Computer Assisted Surgery最新文献

{"title":"The impact of three-dimensional simulation and virtual reality technologies on surgical decision-making and postoperative satisfaction in aesthetic surgery: a preliminary study.","authors":"Mert Ersan, Hasan Demirbaşoğlu, Begüm Kolcu, Hilal Aybüke Yıldız","doi":"10.1080/24699322.2026.2614532","DOIUrl":"10.1080/24699322.2026.2614532","url":null,"abstract":"<p><strong>Background: </strong>Three-dimensional (3D) simulation and virtual reality (VR) technologies are increasingly used in aesthetic surgery consultations to enhance decision-making and expectation management. However, their impact on surgical decision-making and postoperative satisfaction across different procedures remains unclear.</p><p><strong>Objectives: </strong>This study aimed to evaluate the influence of 3D simulation and VR technology in patients undergoing rhinoplasty, breast augmentation, mastopexy, augmentation-mastopexy and breast reduction.</p><p><strong>Methods: </strong>A retrospective study was conducted with 75 female patients who underwent primary aesthetic surgery. Preoperative 3D simulations and VR visualizations were generated using the Crisalix Virtual Esthetics system (Crisalix S.A., Switzerland). Patients were assessed postoperatively at one year using structured surveys to evaluate the influence of 3D simulation and VR technology on their decision-making and satisfaction. Statistical analyses included the Kruskal-Wallis H test, the Chi-Square test, and Spearman's correlation.</p><p><strong>Results: </strong>3D simulation had the greatest influence on breast augmentation (8.4/10), rhinoplasty (7.6/10), and augmentation-mastopexy (7.1/10) patients but was less impactful for mastopexy (6.6/10) and breast reduction (3.8/10) patients (<i>p</i> < 0.001). The most decisive factors were previous patient photos (30.7%) and communication with the surgeon (29.3%), with simulation ranking third (18.7%). Postoperative similarity ratings were highest in breast augmentation (7.9/10) and rhinoplasty (7.5/10) patients. While 70.7% of patients would recommend 3D simulation, VR headset use did not influence decisions (<i>p</i> < 0.001).</p><p><strong>Conclusions: </strong>3D simulation enhances patient engagement and expectation management across various aesthetic procedures. While its influence is more significant in surgeries primarily focused on aesthetic outcomes, it serves as a complementary tool rather than a definitive factor in decision-making.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2614532"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145949422","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"No cancer left behind: a testbed and demonstration of concept for photoacoustic tumor bed inspection.","authors":"Laura Connolly, Hyunwoo Song, Keshuai Xu, Anton Deguet, Simon Leonard, Gabor Fichtinger, Parvin Mousavi, Russell H Taylor, Emad Boctor","doi":"10.1080/24699322.2025.2604123","DOIUrl":"10.1080/24699322.2025.2604123","url":null,"abstract":"<p><p>Cancer resection surgery is unsuccessful if tumor tissue is left behind in the surgical cavity. Identifying the residual cancer requires additional imaging or postoperative histological analysis. Photoacoustic imaging can be used to image both the surface and depths of the resection cavity; however, its performance hinges on consistent probe placement and stable acoustic and optical coupling. As intra-cavity deployment of photoacoustic imaging is largely uncharted, several potential embodiments warrant rigorous investigation. We address this need with an open-source robotic testbed for intraoperative tumor-bed inspection using photoacoustic imaging. The platform integrates the da Vinci Research Kit, depth imaging, and electromagnetic tracking to automate cavity scanning and maintain repeatable probe trajectories. Using tissue-mimicking phantoms, we (i) demonstrate a novel imaging embodiment for photoacoustic tumor-bed inspection and (ii) show how this testbed can be used to investigate and optimize tumor bed inspection strategies and configurations. This study establishes the feasibility of detecting and mapping residual cancer within a simulated surgical cavity. The primary contribution is the testbed itself, designed for integration with existing surgical navigation workflows and rapid prototyping. This testbed serves as an essential foundation for systematic evaluation of photoacoustic, robot-assisted strategies for improving intraoperative margin assessment.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2604123"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12925404/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcement learning for medical image analysis: a systematic review of algorithms, engineering challenges, and clinical deployment.","authors":"Masuda Begum Sampa, Nor Hidayati Abdul Aziz, Md Siddikur Rahman, Nor Azlina Ab Aziz, Rosli Besar, Anith Khairunnisa Ghazali","doi":"10.1080/24699322.2025.2597553","DOIUrl":"10.1080/24699322.2025.2597553","url":null,"abstract":"<p><p>Reinforcement learning (RL) has emerged as a powerful artificial intelligence paradigm in medical image analysis, excelling in complex decision-making tasks. This systematic review synthesizes the applications of RL across diverse imaging domains-including landmark detection, image segmentation, lesion identification, disease diagnosis, and image registration-by analyzing 20 peer-reviewed studies published between 2019 and 2023. RL methods are categorized into classical and deep reinforcement learning (DRL) approaches, focusing on their performance, integration with other machine learning models, and clinical utility. Deep Q-Networks (DQN) demonstrated strong performance in anatomical landmark detection and cardiovascular risk estimation, while Proximal Policy Optimization (PPO) and Advantage Actor-Critic (A2C) achieved optimal policy learning for vessel tracking. Policy gradient methods such as REINFORCE, Twin-Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC) were successfully applied to breast lesion detection, white-matter connectivity analysis, and vertebral segmentation.Monte Carlo learning, meta-RL, and A3C methods proved effective for adaptive questioning, image quality evaluation, and multimodal image registration. To consolidate these findings, we propose a unified Reinforcement Learning Medical Imaging (RLMI) framework encompassing four core components: state representation, policy optimization, reward formulation, and environment modeling. This framework enhances sequential agent learning, stabilizes navigation, and generalizes across imaging modalities and tasks. Key challenges remain, including optimizing task-specific policies, integrating anatomical contexts, addressing data scarcity, and improving interpretability. This review highlights RL's potential to enhance accuracy, adaptability, and efficiency in medical image analysis, providing valuable guidance for researchers and clinicians applying RL in real-world healthcare settings.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2597553"},"PeriodicalIF":1.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145835369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy and agreement between optical navigation (NDI Polaris) and postoperative CT for glenoid pin placement: a cadaveric validation study using AR-guidance.","authors":"Hoel Letissier, Damien Cariou, Agathe Trehin, Gilles Walch, Julien Berhouet, Jean Chaoui","doi":"10.1080/24699322.2026.2709279","DOIUrl":"https://doi.org/10.1080/24699322.2026.2709279","url":null,"abstract":"<p><p>Optimizing glenoid component positioning is a key determinant of stability, function and implant survival in total shoulder arthroplasty. Postoperative computed tomography (CT) is considered the reference method for assessing guide-pin or implant alignment, but it remains a retrospective evaluation and is often difficult to obtain in cadaveric research settings. Optical navigation systems such as the NDI Polaris provide immediate intraoperative geometric measurements and may represent a pragmatic alternative. This cadaveric validation study aimed to evaluate the agreement between NDI Polaris optical-navigation measurements and postoperative CT analysis for glenoid guide-pin positioning. Eight cadaveric shoulders underwent augmented-reality-guided placement of a glenoid guide-pin, followed by postoperative CT acquisition. Intraoperative optical-navigation measurements were recorded using an NDI Polaris tracking station. Two accuracy domains were analyzed relative to CT: (1) linear entry-point deviation (mm) and (2) angular deviation in version and inclination (degrees). Agreement between both measurement modalities was assessed using Bland-Altman analysis, complemented by descriptive accuracy statistics. Sixteen paired measurements were available for comparison. The mean absolute NDI-CT difference was 0.98 mm (SD 0.55; range 0.15-2.17 mm) for entry-point localization and 1.58° (SD 0.97; range 0.03-3.28°) for trajectory orientation. Bland-Altman analysis demonstrated a small systematic bias for both linear (-0.15 mm) and angular (-0.75°) parameters, with most discrepancies contained within approximately ±2 mm and ±4°, respectively. These results suggest that, under standardized acquisition conditions, optical navigation provides geometric measurements that closely approximate CT-based reference values and may serve as a practical surrogate in cadaveric research when CT access is limited. NDI Polaris optical tracking demonstrated good agreement with postoperative CT for evaluating glenoid pin positioning and may represent a practical alternative for accuracy assessment in cadaveric studies. These preliminary findings require confirmation in larger validation studies.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2709279"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148882737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid biofabrication platform for patient-specific aortic phantoms: from surgical rehearsal to device testing.","authors":"Qingzhuo Chi, Guofeng Wang, Wenjun Wang, Xijing Zhuang, Yuanzhe Jin, Bo Liu, Ying He","doi":"10.1080/24699322.2026.2694865","DOIUrl":"https://doi.org/10.1080/24699322.2026.2694865","url":null,"abstract":"<p><p>Fabricating high-fidelity, patient-specific aortic phantoms that possess both complex pathological features and physiological compliance remains a significant challenge for single-method manufacturing techniques. This study presents a complete virtual-to-physical prototyping workflow, enabled by a novel Hybrid Additive Manufacturing Platform (HAMP), for translating clinical imaging data into high-fidelity, patient-specific aortic phantoms. Building upon a validated brush-spin-coating technique capable of precise wall thickness control (±0.1 mm), the HAMP synergistically integrates 3D printing and casting. This integration overcomes the limitations of single-method techniques, uniquely enabling the creation of phantoms with (i) controllable interlayer delamination for mimicking dissection, (ii) enclosed multi-chamber structures for endoleak simulation, (iii) seamless integration of dissimilar materials, and (iv) the replication of complex intra-wall pathologies such as intramural hematoma. The platform's capability was rigorously demonstrated through the successful fabrication and application of four distinct classes of aortic phantoms. These high-fidelity models were directly employed in: fundamental biomechanical studies to visualize dissection propagation; advanced surgical training for complex procedures like <i>ex vivo</i> fenestration; emergency preoperative planning, where a patient-specific model was delivered in under 30 h; and industrial medical device testing using parametric, ISO-compliant models. In each scenario, the phantoms provided functional, anatomically accurate representations suitable for the intended evaluation-whether physical testing, surgical rehearsal, or hydrodynamic assessment. In summary, the HAMP demonstrates a rapid virtual-to-physical prototyping workflow. By enabling the on-demand creation of complex, multi-material, patient-specific phantoms, it provides a versatile tool that bridges digital data and physical reality, addressing needs across research, clinical training, and device development.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2694865"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An automatic detection model for spread through air spaces in postoperative pathological sections based on deep learning in NSCLC.","authors":"Yu Zhang, Shuzhe Deng, Mingyuan Guo, Yuxin Kang, Enguang Lin, Hongxue Meng, Fenqi Du, Yanbin Zhao","doi":"10.1080/24699322.2026.2674321","DOIUrl":"10.1080/24699322.2026.2674321","url":null,"abstract":"<p><p>Spread through air spaces (STAS) is recognized as an aggressive pattern of invasion in lung cancer and has been associated with poorer survival outcomes. However, STAS is frequently overlooked or misdiagnosed during routine pathological diagnosis. We analyzed 129 pathological slides from 91 STAS-positive patients with non-small cell lung cancer (NSCLC). An artificial intelligence framework consisting of a tumor region segmentation algorithm and an object detection algorithm was developed. The segmentation algorithm was first used to isolate the main tumor region, followed by STAS detection using an improved object detection model. The segmentation module achieved a Jaccard similarity of 0.846. The proposed object detection algorithm demonstrated superior performance with a precision of 0.738, recall of 0.747 and average precision (AP) of 0.784 and F1 score of 0.742, outperforming other object detection methods. Both segmentation and detection results met diagnostic requirements, with higher accuracy observed in lung adenocarcinoma (LUAD) than in lung squamous cell carcinoma (LUSC). In external validation, the model achieved a precision of 0.670, recall of 0.705 and AP of 0.732 and F1 score of 0.687. Additionally, the AI-derived STAS counts showed substantial agreement with pathologists (interclass correlation coefficient = 0.703). Kaplan-Meier survival analysis revealed that the number of STAS events was significantly associated with disease-free survival (DFS) in stage I LUAD (<i>p</i> < .05). We propose a deep learning-based artificial intelligence framework for automated STAS detection and quantification in digital whole-slide images of NSCLC. This model holds promising potential to assist pathologists in achieving more comprehensive and accurate diagnosis of STAS.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2674321"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148007608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AutoPathNet: a patient-specific automated 3D trajectory planning framework for minimally invasive evacuation of hypertensive intracerebral hemorrhage.","authors":"Wei Li, Liu Dong, Hairong Yan, YueYang Zhang, Xiliang Liu, Ke Tan","doi":"10.1080/24699322.2026.2726003","DOIUrl":"https://doi.org/10.1080/24699322.2026.2726003","url":null,"abstract":"<p><strong>Purpose: </strong>Rapid and reproducible trajectory planning is important for minimally invasive evacuation of hypertensive intracerebral hemorrhage (ICH). We developed AutoPathNet, a patient-specific automated 3D trajectory-planning framework that generates geometrically favorable candidate trajectories from preoperative imaging.</p><p><strong>Methods: </strong>AutoPathNet reconstructs patient-specific anatomical models from CT-derived inputs, generates candidate entry-target trajectories, filters candidates according to predefined anatomical constraints, and ranks feasible trajectories using a composite geometric planning metric. Algorithm-generated trajectories were compared with surgeon-implemented catheter trajectories reconstructed from postoperative CT in a 21-patient validation cohort.</p><p><strong>Results: </strong>The framework generated three candidate trajectories within approximately 31 s. In 16 of 21 paired cases (76.2%; Wilson 95% confidence interval, 54.9%-89.4%), the best algorithm-generated candidate had a lower <i>m</i> value than the surgeon-implemented catheter trajectory reconstructed from postoperative CT. This comparison uses postoperative catheter position as a procedural imaging reference and reflects geometric planning performance within the current constraint model, suggesting potential value as a neurosurgeon-supervised planning reference rather than evidence of clinical outcome superiority.</p><p><strong>Conclusion: </strong>AutoPathNet rapidly generated patient-specific candidate trajectories for minimally invasive evacuation of hypertensive ICH. The framework may support neurosurgeon-supervised trajectory planning, but prospective validation using multimodal functional constraints and clinical outcome endpoints is required before clinical deployment.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2726003"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978940","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DMRNet: a dynamic multi-scale residual network for Shamrock view and lumbar plexus segmentation.","authors":"Haipo Cui, Yuxiang Wang, Liangqing Lin, Qinghua Wu, Miao Zhou, Wenhui Guo, Ying Shen, Zui Zou","doi":"10.1080/24699322.2026.2677269","DOIUrl":"https://doi.org/10.1080/24699322.2026.2677269","url":null,"abstract":"<p><p>Lumbar plexus block (LPB) is a regional anesthesia technique widely used for hip and knee surgeries. However, despite the assistance of ultrasound guidance, the complex anatomical structure of the lumbar plexus poses significant challenges for anesthesiologists during the procedure. To accurately identify the lumbar plexus located in the posterior third of the psoas major in the Shamrock view, a deep learning-based segmentation model named DMRNet was proposed. This model is designed to precisely delineate muscles, nerves, and bony structures in Shamrock view ultrasound images. DMRNet integrates several innovative modules, including Adaptive Multi-Scale Dilated (AMD) Module that enhances the model's ability to capture multi-scale features; Dense Attention Residual (DAR) Module that adaptively selects salient feature regions; Attention-Enhanced Hybrid (AEH) Module that emphasizes critical features while suppressing irrelevant ones; and two attention mechanisms, Boundary-Aware Spatial Attention Mechanism (BASA) and the Enhanced Residual Multi-Head Attention Mechanism (ER-MHA), that improve the model's capacity to recognize complex contextual patterns. Experimental results demonstrated that DMRNet achieved a mean Intersection over Union of 0.863 and a mean Dice coefficient of 0.926 across all target structures, outperforming other state-of-the-art models. These findings suggest that DMRNet may serve as a useful assistive tool for sonoanatomical recognition in Shamrock view ultrasound images and may provide educational support for ultrasound-guided LPB training.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2677269"},"PeriodicalIF":1.9,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148240280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leg positioner improves efficiency on selected steps in robotic-assisted total knee arthroplasty: an analysis of surgical workflow efficiency and team experience.","authors":"David Putzer, Ahmed Mohamed, Michael C Liebensteiner, Martin Thaler, Michael Nogler","doi":"10.1080/24699322.2026.2668783","DOIUrl":"10.1080/24699322.2026.2668783","url":null,"abstract":"<p><p>Robotic-assisted total knee arthroplasty (TKA) improves surgical precision and reproducibility. Leg positioners are used to stabilize the limb and support workflow, but their impact in robotic-assisted TKA remains unclear. This study examined whether leg positioner use influences surgical efficiency, workflow, and team experience. A retrospective non randomized single-center analysis was conducted on 79 robotic-assisted TKAs performed between 2018 and 2023 with the MAKO system. Fifty-seven procedures (72%) used a leg positioner, while 22 (28%) served as a control group. Three senior surgeons performed the operations, with step durations and system interactions recorded by independent observers. Surgical phases were divided into preparation, cut-to-suture, and wrap-up, and further into robotic-assisted and conventional steps. Postoperative questionnaires based on the NASA-TLX framework were completed by surgeons and scrub technicians. Median cut-to-suture time was 1:29 h, with no significant difference between leg positioner (1:25 h) and control cases (1:35 h, <i>p</i> = 0.251). Robotic-assisted steps (0:28 h) were unaffected (<i>p</i> = 0.763), while conventional steps were significantly longer without the leg positioner (0:41 h vs. 0:34 h, <i>p</i> = 0.006). Sub-analysis showed slower bone registration and robot positioning with the leg positioner, but faster final implantation (3 vs. 5 min, <i>p</i> < 0.001) and suturing (16 vs. 19 min, <i>p</i> = 0.027). Questionnaires indicated high satisfaction overall, though surgeons reported reduced ease of robotic arm operation and confidence in ligament balancing. The leg positioner redistributed time across surgical steps rather than improving overall efficiency. It offers stability benefits but may restrict intraoperative flexibility. Further studies should address ergonomics, cost-effectiveness, and long-term outcomes.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2668783"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147856656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The VELYS robotic-assisted solution: a strategic technology evaluation informed through scoping review.","authors":"Scott R Morrison, Andrew J Hall, Nick D Clement, Christopher Gee, Swati Chopra, Jon V Clarke","doi":"10.1080/24699322.2026.2699503","DOIUrl":"10.1080/24699322.2026.2699503","url":null,"abstract":"<p><p>Robot-assisted surgery (RAS) is increasingly utilized in arthroplasty, with emerging evidence suggesting potential benefits, however, the overall evidence base remains immature. Given the heterogeneity of available systems, device-specific evaluation is required. The VELYS Robotic-Assisted Solution (VRAS; Johnson & Johnson MedTech) is an imageless, semi-active robotic platform for knee arthroplasty. This study provides a structured strategic evaluation of VRAS aligned with the National Institute for Health and Care Excellence Early Value Assessment (EVA) and proposes a roadmap for future evidence generation. 16 relevant studies were identified in a systematic literature search, predominantly retrospective and lacking randomized controlled trials. Findings were synthesized using a strengths, weaknesses, opportunities, and threats framework, with subsequent analysis to inform strategic recommendations. Key strengths of VRAS include avoidance of pre-operative computed tomography imaging, integration with established implant systems, a relatively short learning curve, and early signals of improved short-term outcomes. Weaknesses center on the limited quality and maturity of the evidence base, absence of long-term outcome data, and system-specific design considerations such as lack of haptic feedback boundaries. Opportunities include reduced resource burden with imageless workflows and potential optimization of patient outcomes, while threats involve high capital costs, competition from more established RAS platforms, and the risk of unmet evidence requirements within defined timelines. Across EVA domains, evidence for patient-reported outcomes, cost-effectiveness, and long-term clinical benefit remains insufficient. High-quality, adequately powered studies, comprehensive health-economic analyses, and registry-linked longitudinal studies are essential to define the clinical and economic value of VRAS in light of the EVA.</p>","PeriodicalId":56051,"journal":{"name":"Computer Assisted Surgery","volume":"31 1","pages":"2699503"},"PeriodicalIF":2.3,"publicationDate":"2026-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148450530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}