Search Results (499)

Background: Advances in screening programs have led to increased detection of early-stage non-small cell lung cancer (NSCLC), including synchronous or metachronous nodules amenable to surgical resection. Patients requiring contralateral anatomical lung resections present a unique surgical challenge due to potential impairments in lung function and the complexities of one-lung ventilation. This study evaluates the feasibility, safety, and perioperative outcomes of robotic-assisted thoracic surgery (RATS) for contralateral anatomical lung resections in patients with NSCLC. Methods: A retrospective analysis was conducted on 20 patients who underwent RATS contralateral anatomical resection between January 2019 and June 2024. Preoperative pulmonary function, perioperative characteristics, and oncological outcomes were assessed. Operative parameters, including conversion rates, intraoperative oxygenation, need for extracorporeal membrane oxygenation (ECMO), and postoperative complications, were recorded. Results: Seventy percent of the patients underwent surgery for metachronous tumors. The median forced expiratory volume in 1 s (FEV1) was 75.94% (66.62–89.24). The most common resection was segmentectomy (65.0%). The median operative time was 148.0 min (108.0–194.75). There were no conversions to open surgery or ECMO requirements. Intraoperative parameters remained stable (median FiO₂: 0.8; lowest SaO₂: 92.0%). Complications occurred in 25% of the patients, mostly Clavien–Dindo grade 2. No in-hospital, 30-day, or 90-day mortality was observed. Conclusions: Robotic-assisted contralateral anatomical lung resection is a feasible and safe approach for patients with previous contralateral surgery, supporting its role as a minimally invasive alternative for complex surgical cases. Full article

Background/objectives: Robotic-assisted partial nephrectomy (RAPN) is increasingly utilised for the management of renal masses, with the growing use of different robotic platforms and increasing complexity of renal masses managed robotically. Appropriate patient selection, the development of operative skills and experience and sensible surgical decision making are required to optimise the outcomes of RAPN and minimise the risk of complications. We provide a comprehensive review of strategies to predict, avoid and manage the complications of RAPN. Methods: We conducted a comprehensive literature review to outline many of the reported complications arising from RAPN, with a focus on preoperative considerations (patient selection, imaging, 3D modelling and predictive models), intraoperative considerations (positioning and kidney exposure complications) and practical management strategies to identify and manage the complications of this procedure. Results: Many complications of RAPN can be predicted, and we outline strategies to mitigate these risks through careful preparation prior to surgery, including descriptions of preventative strategies and important preoperative considerations. We also present a detailed outline of management for the most common complications of RAPN, including bleeding/haemorrhage, urine leak and intraoperative complications such as adjacent organ injuries. Conclusions: RAPN can be a challenging procedure with a significant risk of complications. Assiduous preoperative planning, thoughtful intraoperative decision making and the early recognition and management of complications are essential to optimise patient outcomes following RAPN. Full article

Objectives: Robotic-assisted segmentectomy (RAS) has proven to be safe and feasible for early-stage lung cancer; nonetheless, its oncologic efficacy and long-term outcomes are still debated. We aimed to explore whether RAS could be an alternative to robotic-assisted lobectomy (RAL) in early-stage NSCLC, focusing on long-term outcomes such as 10-year cancer-specific survival (CSS), cumulative rate of relapse (RR), and local recurrence (LR). Methods: Patients undergoing RAS for early-stage NSCLC (clinical stage I) were analyzed from August 2007 to August 2023. A 1:3 propensity score-matched analysis was performed among patients undergoing RAL, based on demographic characteristics and pathological stage. Primary endpoints were CSS, RR, and LR. Results: A total of 40 patients undergoing RAS were retrospectively enrolled. After matching 120 patients undergoing RAL, no significant differences were found in postoperative complications, median operative time, or length of hospital stay. Patients undergoing RAS had comparable 10-year CSS (p = 0.90) and RR (p = 0.99) to those undergoing RAL, whereas 10-year of cumulative incidence of local recurrence (LR) was 11.0% (95% CI: 3.4–23.7%) for RAS patients and 2.8% (95% CI: 0.5–8.9%) for RAL patients (p = 0.08). Additionally, RAL provided a significantly higher number of N1 and N2 lymph node retrievals (p < 0.0001 and 0.06, respectively), as well as a higher number of N2 stations (p = 0.0001). Conclusions: Based on our experiences, even though RAS can ensure excellent long-term outcomes in selected cases of early-stage NSCLC, comparable to RAL, the local recurrence rate was higher in the RAS group. Full article

Mastitis is a significant challenge in the buffalo industry, affecting both milk production and animal health and resulting in economic losses. This study presents the first fully automated AI-driven thermal imaging system integrated with robotic milking, specifically developed for the real-time, non-invasive monitoring of udder health in Italian Mediterranean buffalo. Unlike traditional approaches, the system leverages the synchronized acquisition of thermal images during milking and compensates for environmental variables through a calibrated weather station. A transformer-based neural network (SegFormer) segments the udder area, enabling the extraction of maximum udder skin surface temperature (USST), which is significantly correlated with somatic cell count (SCC). Initial trials demonstrate the feasibility of this approach in operational farm environments, paving the way for scalable, precision diagnostics of subclinical mastitis. This work represents a critical step toward intelligent, automated systems for early detection and intervention, improving animal welfare and reducing antibiotic use. Full article

Industrial Vision Inspection Systems (VISs) often struggle to adapt to increasing variability of modern manufacturing due to the inherent rigidity of their hardware architectures. Although the Reconfigurable Manufacturing System (RMS) paradigm was introduced in the early 2000s to overcome these limitations, designing such reconfigurable machines remains a complex, expert-dependent, and time-consuming task. This is primarily due to the lack of structured methodologies and the reliance on trial-and-error processes. In this context, this study proposes a novel theoretical framework to facilitate the design of fully reconfigurable handling systems for VISs, with a particular focus on fixture design. The framework is grounded in Model-Based Definition (MBD), embedding semantic information directly into the 3D CAD models of the inspected product. As an additional contribution, a general hardware architecture for the inspection of axisymmetric components is presented. This architecture integrates an anthropomorphic robotic arm, Numerically Controlled (NC) modules, and adaptable software and hardware components to enable automated, software-driven reconfiguration. The proposed framework and architecture were applied in an industrial case study conducted in collaboration with a leading automotive half-shaft manufacturer. The resulting system, implemented across seven automated cells, successfully inspected over 200 part types from 12 part families and detected more than 60 defect types, with a cycle below 30 s per part. Full article

The optimization of robotic cells is a key challenge in the manufacturing industry due to the need to maximize efficiency in limited spaces and minimize operation times. Traditional cell design methods often face challenges due to the high complexity and dynamic nature of real-world applications. In response, this study presents a dual approach to optimize both spatial design and traversal time in robotic cells, using bioinspired evolutionary algorithms. Initially, a genetic algorithm is employed to optimize the layout of the cell elements, reducing space usage and avoiding interferences between workstations. Subsequently, an ant colony optimization algorithm is used to optimize the robots’ trajectories, minimizing cycle time. Through simulations and a digital model of the cell, key metrics such as total space reduction, operational time improvement, and productivity increase are evaluated. The results demonstrate that the combination of both approaches achieves significant improvements, enabling an average reduction of 21.19% in the occupied area and up to 20.15% in operational cycle time, consistently outperforming traditional methods. This approach has the potential to be applied in various industrial configurations, representing a relevant contribution in the integration of artificial intelligence techniques for the enhancement of robotic systems. Full article

Bird-dropping fouling and hotspot anomalies remain the most prevalent and detrimental defects in utility-scale photovoltaic (PV) plants; their co-occurrence on a single module markedly curbs energy yield and accelerates irreversible cell degradation. However, markedly disparate visual–thermal signatures of the two phenomena impede high-fidelity concurrent detection in existing robotic inspection systems, while stringent onboard compute budgets also preclude the adoption of bulky detectors. To resolve this accuracy–efficiency trade-off for dual-defect detection, we present YOLOv8-SG, a lightweight yet powerful framework engineered for mobile PV inspectors. First, a rigorously curated multi-modal dataset—RGB for stains and long-wave infrared for hotspots—is assembled to enforce robust cross-domain representation learning. Second, the HSV color space is leveraged to disentangle chromatic and luminance cues, thereby stabilizing appearance variations across sensors. Third, a single-head self-attention (SHSA) block is embedded in the backbone to harvest long-range dependencies at negligible parameter cost, while a global context (GC) module is grafted onto the detection head to amplify fine-grained semantic cues. Finally, an auxiliary bounding box refinement term is appended to the loss to hasten convergence and tighten localization. Extensive field experiments demonstrate that YOLOv8-SG attains 86.8% mAP@0.5, surpassing the vanilla YOLOv8 by 2.7 pp while trimming 12.6% of parameters (18.8 MB). Grad-CAM saliency maps corroborate that the model’s attention consistently coincides with defect regions, underscoring its interpretability. The proposed method, therefore, furnishes PV operators with a practical low-latency solution for concurrent bird-dropping and hotspot surveillance. Full article

Human-Robot Collaboration (HRC) is pivotal for flexible, worker-centric manufacturing in Industry 5.0, yet dynamic task allocation remains difficult because operator states—fatigue and skill—fluctuate abruptly. I address this gap with a hybrid framework that couples real-time perception and double-estimating reinforcement learning. A Convolutional Neural Network (CNN) classifies nine fatigue–skill combinations from synthetic physiological cues (heart-rate, blink rate, posture, wrist acceleration); its outputs feed a Double Deep Q-Network (DDQN) whose state vector also includes task-queue and robot-status features. The DDQN optimises a multi-objective reward balancing throughput, workload and safety and executes at 10 Hz within a closed-loop pipeline implemented in MATLAB R2025a and RoboDK v5.9. Benchmarking on a 1000-episode HRC dataset (2500 allocations·episode⁻¹) shows the hybrid CNN+DDQN controller raises throughput to 60.48 ± 0.08 tasks·min⁻¹ (+21% vs. rule-based, +12% vs. SARSA, +8% vs. Dueling DQN, +5% vs. PPO), trims operator fatigue by 7% and sustains 99.9% collision-free operation (one-way ANOVA, p < 0.05; post-hoc power 1 − β = 0.87). Visual analyses confirm responsive task reallocation as fatigue rises or skill varies. The approach outperforms strong baselines (PPO, A3C, Dueling DQN) by mitigating Q-value over-estimation through double learning, providing robust policies under stochastic human states and offering a reproducible blueprint for multi-robot, Industry 5.0 factories. Future work will validate the controller on a physical Doosan H2017 cell and incorporate fairness constraints to avoid workload bias across multiple operators. Full article

Digital Twins (DTs) are transforming manufacturing by bridging the physical and digital worlds, enabling real-time insights, predictive analytics, and enhanced decision making. In Industry 4.0, DTs facilitate automation and data integration, while Industry 5.0 emphasizes human-centric, resilient, and sustainable production. However, implementing DTs in robotic metal additive manufacturing (AM) remains challenging because of the complexity of the wire-based laser metal deposition (LMD) process, the need for real-time monitoring, and the demand for advanced defect detection to ensure high-quality prints. This work proposes a structured DT architecture for a robotic wire-based LMD cell, following a standard framework. Three DT implementations were developed. First, a real-time 3D simulation in RoboDK, integrated with a 2D Node-RED dashboard, enabled motion validation and live process monitoring via MQTT (message queuing telemetry transport) telemetry, minimizing toolpath errors and collisions. Second, an Industrial IoT-based system using KUKA iiQoT (Industrial Internet of Things Quality of Things) facilitated predictive maintenance by analyzing motor loads, joint temperatures, and energy consumption, allowing early anomaly detection and reducing unplanned downtime. Third, the Meltio dashboard provided real-time insights into the laser temperature, wire tension, and deposition accuracy, ensuring adaptive control based on live telemetry. Additionally, a prescriptive analytics layer leveraging historical data in FireStore was integrated to optimize the process performance, enabling data-driven decision making. Full article

The rapid advancement of genomic technologies has significantly transformed biomedical research and clinical applications, particularly in oncology. Identifying patient-specific genetic mutations has become a crucial tool for early cancer detection and personalized treatment strategies. Detecting tumors at the earliest possible stage provides critical insights beyond traditional tissue analysis. This paper presents a novel cyber-physical system that combines high-resolution tissue scanning, laser microdissection, next-generation sequencing, and genomic analysis to offer a comprehensive solution for early cancer detection. We describe the methodologies for scanning tissue samples, image processing of the morphology of single cells, quantifying morphometric parameters, and generating and analyzing real-time genomic metadata. Additionally, the intelligent system integrates data from open-access genomic databases for gene-specific molecular pathways and drug targets. The developed platform also includes powerful visualization tools, such as colon-specific gene filtering and heatmap generation, to provide detailed insights into genomic heterogeneity and tumor foci. The integration and visualization of multimodal single-cell genomic metadata alongside tissue morphology and morphometry offer a promising approach to precision oncology. Full article

Collaborative robots are vital in Industry 5.0 operations. They are utilized to perform tasks in collaboration with humans or other robots to increase overall production efficiency and execute complex tasks. Aiming at a comprehensive approach to assembly processes and highlighting new applications of collaborative robots, this paper presents the development of a digital twin (DT) for the design, monitoring, optimization and simulation of robots’ deployment in assembly cells. The DT integrates information from both the physical and virtual worlds to design the trajectory of collaborative robots. The physical information about the industrial environment is replicated within the DT in a computationally efficient way that aligns with the requirements of the path planning algorithm and the DT’s objectives. An enhanced artificial fish swarm algorithm (AFSA) is utilized for the 4D path planning optimization, taking into account dynamic and static obstacles. Finally, the proposed framework is utilized for the examination of a case in which four industrial robotic arms are collaborating for the assembly of an industrial component. Full article

Sensorineural hearing loss occurs when cochlear hair cells fail to convert mechanical sound waves into electrical signals transmitted via the auditory nerve. Cochlear implants (CIs) restore hearing by directly stimulating the auditory nerve with electrical impulses, often while preserving residual hearing. Over the past two decades, robotic-assisted techniques in otologic surgery have gained prominence for improving precision and safety. Robotic systems support critical procedures such as mastoidectomy, cochleostomy drilling, and electrode array (EA) insertion. These technologies aim to minimize trauma and enhance hearing preservation. Despite the outpatient nature of most CI surgeries, surgeons still face challenges, including anatomical complexity, imaging demands, and rising costs. Robotic systems help address these issues by streamlining workflows, reducing variability, and improving electrode placement accuracy. This review evaluates robotic systems developed for cochlear implantation, focusing on their design, surgical integration, and clinical outcomes. This review concludes that robotic systems offer low insertion speed, which leads to reduced insertion forces and lower intracochlear pressure. However, their impact on trauma, long-term hearing preservation, and speech outcome remains uncertain. Further research is needed to assess clinical durability, cost-effectiveness, and patient-reported outcomes. Full article

Background and Objectives: Systemic conversion of stage III–IV non-small-cell lung cancer (NSCLC) to a surgically resectable state with immune checkpoint inhibitors (ICIs) or tyrosine kinase inhibitors (TKIs) creates an emerging cohort of candidates for “salvage” pulmonary resection. No comprehensive evidence synthesis has yet evaluated the feasibility, safety, or oncologic value of this strategy. We aimed to systematically review peri-operative and survival outcomes of salvage lung resection following ICI or TKI therapy. Methods: MEDLINE, Embase, and PubMed were searched (inception–1 May 2025). Studies reporting ≥5 adult NSCLC patients who underwent anatomical lung resection after at least one cycle of ICI or TKI were eligible. Two reviewers screened records, extracted predefined variables, and assessed risk of bias with the Newcastle–Ottawa Scale. Pooled proportions were calculated with a random-effects model. Results: Fourteen observational series (n = 312 patients) met inclusion. Median age was 62 years (range 38–81); 58% were male. Lobectomy (63%) and segmentectomy (21%) were most frequent. Video-assisted/robotic approaches were achieved in 48%. The pooled R0 rate was 93% (95% CI 89–97%); pathologic complete response occurred in 27% (95% CI 19–36%). Major complications (Clavien–Dindo ≥ III) were 11% (95% CI 7–16%), and 30-day mortality was 1.3% (95% CI 0–3%). One-year disease-free and overall survival were 68% and 88%, respectively. Conclusions: Current evidence—albeit heterogeneous—indicates that salvage pulmonary resection after modern systemic conversion therapy is technically feasible, associated with acceptably low morbidity, and yields encouraging short-term oncologic outcomes. Prospective, registry-based studies are needed to define selection criteria and long-term benefit. Full article

Introduction: Available guidelines recommend performing nephron-sparing surgery in selected renal cell carcinoma (RCC) patients. Many studies provided robot-assisted partial nephrectomy (RAPN) functional and oncological outcomes, with most of these including a wide timespan and a number of surgeons with different experiences, which might lead to the heterogeneity of the results. In this study, we aim to provide a contemporary report of RAPN patient outcomes performed at two referral centers by experienced surgeons. Materials and Methods: Overall, 333 RAPN patients treated at two European referral centers between 2019 and 2021 were identified. Continuous and categorical variables were reported using medians and proportions. Multi-variable logistic regression (MLR) models were fitted to test predictors of off-clamp technique use and trifecta achievement. Results: The median age was 65 (IQR: 57–73) years. The clinical stage distribution was as follows: 224 (67%) cT1a vs. 89 (26%) cT1b vs. 20 cT2 (7%). The median warm ischemia time was 14 (10–18) minutes, with trifecta being achieved in 74% (n = 240) of patients. In MLR models predicting off-clamp surgery, an increasing R.E.N.A.L. score was independently associated with a lower chance of attempting such a technique (OR: 0.69, p-value < 0.001). In models predicting trifecta achievement, both a higher R.E.N.A.L. score (OR: 0.78, p-value = 0.007) and the presence of multiple lesions (OR: 0.29, p-value = 0.007) were independently associated with lower chances of reaching the outcome. Significant upstaging of chronic kidney disease (CKD) stage was recorded in 9.4% of patients after one year of follow-up. Conclusions: We reported the contemporary outcomes of patients treated with RAPN by highly experienced surgeons from two referral centers. This report represents a valid benchmark that could be used for individual patient counseling in the decision-making process. Full article

The integration of robots into collaborative environments, where they physically interact with humans, requires systems capable of ensuring both safety and performance. This work introduces the development of a Variable Stiffness Impact Testing Device (VSITD), designed to emulate physical human–robot interaction by replicating biomechanical properties such as muscle elasticity and joint compliance. The proposed system integrates a Variable Stiffness Mechanism (VSM) with a multi-sensor configuration that includes a high-resolution Force Sensitive Resistors (FSR) matrix, piezoelectric load cells, and an FPGA-based acquisition unit. The FPGA enables fast acquisition of contact forces and pressures, while the mechanical stiffness configuration of the VSM can be rapidly reconfigured to simulate a wide range of impact scenarios. The device aims to validate compliance with the standard ISO/TS 15066 safety standard of robotic work cell in the context of collaborative application. The modularity and flexibility of the VSITD make it suitable for evaluating a wide range of collaborative robotic platforms, providing a reliable tool for pre-deployment validation in shared workspaces. By combining real-time sensing with adaptable stiffness control, the VSITD establishes a new benchmark for safety testing in human–robot collaboration scenarios. Full article