International Journal of Computer Assisted Radiology and Surgery最新文献

{"title":"Introducing FRAM as a tool for surgical process modeling: a case study for understanding surgical scheduling complexity.","authors":"Sidra Rashid, Rebecca Weber, Alexander Geiger, Lars Wagner, Lukas Bernhard, Emily Spicker, Alissa Jell, Johannes Fottner, Dirk Wilhelm, Niklas Grabbe","doi":"10.1007/s11548-026-03800-2","DOIUrl":"https://doi.org/10.1007/s11548-026-03800-2","url":null,"abstract":"<p><strong>Introduction: </strong>Surgical processes are inherently variable leading to unexpected delays and cancellations. Due to inefficient and outdated processes, there is a need to understand and improve potential bottlenecks. However, traditional surgical process models (SPMs) focus on intraoperative procedures and sequential workflows, but most cannot adequately capture parallel workflows, variability, and sociotechnical interactions. We propose the use of Functional Resonance Analysis Method (FRAM) in conjunction with FRAMalyse to enable systematic measurement of variability propagation and potential process bottleneck identification. Additionally, we introduce a structured FRAM Model Interpreter (FMI) cycle-mapping interpretation protocol that maps function activations across cycles to temporal process phases, an approach for interpreting FRAM simulation outputs that has not been used in prior qualitative applications of the method.</p><p><strong>Material and methods: </strong>Based on stakeholder interviews and workflow observations, we developed a FRAM model of the daily surgical scheduling process at University Hospital Technical University of Munich (TUM). FMI profiles for each function were determined based on expert interviews and function activations were recorded during model simulation. Additionally, the model was analyzed using FRAMalyse to identify functions with high variability potential arising from their coupling structure, thereby providing a basis for future bottleneck validation and surgical scheduling optimization.</p><p><strong>Results: </strong>We identified 98 interconnected FRAM functions across four agents and phases. Scheduling and rescheduling functions spanned multiple cycles, indicating continuous and reciprocal coordination. Surgical team allocation function showed the highest variability, while attending surgeons performed the most functions and central patient management (CPM) had the highest coupling density.</p><p><strong>Conclusion: </strong>This study introduces the first quantitative application of FRAM and FRAMalyse in the healthcare domain, systematically identifying candidate bottlenecks across the full surgical scheduling pathway as a foundation for process improvement.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based segmentation of peritoneal cancer index regions from CT imaging.","authors":"Pieter C Gort, Lotte J S Fleurkens-Ewals, Lenah D Kampmeijer, Anna F van Herwijnen, Marion W Tops-Welten, Cris H B Claessens, Joost Nederend, Ignace H J T De Hingh, Max J Lahaye, Misha D P Luyer, Fons van der Sommen","doi":"10.1007/s11548-026-03796-9","DOIUrl":"https://doi.org/10.1007/s11548-026-03796-9","url":null,"abstract":"<p><strong>Purpose: </strong>Peritoneal metastases (PM) are staged using the surgically determined peritoneal cancer index (PCI), which requires invasive laparoscopic assessment. Although CT is routinely used for preoperative evaluation, imaging-based assessment of PM extent remains challenging and is often less structured than surgical PCI scoring. A recent consensus study defined radiological PCI (rPCI) regions for cross-sectional imaging. We present the first deep learning approach to automatically segment 13 rPCI regions on CT.</p><p><strong>Methods: </strong>62 contrast-enhanced CT scans were retrospectively collected across the full PCI range. Each scan was annotated into non-overlapping rPCI regions by one researcher, reviewed by a second, with disagreements resolved by a radiologist. Using fivefold cross-validation, we compared nnU-Net and Swin UNETR with Dice, 95th-percentile Hausdorff distance (HD95) and Average Surface Distance (ASD). We introduce an anatomically constrained pipeline that trains on merged super-regions and splits them during post-processing using TotalSegmentator landmarks at the hips and the ligament of Treitz.</p><p><strong>Results: </strong>On the identical 62-scan cohort, the baseline nnU-Net reached an overall Dice of 0.81 and outperformed Swin UNETR (0.76). The proposed anatomically constrained pipeline improved the overall Dice to 0.84 and reduced boundary error (HD95 <math><mrow><mn>13.7</mn> <mo>→</mo> <mn>11.8</mn></mrow> </math>  mm; ASD <math><mrow><mn>4.1</mn> <mo>→</mo> <mn>3.4</mn></mrow> </math>  mm), with the largest gains in the small-bowel regions, approaching the interobserver Dice of 0.87.</p><p><strong>Conclusions: </strong>Automated rPCI region segmentation on CT is feasible and approaches interobserver agreement. Encoding anatomical boundary constraints substantially improves segmentation quality in the most challenging regions. This provides a reproducible foundation for noninvasive, imaging-based PCI assessment. The main limitations are the single-center cohort and the small interobserver subset. Code is available at: https://github.com/PieterGort/rpci-region-segmentation.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance changes in automated lesion detection under federated learning with sequential institution addition.","authors":"Yukihiro Nomura, Shouhei Hanaoka, Aiki Yamada, Tomomi Takenaga, Takahiro Nakao, Toshiya Nakaguchi, Takeharu Yoshikawa, Osamu Abe","doi":"10.1007/s11548-026-03795-w","DOIUrl":"https://doi.org/10.1007/s11548-026-03795-w","url":null,"abstract":"<p><strong>Purpose: </strong>Federated learning (FL) enables multiple institutions to collaboratively train machine learning models while keeping data local and has attracted attention in medical image processing, including computer-aided detection (CAD). In FL, performance is expected to improve through retraining as additional institutions participate. The purpose of this study was to investigate how CAD software performance changes as the number of participating institutions is sequentially increased within an FL framework.</p><p><strong>Methods: </strong>We used two types of CAD software for cerebral aneurysm detection in magnetic resonance (MR) angiography images and brain metastasis detection in contrast-enhanced T1-weighted MR images. Datasets from different institutions or scanner vendors were treated as independent FL clients and incorporated sequentially. Training strategies included from-scratch training, fine-tuning (FT) of selected layers, and full fine-tuning (FFT) of all parameters. Performance was assessed using the competition performance metric on test sets from the initial institutions as well as from all participating institutions.</p><p><strong>Results: </strong>For both CAD software types, sequential institution addition combined with FT generally showed higher median performance changes than from-scratch training. FT showed performance comparable to that of FFT while requiring substantially fewer trainable parameters. Performance improvements generally accumulated with sequential institution addition, whereas simultaneous addition resulted in less consistent improvements.</p><p><strong>Conclusions: </strong>Sequential institution addition under FL may improve CAD software performance when combined with appropriate FT strategies. FT that updates only a subset of layers may achieve performance changes comparable to those of FFT while requiring substantially fewer trainable parameters across different lesion detection tasks.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reevaluation of the Bayesian network model to support immunotherapy decision-making in recurrent/metastatic head and neck squamous cell carcinoma.","authors":"Matthaeus Stoehr, Johannes Stoehr, Andreas Dietz, Jan Gaebel","doi":"10.1007/s11548-026-03803-z","DOIUrl":"https://doi.org/10.1007/s11548-026-03803-z","url":null,"abstract":"<p><strong>Purpose: </strong>Recurrent and metastatic head and neck squamous cell carcinoma (RM-HNSCC) is a challenging malignant disease due to limited treatment options and heterogeneous therapeutic responses. Despite Immunotherapy with PD-1 inhibitors such as Nivolumab and Pembrolizumab has become a standard of care, it is still complex and multifactorial to make a clinical decision in giving the patient its best individualized therapy. This study refines the use of Bayesian networks (BN) in application of supporting therapy selection in RM-HNSCC, integrating clinical, pathological, and molecular features.</p><p><strong>Methods: </strong>The previously introduced immune-oncologic BN for head and neck cancer was rechallenged by clinical data from 82 patients with RM-HNSCC. Therapy decisions (Nivolumab vs. Pembrolizumab) were calculated from the primary patient data. Model performance was assessed by comparing predicted versus actual therapy using retrospective data.</p><p><strong>Results: </strong>The reevaluation of the immune-oncologic BN revealed an inconsistency in the conditional probabilities of the initial model leading to a correction. Consequently, the model achieved 94.7% predictive accuracy for Nivolumab administration (18/19 cases) and 95.2% (60/63) for Pembrolizumab, yielding an overall prediction accuracy of 95.1% with a Cohens Kappa of 0.859. The model provided transparent reasoning paths for therapy selection and enabled hypothetical simulations in clinical scenarios.</p><p><strong>Conclusion: </strong>The immune-oncologic BN offers a promising approach to enhance personalized immunotherapy selection in RM-HNSCC patients by integrating diverse clinical variables into an interpretable decision support framework. The results support further prospective validation and clinical integration. Given the increasing complexity of biomarker-driven therapy, probabilistic models may contribute to more consistent and evidence-aligned treatment decisions.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From pre- to intra-operative MRI: predicting brain shift in temporal lobe resection for epilepsy surgery.","authors":"Jingjing Peng, Giorgio Fiore, Yang Liu, Ksenia Ellum, Debayan Dasgupta, Keyoumars Ashkan, Andrew McEvoy, Anna Miserocchi, Sebastien Ourselin, John Duncan, Alejandro Granados","doi":"10.1007/s11548-026-03794-x","DOIUrl":"https://doi.org/10.1007/s11548-026-03794-x","url":null,"abstract":"<p><strong>Introduction: </strong>Brain shift reduces the accuracy of neuronavigation based on preoperative magnetic resonance imaging (MRI). Intraoperative MRI can depict this deformation but is costly, disruptive, and not widely available.</p><p><strong>Methodology: </strong>We propose NeuralShift, a U-Net-based framework that predicts a dense brain displacement field from preoperative MRI and resection laterality for patients undergoing temporal lobe resection. Of 98 paired preoperative and intraoperative MRI cases, eight were reserved as a fixed validation set for checkpoint selection. The remaining 90 were divided into nine disjoint folds; nine independently initialised models were trained with 80 cases and evaluated on a previously unseen 10-case test fold. Performance was assessed using registration-referenced Target Registration Error (TRE) at ipsilateral and midline landmarks and overlap between predicted and intraoperative brain masks.</p><p><strong>Results: </strong>The predicted masks achieved an unweighted mean fold-wise Dice score of 0.97, with a mean within-fold patient-wise standard deviation of 0.015 (fold means, 0.95-0.98), compared with 0.93 and 0.014, respectively, before deformation. Mean post-prediction TRE ranged from 1.12 to 3.05 mm across the evaluated landmarks and resection sides, compared with 1.46 to 4.76 mm before deformation.</p><p><strong>Conclusion: </strong>NeuralShift demonstrates the feasibility of predicting a cohort-level prior for brain deformation using only information available before surgery. The registration-derived supervision, single-centre homogeneous cohort, and absence of external or independent physical validation preclude claims of clinical equivalence to biomechanical methods; prospective multi-centre validation is required. Code will be made publicly available after acceptance at https://github.com/SurgicalDataScienceKCL/NeuralShift .</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149065834","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From clinical challenges to innovation: an idea finding approach for advancing healthcare.","authors":"Emily Spicker, Sonja Stabenow, Sidra Rashid, Lukas Bernhard, Charlotte Haid, Maximilian Berlet, Johannes Fottner, Dirk Wilhelm","doi":"10.1007/s11548-026-03789-8","DOIUrl":"https://doi.org/10.1007/s11548-026-03789-8","url":null,"abstract":"<p><strong>Purpose: </strong>The workload in European healthcare is increasing due to demographic changes, as fewer employees must care for an ever-growing number of patients. Solutions that ease staff stress are needed, but successful development and integration require a thorough understanding of current challenges. Healthcare professionals identify opportunities to enhance patient care and processes, yet often lack the necessary knowledge and resources to implement their ideas. Engineers and management possess these resources but frequently lack insight into daily clinical struggles.</p><p><strong>Methods: </strong>This article presents a set of methods for analyzing clinical processes and identifying areas that need optimization from a non-medical perspective. The methods include observation, needs finding, and a system to prioritize and group identified needs.</p><p><strong>Results: </strong>Applied at a German university hospital, they produce a comprehensive list of needs that can inspire new projects. Using this methodology, we identify opportunities for improvement in documentation, purchasing, communication, information access, material preparation, and physical workload.</p><p><strong>Conclusion: </strong>The proposed methodology allows needs to be connected to actionable solutions. It provides a holistic overview, allowing for a comprehensive understanding, followed by a condensation of the issues that streamlines the generation of new topics and ideas.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated framework for volumetric musculoskeletal segmentation and assessment of torso CT images in large-scale databases.","authors":"Sanaa Amina Gourine, Mazen Soufi, Yoshito Otake, Yuto Masaki, Yoko Murakami, Yukihiro Nagatani, Yoshiyuki Watanabe, Keisuke Uemura, Kohei Kono, Masaki Takao, Takahito Fujimori, Nobuhiko Sugano, Yoshinobu Sato","doi":"10.1007/s11548-026-03785-y","DOIUrl":"https://doi.org/10.1007/s11548-026-03785-y","url":null,"abstract":"<p><strong>Purpose: </strong>Automated assessment of musculoskeletal (MSK) structures in large-scale torso CT databases remains challenging due to high anatomical complexity and imaging variability. This study proposes an automated framework for the volumetric segmentation and quantitative analysis of 19 torso muscles and two fat structures, validating the integration of predictive uncertainty as a quality control tool for large-scale analysis.</p><p><strong>Methods: </strong>We validated two model architectures incorporating structure-wise predictive uncertainty: a 2D Bayesian UNet (2D BUNet) using Monte-Carlo dropout sampling and a 3D patch-based nnUNet utilizing entropy of prediction probabilities, named 3D MSKSegmenter (3DMS). We additionally compared the accuracy against TotalSegmentator (TS), a state-of-the-art public whole body segmentation model. The predictive uncertainty was employed as a quality control tool to filter reliable cases for downstream feature analysis, and the investigation of structure volume and density [mean Hounsfield Units (HU)] associations with age and sex across a large-scale database.</p><p><strong>Results: </strong>The segmentation accuracy was validated via fivefold cross-validation on 20 manually labeled CT images. 3DMS achieved a superior average Dice coefficient (DC) of 0.89 ± 0.03 compared with 2D BUNet and TS (p < 0.001). Strong negative correlations between the predictive uncertainty and DC (- 0.870 for 3DMS and - 0.955 for 2D BUNet) were observed. Furthermore, an external test on a single-institutional database of approximately 1200 patients confirmed the robustness of our framework for large-scale analysis.</p><p><strong>Conclusions: </strong>We developed and validated an automated framework for volumetric segmentation and analysis of 21 torso structures. By integrating the predictive uncertainty, our method ensures reliable biomarker extraction in large-scale studies. Our findings revealed significant age- and sex-related trends in muscle density and volume, demonstrating the framework's potential as a scalable solution for automated MSK assessment in CT images.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PICO: Projection-Informed Consistency Optimisation for 6DoF surgical tool pose estimation.","authors":"Lucy Fothergill, Pietro Valdastri, Dominic Jones, Duygu Sarikaya","doi":"10.1007/s11548-026-03802-0","DOIUrl":"https://doi.org/10.1007/s11548-026-03802-0","url":null,"abstract":"<p><strong>Purpose: </strong>Accurate 6 DoF pose estimation of surgical tools is critical for automation, robotic proprioception, and safe interaction with the tissue operated on. Kinematics-based approaches suffer from accumulated errors due to the cable-driven nature of robotic arms, while vision-based methods often rely on external markers or trackers. Although more recent vision-based advances have been proposed, these two-stage pose estimation methods often lack real-time robustness due to accumulated errors and computational overhead.</p><p><strong>Methods: </strong>We propose a novel end-to-end trainable model, PICO. Our model employs a multi-task learning architecture to predict segmentation and depth maps, alongside regression of translation and rotation parameters. We define two proxy tasks that enforce geometric consistency in both 2D and 3D spaces, improving accuracy and robustness. For this, we propose a projection loss, and a point-to-point loss.</p><p><strong>Results: </strong>We evaluate our method on the SurgRIPE dataset, benchmarking its performance against state-of-the-art approaches using standard 6DoF pose estimation metrics. Our results demonstrate consistently strong performance across all four subsets, specifically in rotation, ranking second even under occlusion. It also demonstrates comparable translational performance, remaining competitive, especially in occluded cases.</p><p><strong>Conclusion: </strong>PICO demonstrates the effectiveness of multi-task learning and geometry-aware proxy tasks for robust and reliable surgical tool pose estimation, especially in occluded scenarios, highlighting potential for future applications.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149041703","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A U-Net Transformer for magnetic resonance elastography.","authors":"Weiheng Zhong, Matthew W Urban, Hadi Meidani","doi":"10.1007/s11548-026-03790-1","DOIUrl":"https://doi.org/10.1007/s11548-026-03790-1","url":null,"abstract":"<p><strong>Purpose: </strong>Magnetic resonance elastography (MRE) is a critical non-invasive modality for quantifying tissue stiffness, but its clinical utility is limited by the high computational cost and noise sensitivity of traditional inversion techniques, such as algebraic Helmholtz inversion (AHI) and finite element methods (FEM). This study proposes a high-performance deep learning alternative to accelerate and stabilize MRE reconstruction.</p><p><strong>Methods: </strong>We developed an open-source, physics-based simulation framework that pairs realistic liver and tumor geometries from the LiTS dataset with clinically informed stiffness priors to generate a large-scale benchmark dataset. To improve reconstruction accuracy, we introduce the U-Net Transformer (UNT), a novel architecture that hybridizes the local inductive bias of convolutional neural networks with the global context-awareness of Transformers.</p><p><strong>Results: </strong>Numerical experiments indicate that data-driven training consistently outperforms physics-informed training in prediction accuracy. The proposed UNT architecture demonstrates significantly higher reconstruction accuracy than current state-of-the-art machine learning models. Additionally, we validated a physics-informed fine-tuning strategy that adapts the model to \"sparse-data\" clinical scenarios, enhancing patient-specific accuracy with minimal computational overhead.</p><p><strong>Conclusion: </strong>The UNT architecture and the accompanying simulation framework establish a standardized foundation for AI-driven MRE inversion. By providing a rigorous performance baseline for neural operators, this work offers a validated pathway toward real-time, AI-assisted clinical decision support in radiological diagnostics.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Expanding research on clinical agent data to a virtual space of location, time, and context: exploration of data structures and limitations using a pragmatic digital twin.","authors":"Sidra Rashid, Katarina Sliepkova, Lukas Bernhard, Sonja Stabenow, Emily Spicker, Stefanie Rinderle-Ma, Johannes Fottner, Dirk Wilhelm, Maximilian Berlet","doi":"10.1007/s11548-026-03788-9","DOIUrl":"https://doi.org/10.1007/s11548-026-03788-9","url":null,"abstract":"<p><strong>Introduction: </strong>Fragmented healthcare data and the inflexible structure of current hospital information systems (HIS) hinder the routine use of data for research and process optimization. Conventional HISs often fail to represent physical resources, spatial relationships, and temporal dependencies among hospital entities. In this feasibility study, we demonstrate the practical application of OMNI-SYS, a pragmatic object-centric hospital information system (oHIS) framework that represents objects within a 3D space of Time, Location, and Context. Using pre-operative patient tracking as an example, we show how OMNI-SYS generates structured datasets suitable for process mining and clinical workflow optimization.</p><p><strong>Material and methods: </strong>At TUM University Hospital in Munich, 18 healthy volunteers followed predefined pre-operative pathways for seven common gastrointestinal surgery procedures. Participants moved through real hospital departments, including administrative registration, premedication, ECG, lung function, endoscopy, CT, and MRI, while spatiotemporal events were recorded using OMNI-SYS. OMNI-SYS is an object-centric digital twin that integrates this data, capturing all relevant agents and their interactions throughout the study. The resulting dataset, with 88 timestamped events, was analyzed for its structure and suitability for advanced data analysis.</p><p><strong>Results: </strong>OMNI-SYS successfully captured interactions among patients, simulated staff, and devices at all pre-operative stations. The resulting object-centric data structure was directly compatible with contemporary process mining and workflow optimization methods. Patient co-occurrence at different stations could be efficiently assessed, and OMNI-SYS enabled all recorded events to be replayed for comprehensive post-hoc analysis of inter-object relationships.</p><p><strong>Conclusion: </strong>oHIS-like frameworks, such as OMNI-SYS, can facilitate routine clinical data and process mining by providing structured datasets for modern evaluation. In our study, OMNI-SYS generated object-centric data that models healthcare entities and their interactions, addressing key limitations of current data infrastructures. These results provide a foundation for designing real-world studies to analyze routine clinical workflows and align data structures to bridge clinical routine and research.</p>","PeriodicalId":51251,"journal":{"name":"International Journal of Computer Assisted Radiology and Surgery","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149009051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}