Search Results (3,058)

Assistive bathing for the elderly and disabled presents significant challenges regarding caregiver workload and safety. This paper presents the design and verification of a multi-module integrated intelligent bathing assistance system. The system automates the entire bathing sequence through four coordinated modules: a robotic scrubbing unit, a climate-controlled cabin, a passive multifunctional wheelchair, and a multi-degree-of-freedom transfer device. A key innovation is the wheelchair’s passive design with an automated docking mechanism, ensuring safety in wet environments. Unlike existing commercial solutions and the existing literature, which primarily focus on fragmented, singular functionalities (such as transfer-only devices or fixed-spray cabins), the core advantage of the developed system lies in its holistic integration of safe physical transfer, adaptive robotic scrubbing, and microenvironment control into a seamless, unified architecture. Employing a modular and ergonomic approach, the system executes a predefined 12-step automated workflow. Experimental validation demonstrates an average bathing time of 16.6 min and a quantifiable 69.8% reduction in caregiver workload, confirming the system’s high efficiency and practical utility in alleviating caregiver burden. Full article

Introduction: Post-prostatectomy incontinence (PPI) remains a major functional concern after robot-assisted radical prostatectomy (RARP). Posterior musculofascial reconstruction (PMFR) has been shown to facilitate early urinary continence (EUC), but variations in technique may further improve outcomes. We evaluated whether a modified vesicourethral anastomosis (VUA) incorporating simultaneous PMFR with a single barbed suture [pontine VUA (P-VUA)] may facilitate continence recovery compared with the standard Van Velthoven anastomosis (ST-VUA). Materials and Methods: This prospective study included patients undergoing RARP between January 2021 and December 2023. Allocation was based on surgeon preference. UC was defined as the use of no pads or one dry safety pad per day and was assessed at 10, 30, 90, 180, and 365 days after catheter removal. Multivariable logistic regression was performed to evaluate factors associated with 30-day continence. Time to continence was additionally analyzed using Kaplan–Meier methods. Results: This prospective comparative study included 157 patients undergoing robot-assisted radical prostatectomy (RARP) between January 2021 and December 2023 (76 ST-VUA, 81 P-VUA). Baseline and pathological characteristics were comparable between groups. Catheterization time was significantly shorter in the P-VUA group (5.0 ± 1.1 vs. 6.7 ± 1.4 days, p < 0.001). Continence rates were higher in the P-VUA group at 10 days (72.8% vs. 55.3%, p = 0.03), 30 days (84.0% vs. 68.4%, p = 0.035), 90 days (92.6% vs. 76.3%, p = 0.007), 180 days (93.8% vs. 82.9%, p = 0.044), and 365 days (97.5% vs. 86.8%, p = 0.015). Kaplan–Meier analysis demonstrated a shorter time to continence in Group P (log-rank p = 0.0037). In multivariable analysis, P-VUA was independently associated with higher odds of 30-day continence (OR 6.38, 95% CI 2.08–19.63, p = 0.001). Conclusions: The study suggests that the P-VUA technique was associated with faster recovery of urinary continence compared with ST-VUA in this prospective, non-randomized cohort. These findings support the hypothesis that integrating anatomical reconstruction principles into the anastomotic step may enhance functional outcomes after RARP. However, the results should be interpreted with caution, given the study design and sample size, and require confirmation in larger, preferably randomized studies. Full article

Background: Artificial intelligence (AI) is increasingly applied in pulmonary endoscopy, including diagnostic bronchoscopy, interventional pulmonology and endobronchial imaging. Advances in computer vision, machine learning and robotic systems have expanded the potential for automated lesion detection, navigation to peripheral pulmonary lesions, and real-time procedural support. However, the current evidence base remains heterogeneous, and translational challenges persist. Methods: This review summarizes current applications and developments of AI across white-light bronchoscopy (WLB), image-enhanced bronchoscopy (e.g., narrow-band imaging and autofluorescence imaging), endobronchial ultrasound (EBUS), virtual and robotic bronchoscopies, and workflow optimization and training. The authors also examine the methodological limitations, regulatory considerations, and implementation barriers that affect translation into routine practice. Results: Reported developments include deep learning-based models for mucosal abnormality detection, lymph-node characterization during EBUS-guided transbronchial needle aspiration (EBUS-TBNA), improved lesion localization, and reduction in operator-dependent variability. Additionally, AI-assisted simulation platforms and decision-support tools are reshaping training paradigms. Nevertheless, most studies remain retrospective or single-center, with limited external validation, dataset heterogeneity, unclear model explainability, and incomplete integration into clinical workflows. Conclusions: AI has the potential to support lesion detection, navigation, and training in pulmonary endoscopy. However, robust prospective validation, standardized datasets, transparent model reporting, robust data governance, multidisciplinary collaboration, and careful integration into clinical practice are required before widespread adoption. Full article

Compliant mechanisms have contributed to many advances in soft robotics, and there is strong motivation to translate these ideas to assistive devices where adaptive motion at the human interface is required. This work presents novel reconfigurable compliant joints (RCJs) as a parameterized joint element for functional biomimicry in lower-extremity joints for prosthetic knees and ankle–foot orthoses, with concepts that extend to other limb joints. The RCJ uses a rigid hub and outer ring joined by an array of flexible links with centerlines defined by cubic Bézier curves. Link shapes are organized into four Bézier classes (A–D), with base types using 10, 12, or 14 uniformly distributed link slots and variants generated by modifying active-link count and distribution, forming a structured morphology space of 12 configurations for machine design. Dual-extrusion 3D-printed prototypes are characterized by a custom testing apparatus using a 2.2 kN load cell at 25 mm/s over a 0–90° rotation range across six recorded load cycles to measure torque–angle curves and stiffness under large deformations. Angle-dependent stiffness is evaluated over three fixed intervals (0–30°, 30–60°, and 60–90°) to quantify multi-stage behavior. A 2-dimensional corotational frame model and a Simscape Multibody model, including a rolling-contact knee configuration, use the same parameterization to relate geometry, nonlinear mechanics, and system-level motion. Experiments and simulations show multi-stage torque–angle profiles and predictable stiffness modulation across all configurations, with both magnitude and transition angle tunable through Bézier class and active-link distribution, positioning the RCJ as a CAD/CAE-compatible joint architecture for assistive devices or wearable robotic systems and a basis for advancing functional biomimicry in compliant mechanism design. Full article

Background/Objectives: Adult spinal deformity (ASD) correction is a complex surgery to restore spinal alignment and relieve patients’ symptoms. Modern techniques and technologies allow for aggressive surgical correction in tissue-friendly ways that preserve anatomy and may enable faster recovery. Robotic-assisted posterior spinal stabilization may be used as an adjunct to complex ASD reconstruction to facilitate a minimally invasive approach, reduce perioperative morbidity and physiological insult, and allow for the performance of procedures traditionally reserved for large academic centers to be effectively performed by qualified surgeons in optimized patients at smaller hospitals with fewer resources. The objective of this study is to assess realignment, perioperative complications, and patient-reported outcomes of complex, minimally invasive, robotic-assisted adult spinal deformity correction in a surgical specialty hospital. Methods: Demographic, surgical, and perioperative data were collected from the medical record. The Oswestry Disability Index (ODI) and Numeric Rating Scale (NRS) for pain scores were collected preoperatively and at regular post-op visits. X-rays were captured preoperatively before hospital discharge and at follow-up visits. Results: Fifty consecutive deformity patients were corrected with a two-stage approach (anterior column reconstruction followed by posterior stabilization with robotic-assisted screw placement on the next day) at a 48-bed (eight operating rooms), surgeon-owned, subspecialty hospital. The average patient age was 70 years, and 64% were female. The average estimated blood loss (EBL) values for the first and second stages were 62 mL and 205 mL, respectively. The average operative time was 172 min during the first stage and 210 min for the second stage. Three interbody spacers (first stage) and 16 screws (second stage) were inserted on average in each procedure. The average length of stay (LOS) in the hospital was 5 days, and the average follow-up period was 10.6 months. No patients required a transfer to another facility with intensive care unit (ICU) capabilities, and none required a revision of hardware placement. There was an average reduction in the lumbar coronal scoliotic curve of 14.5° and an increase in lumbar lordosis of 14.8° at the latest follow-up (p < 0.01). The average mismatch between pelvic incidence and lumbar lordosis (PI-LL) preoperatively was 17.6°, which was reduced to 9.6° at the latest postoperative follow-up (p < 0.01). Mean ODI (%) and NRS scores were significantly improved by 33.8% (46.7 ± 13.3 to 30.9 ± 19.8; p < 0.01) and 55% (6.0 ± 2.2 to 2.7 ± 2.6; p < 0.01), respectively, at last follow-up. Conclusions: This study demonstrates the feasibility of performing complex, robotic-assisted ASD corrective surgery in a surgical specialty hospital, achieving significant correction of sagittal and coronal deformities, relieving patients’ symptoms, and offering efficiency and consistency to pedicle screw placement. This study demonstrates that a minimally invasive approach to complex deformity reconstruction reduces perioperative morbidity with decreased operative times, EBL, and LOS when compared to historic controls. This approach allows for the democratization of deformity care in that procedures typically reserved for large academic centers can be successfully accomplished at smaller institutions in optimized patients by qualified surgeons with appropriate perioperative support staff. Full article

Purpose: The posterior tibial plateau offset (PTPO) is a parameter of sagittal plane bony tibia morphology with high variability and clinical relevance, particularly in cases involving stemmed tibial implants, where posterior tibial cortex interference may occur. However, its change during total knee arthroplasty (TKA), and its relationship to tibial component positioning remain unknown. Methods: Pre- and postoperative sagittal radiographs of 98 patients undergoing primary, mechanically aligned TKA using a single implant system were retrospectively analyzed. PTPO was measured as the distance between the tibial anatomical axis and the center of the tibial plateau or tibial component. Tibial component placement (TCP) was assessed anteriorly and posteriorly and categorized as anatomical (0–1 mm), mild (1–3 mm), or moderate (>3 mm) underhang (TCU) or overhang (TCO). Pre- and postoperative changes in PTPO were analyzed, preoperative PTPO was compared across TCP categories. Correlations with absolute anterior and posterior deviation from anatomical component placements were calculated. Results: PTPO showed high preoperative variability (mean 6.89 ± 3.69 mm) and was significantly reduced after TKA (5.89 ± 3.44 mm; mean change −1.06 ± 3.44 mm; p < 0.001). Higher preoperative PTPO was associated with anterior (p = 0.01) and posterior TCU (p = 0.02). PTPO showed a moderate correlation with anterior (r = 0.53, p < 0.01) and a strong correlation with posterior implant deviation (r = 0.68, p < 0.01). Conclusions: PTPO shows high variability among patients undergoing TKA, is significantly altered through surgery and correlates with tibial component malposition, particularly TCU. Surgeons should consider PTPO during preoperative planning to optimize tibial component positioning and reduce the risk of implant-to-bone conflict, especially when using stemmed implants. In patients with a high preoperative PTPO, accuracy-enhancing techniques such as computer navigation or robotic assistance may be considered. Full article

Labor shortages and the need for increased productivity have accelerated the development of robotic harvesting systems for greenhouse crops; however, reliable operation under fruit occlusion and clustered arrangements remains a major challenge, particularly due to the limited integration between perception and motion planning modules. The paper presents the design and experimental validation of an autonomous robotic system for greenhouse tomato harvesting. The proposed platform integrates a rail-guided mobile base, a six-degrees-of-freedom robotic manipulator, and an adaptive end effector with a hybrid vision framework that combines convolutional neural networks and watershed-based segmentation to enable robust fruit detection and localization under occluded conditions. The proposed approach enables improved separation of overlapping fruits and provides accurate spatial localization through stereo vision combined with IMU-assisted camera-to-robot coordinate transformation. An occlusion-aware trajectory planning strategy was developed to generate collision-free manipulation paths in the presence of leaves and stems, enhancing harvesting safety and reliability. The system was trained and evaluated using a dataset of real greenhouse images supplemented with synthetic data augmentation. Experimental trials conducted under practical greenhouse conditions demonstrated a fruit detection precision of 96.9%, recall of 93.5%, and mean Intersection-over-Union of 79.2%. The robotic platform achieved an overall harvesting success rate of 78.5%, reaching 85% for unobstructed fruits, with an average cycle time of 15 s per fruit in direct harvesting scenarios. The rail-guided mobility significantly improved positioning stability and repeatability during manipulation compared with fully mobile platforms. The results confirm that integrating hybrid perception with occlusion-aware motion planning can substantially improve the functionality of robotic harvesting systems in protected cultivation environments. The proposed solution contributes to the advancement of automation technologies for greenhouse vegetable production and supports the transition toward more sustainable and labor-efficient agricultural practices. Full article

Patellar maltracking is among the most common causes of anterior knee pain after total knee arthroplasty (TKA), underscoring the need for accurate prevention and treatment. Therefore, the purpose of this narrative review is to provide a comprehensive overview of current evidence on post-TKA tracking, focusing on component alignment, preoperative patient assessment, and revision treatment options. A PubMed database search was performed, leveraging the literature from the last 20 years, and the results were qualitatively synthesized. According to current studies, several precautions should be taken to prevent patellofemoral stress and, consequently, patellar maltracking, such as avoiding internal rotation, valgus alignment, and excessive flexion of the femoral component and internal rotation of the tibial component. Regarding alignment strategies, kinematic alignment appears to offer potential benefits over mechanical alignment in certain functional outcomes and patient satisfaction scores. However, these differences should be interpreted cautiously as they may not always exceed the minimal clinically important difference. Furthermore, recent evidence indicates that quadriceps biomechanics influence TKA outcomes, potentially suggesting that conventional surgical approaches may need to be individualized, though these preliminary findings require prospective validation. Currently, robotic-assisted surgery represents a developmental direction for patient-tailored interventions and offers great promise for better prosthesis customization to the individual patient. Integration of imaging data with dynamic soft-tissue assessment enables more predictable reconstruction of joint kinematics. Regarding surgical treatment, the selection of specific methods requires a prior clinical and radiographic assessment. Indications range from patellar maltracking direction and component malrotation to patient preferences and rehabilitation potential. Ultimately, the future of TKA relies on personalized interventions to prevent complications and improve patient outcomes. This evolution is driven by the shift from mechanical alignment to kinematic alignment, alongside quadriceps tendon assessment and intraoperative robotic-assisted measurement, all aimed at optimizing the accuracy of implant positioning. Full article

Energy-efficient and resilient decentralized unmanned aerial vehicles (UAV) swarm operation in global navigation satellite system (GNSS) denied environments remains challenging because propulsion demand, communication load, and onboard inference are tightly coupled at the mission level. Although prior studies have examined some of these components separately, their joint evaluation within adaptive decentralized swarms remains limited under degraded navigation conditions. This study proposes an energy-aware adaptive communication-topology framework integrated with lightweight edge artificial intelligence (AI)-assisted navigation for decentralized UAV swarms operating without reliable GNSS support. The approach combines a unified mission-level energy-accounting structure for propulsion, communication, and onboard inference, a residual-energy-aware topology adaptation mechanism for preserving swarm connectivity, and a convolutional neural network-long short-term memory (CNN–LSTM) based edge-AI navigation module for improving localization robustness. The framework was evaluated in 1200 s Robot Operating System 2 (ROS2)–Gazebo–PX4 simulation scenarios against fixed topology and extended Kalman filter (EKF)-based baselines. Under the adopted simulation assumptions, the proposed configuration achieved a 22.7% reduction in total energy consumption, with the largest decrease observed in the communication-energy component, while preserving positive algebraic connectivity across all evaluated runs. The edge-AI module yielded a 4.8% root mean square error (RMSE) reduction relative to the EKF baseline, indicating a modest but meaningful improvement in localization performance. These results support the feasibility of integrated energy-aware swarm coordination in GNSS-denied environments; however, they should be interpreted as simulation-based evidence under the adopted modeling assumptions, and further high-fidelity propagation modeling, broader learning validation, and hardware-in-the-loop studies remain necessary. Full article

Many patients with primary or metastatic lung cancer are not candidates for surgery, additional radiation, or further systemic therapy due to advanced age or comorbidities; this creates a need for minimally invasive locoregional options. Image-guided thermal ablation (IGTA) is being applied across a broader spectrum of lesions, while bronchial artery chemoembolization (BACE) is emerging as a therapy option for treatment-refractory advanced disease. Recent studies in thermal ablation have focused on optimizing energy delivery and protocols, as well as improving ablation zone predictability and analysis. Advances in lesion targeting, including cone beam CT fusion, electromagnetic guidance, and robotic-assisted ablation, allow for treatment of subcentimeter and ground-glass lesions in anatomically challenging locations. Growing clinical experience supports IGTA for intrathoracic oligoprogression and as salvage therapy after recurrence. In the endovascular space, improved imaging, microcatheters, and drug-eluting microspheres have expanded the use of BACE for disease and symptom control in advanced lung cancer. Multimodal strategies combining minimally invasive locoregional treatments with systemic therapies and radiation are being explored, with early data showing improvements in survival without increased toxicity. This narrative review synthesizes emerging techniques, clinical data, and indications for percutaneous and endovascular lung cancer treatments and underscores the need for prospective and randomized trials to refine patient selection, treatment sequencing, and long-term outcomes. Full article

Artificial intelligence (AI) is increasingly transforming spine surgery, with expanding applications in diagnostics, intraoperative imaging, and surgical navigation. As the field advances toward greater precision and safety, machine learning (ML) and deep learning technologies are being integrated to augment surgeon expertise and optimize operative workflows. In particular, AI-driven innovations in image acquisition and navigation are reshaping intraoperative decision-making and technical execution. This narrative review provides an overview of AI applications relevant to intraoperative imaging and navigation in spine surgery. We begin by defining key concepts in AI, ML, and deep learning and briefly outline the historical evolution of AI within spine practice. We then examine current capabilities in image recognition and automated pathology detection, emphasizing their clinical relevance. Given the central role of imaging accuracy in modern navigation-assisted procedures, we review conventional acquisition platforms, including intraoperative computed tomography (CT) systems (e.g., O-arm, GE, Airo), surface-based registration to preoperative CT (Stryker, Medtronic), and optical surface mapping technologies (e.g., 7D Surgical). Emerging AI-optimized advancements are subsequently discussed, including low-dose intraoperative CT protocols, expanded scan windows, metal artifact reduction algorithms, integration of 2D fluoroscopy with preoperative CT datasets, and 3D reconstruction derived from 2D imaging. These developments aim to improve image quality, reduce radiation exposure, and enhance navigational accuracy. By synthesizing current evidence and technological progress, this review highlights how AI-enhanced imaging systems are redefining intraoperative spine surgery and shaping the future of precision-based care. The primary purpose of this review is to outline the applications of AI and its potential for perioperative and intraoperative optimization, including radiation exposure reduction, workflow streamlining, preoperative planning, robot-assisted surgery, and navigation. The secondary purpose is to define AI, machine learning, and deep learning within the medical context, describe image and pathology recognition, and provide a historical overview of AI in orthopedic spine surgery. Full article

Artificial intelligence (AI) is rapidly reshaping head and neck surgical oncology by augmenting decision-making across the full perioperative continuum. This state-of-the-art review aims to provide head and neck surgical oncologists with a conceptual framework for understanding and critically appraising AI tools entering clinical practice, summarizing how machine learning, deep learning, and generative AI are being integrated into contemporary surgical workflows. Preoperative applications include detection of occult nodal metastasis and extranodal extension. Intraoperative innovations include augmented reality-assisted navigation, real-time margin assessment, and improving visual clarity and tissue handling for robotic platforms. Postoperatively, AI can predict complications like free flap failure and oncologic outcomes. Large language models are being operationalized for clinician-facing applications such as documentation and inbox support, as well as patient-facing education. Despite promising results, broad clinical deployment remains limited by concerns about privacy, validation, reliability, safety, and ethics. Widespread adoption will require prospective clinical trials, robust governance, and human-centered workflows that ensure AI remains a safe, assistive copilot. Full article

This paper presents a low-cost, lightweight wearable sensing module for real-time multi-degree-of-freedom motion analysis, which is validated using ankle movements from a representative case study. The system is based on a compact inertial measurement unit integrated into a custom-made enclosure and employs Kalman filter-based sensor fusion to estimate three-dimensional joint orientation. An experimental campaign involving sixteen healthy participants was conducted, and measurements were compared against a gold-standard optical motion capture system, Optitrack V120 Trio. Ankle kinematics were analysed across all anatomical planes, including dorsiflexion/plantarflexion, inversion/eversion, and adduction/abduction. Quantitative metrics, including cosine similarity consistently above 0.98 across all movements and root mean square error within 4° on average, demonstrate strong agreement between the angular measuring device and motion capture data, with errors remaining within clinically acceptable limits. The results confirm the feasibility of the proposed system as a reliable, portable, and affordable alternative to laboratory-based measurement technologies. Beyond ankle assessment, the sensing approach is applicable to a wide range of motion-assistive and rehabilitation systems, supporting continuous monitoring, personalised therapy, and future integration into intelligent wearable devices. Full article

Management of unstable upper lumbar fractures with corpectomy and posterior fixation is technically demanding, and conventional workflows may require intraoperative repositioning, increasing operative complexity. Lateral mini-open upper lumbar corpectomy (LMULC) paired with robotic-assisted (RA) posterior percutaneous pedicle screw fixation (PPPSF) can be performed in a single position to facilitate ventral spinal decompression and stabilization in the anatomically constrained upper lumbar spine. In this study, we describe the operative technique and report four illustrative cases of unstable L1 or L2 fractures treated with single-position LMULC, RA-PPPSF, and short-segment fusion. Clinical, radiological, intraoperative variables and postoperative outcomes were evaluated. The mean age was 52.3 ± 17.7 years. The median operation time was 314 min (range 268–361 min); the median estimated blood loss (EBL) was 225 mL (range 100–400 mL). The median preoperative kyphosis was 10.15° (range 8.4–14.6°), the median postoperative kyphosis measured 6.65° (range 1.7–10.8°) and the median correction achieved was 3.5° (range −2.4–12.9°). The median visual analog scale (VAS) pain score reduced from 7 (range 7–9) preoperatively to 4.5 (range 2–6) postoperatively at discharge. At a median follow-up of 12 months (range 6–15 months), all patients had uncomplicated recoveries, demonstrated solid fusion on imaging, and reported favorable MacNab outcomes. Single-position LMULC with RA-PPPSF was technically feasible in this preliminary illustrative series and resulted in favorable clinical and radiographic outcomes. However, further studies in larger cohorts are warranted to help confirm these findings and better define the potential advantages and limitations of this technique. Full article

Background/Objectives: Robot-assisted partial nephrectomy (RAPN) remains a technically demanding procedure, associated with a non-negligible risk of perioperative complications. This study aimed to assess the impact of preoperative planning and intraoperative navigation using patient-specific three-dimensional (3D) virtual model reconstructions on perioperative outcomes of RAPN. Methods: We analyzed 307 patients who underwent RAPN for renal tumors at a tertiary center between 2021 and 2024. Starting in 2023, 3D modeling (Medics3D) was integrated for selected cases (n = 69) and compared to a 2D-imaging control group (n = 238). The primary outcome was trifecta achievement, defined as the simultaneous presence of negative surgical margins, ≥90% preservation of preoperative eGFR at discharge, and absence of perioperative complications. Clamping strategies were categorized as on-clamp, selective/super-selective, or off-clamp. Mann–Whitney and Chi-squared tests compared the groups; multivariable logistic regression identified independent predictors of trifecta achievement. Results: Baseline characteristics were balanced between the 3D and control groups: median age (62 vs. 61 years, p = 0.5), BMI (28 vs. 26, p = 0.3), and eGFR (85 vs. 86 mL/min/1.73 m², p = 0.5). Median tumor size was 4.2 vs. 4.0 cm (p = 0.4), and RENAL complexity was comparable (p = 0.12). Selective or super-selective clamping was significantly more frequent in the 3D group (32% vs. 15%; p < 0.01). While WIT (17.5 vs. 18.5 min, p = 0.09) and complication rates (26% vs. 29%, p = 0.7) were similar, the 3D group showed a significantly lower rate of positive surgical margins (5% vs. 15%; p = 0.030). Trifecta achievement was significantly higher in the 3D group (51% vs. 32%; p = 0.004). On multivariable analysis, 3D modeling remained an independent predictor of trifecta achievement (OR 2.1, 95% CI 1.17–3.70; p = 0.013). Conclusions: The use of patient-specific 3D kidney reconstructions was associated with improved perioperative outcomes in patients undergoing RAPN. These findings support the integration of 3D modeling into routine surgical workflows to enhance operative precision and optimize patient outcomes. Full article