identify suspicious activities and potential threats in real time. This multi-layered approach enhances the overall security posture of REST APIs in cloud environments.

Artificial Intelligence (AI) systems based on traditional Deep Learning (DL) are expected to play a leading role in the early detection of various diseases in healthcare applications. However, there are two major drawbacks of these systems: protecting patient privacy and obtaining sufficiently large, high-quality datasets to train reliable models. In traditional DL, collecting data from different sources on a single central server increases system complexity and raises serious privacy and security concerns. Federated Learning (FL) makes it possible to train models locally at multiple data locations while collaboratively improving a global model without exposing raw data, making it a promising architectural solution for privacy preservation. Although previous studies have reported that FL can achieve performance comparable to centralized DL approaches, traditional FL approaches often struggle to maintain consistent performance across different settings. This limitation becomes more noticeable when heterogeneous data distributions, modalities, and domains are involved. In these situations, client drift, overfitting, and generalization capability of the global model arise as major challenges. Thus, this study presents FedCycle as an incremental improvement of the FedAvg algorithm. It modifies the aggregation frequency. It aims to overcome these drawbacks and make the global model more stable and efficient. The FedCycle eliminates centralized data collection, enhances data security, and effectively reduces client drift and overfitting by supporting model training across heterogeneous data distributions, modalities, and domains. The performance evaluation involves extensive experiments using various real-world breast cancer image datasets, namely BREAKHIS, ROBOFLOW, RSNA, BUSI, and BCFPP. The presented method is evaluated against both traditional DL and FL approaches using accuracy, precision, recall, F1-score, and AUC. The findings confirm that applying fine-tuning within FedCycle reduces overfitting during training. As a result, FedCycle achieves performance improvements of 7.75% and 4.65% in accuracy and F1-score on the RSNA and BCFPP datasets compared to traditional DL approaches, while also providing an average improvement of approximately 1.5% in accuracy and F1-score across BREAKHIS, ROBOFLOW, and BUSI datasets compared to FedAvg.

Agrivoltaic systems co-locate photovoltaic (PV) arrays and crops, offering land-use efficiency and potential microclimate benefits, yet they introduce new challenges for computer-vision-based crop monitoring. PV structures produce strong, spatially varying shadows, specular reflections, and periodic occlusions that confound visual cues for diagnosing crop diseases and abiotic stresses. Meanwhile, agrivoltaic deployments are often distributed across farms and operators, making centralized data collection impractical due to privacy, ownership, and regulatory concerns. This paper proposes PrivAgriVolt, a novel privacy-preserving learning framework for agrivoltaic crop issue recognition that explicitly models PV-induced illumination and enables collaborative training without sharing raw images. The core algorithm integrates (i) a PV-geometry-conditioned shadow normalization module that fuses estimated array layout and sun-angle priors into a shadow-aware appearance canonization network, reducing illumination-induced domain shift across times and sites; (ii) a federated contrastive stress learner that aligns stress semantics across farms via prototype-based contrastive objectives while remaining robust to heterogeneous sensors and crop stages; and (iii) an adaptive privacy layer that combines secure aggregation with budget-aware gradient perturbation and client-level clipping to provide formal privacy guarantees while preserving fine-grained diagnostic performance. Extensive experiments on real agricultural vision benchmarks and agrivoltaic shadow variants demonstrate that PrivAgriVolt improves stress recognition and segmentation under PV shading while maintaining strong privacy–utility trade-offs.

Advances in information technology have facilitated the digitalization of several industries, including trade, by enabling more efficient communication and data exchange. Previous research has shown that the expansion of e-commerce presents legal challenges, particularly in terms of the application of exemption clauses by commercial actors. Therefore, the expansion of digital commerce must be consistent with the principles set out in Law Number 7 of 2014 concerning Trade and Law Number 11 of 2008 concerning Electronic Information and Transactions (ITE), as amended by Law Number 19 of 2016 and Law Number 1 of 2024. Both regulations provide the legal basis that must be implemented to provide fairness and legal certainty in e-commerce transactions. This study takes a normative legal approach and incorporates qualitative legal analysis of relevant legal standards. The research presented provides a critical perspective on the need for appropriate and enforceable legislative restrictions to protect the interests of all parties involved in e-commerce.

Background Maxillofacial trauma requires timely recognition of urgent conditions, yet specialized expertise is often limited in rural settings. Hub-and-spoke trauma networks supported by telemedicine may optimize triage, reduce unnecessary transfers, and integrate advanced workflows. Evidence for maxillofacial trauma teleconsultation, however, remains sparse. Methods A retrospective study of all teleconsultations for maxillofacial trauma between January 2023 and August 2025 within the major trauma network of South-Eastern Vast Area of Tuscany (AV-TSE) (population ∼809,000) was conducted. Thirteen spokes hospitals from 13 peripheral Azienda Unità Sanitaria Locale Toscana Sud-Est (AUSL-TSE) are connected to the tertiary hub in Siena, for example, the Azienda ospedaliero-universitaria Senese (AOUS). Teleconsultations used a secure platform provided by Ente di supporto tecnico amministrativo regionale (ESTAR) enabling safe exchange of clinical data, photographs/videos, and radiological images. Patients were triaged as emergency (immediate transfer), urgency (hub evaluation within 72 h), or elective (spoke follow-up). Primary outcomes were: avoided transfers, efficiency, and equity of access. Multivariable logistic regression assessed predictors of avoided transfer and loss to follow-up (LTFU). Results A total of 670 patients were analyzed (mean age = 64.4 years; 43.9% female). Zygomaticomaxillary complex (29.1%), orbital (19.4%), and maxillary fractures (15.1%) predominated. Overall, 174 patients (26.0%) were managed locally, avoiding ∼4520 km and 75 h of travel. Conservative outpatient care was most frequent (57.6%), while 13.7% required surgery under general anesthesia. Older age independently predicted both avoided transfer (OR = 1.03/year, 95% CI = 1.02–1.04) and LTFU (OR = 1.023, 95% CI = 1.010–1.035). No duplicate CT scans were required. Virtual surgical planning by computer-aided design (CAD) and computer-aided manufacturing (CAM) enabled preoperative workflows to begin before transfer, reducing delays. Conclusions In the experience of AV-TSE, a pragmatic telemedicine teleconsultation pathway between AOUS and AUSL-TSE decentralized one-quarter of cases, reducing transfers while ensuring safety. Integration with CAD/CAM planning enhanced surgical readiness. Improving follow-up reliability, especially in older patients, remains a priority for future network optimization.