Healthcare Technology Letters最新文献

{"title":"Acquiring Aligned Endoscopic and Depth Image Pairs Using Structured-Light Projection, Neural Surfaces and an Electromagnetic Positional Sensor","authors":"Ryo Furukawa,&nbsp;Taiyo Inui,&nbsp;Ryusuke Sagawa,&nbsp;Hiroshi Kawasaki","doi":"10.1049/htl2.70039","DOIUrl":"10.1049/htl2.70039","url":null,"abstract":"<p>Minimally invasive endoscopic procedures are gaining importance for better patient outcomes. While traditional systems use standard cameras, there is an increasing demand for 3D information to improve diagnostic and surgical accuracy. As a result, various 3D endoscopic systems have been developed, with structured-light (SL) projection gaining traction for its compatibility with existing equipment. Despite advances in SL based scan, practical 3D endoscopy still faces key challenges. These include issues like pattern interference and pose estimation failures. Additionally, removing projected patterns to obtain clean texture images remain crucial for medical diagnosis. To address these challenges, we propose an approach based on neural signed distance field (neural SDF), incorporating a pattern reflection model. The method integrates (1) camera pose estimation using an electromagnetic sensors, (2) 3D shape measurement via SL projection, (3) multi-frame shape integration using a neural implicit surface representation, and (4) removal of projected patterns from endoscopic images using a pix2pix model. To ensure robust optimization, electromagnetic tracking provides accurate initial camera poses for optimization. By simulating SL projection in the neural surface model and optimizing camera pose, projector pose, and surface geometry, and by removing SL projection from the captured images, we can obtain paired sequences of endoscopic and depth maps.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12686141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAMmorph: A Weakly-Supervised Multimodal Medical Image Registration With SAM-Based Segmentation and Bidirectional Mechanism","authors":"Qilin Wang,&nbsp;Mengting Yang,&nbsp;Yipeng Hu,&nbsp;Zhe Min","doi":"10.1049/htl2.70048","DOIUrl":"10.1049/htl2.70048","url":null,"abstract":"<p>In this paper, we introduce SAMmorph, a novel multimodal medical image registration framework addressing challenges in aligning cross-modality images due to significant appearance differences and heavy reliance on expensive annotations. It comprises three key components: the VoxelSAM module for generating pseudo-labels, the FusionLayer which takes moving/fixed images and their pseudo-labels as input, integrates them via two optional schemes (stack: concatenating along the channel dimension to form a 4-channel tensor; enhance: modulating image intensity with mask confidence to generate a 3-channel tensor) to produce combined features for registration, and the deformable registration network which includes two architectures (joint and independent registration networks) to implement the bidirectional registration strategy. Experiments demonstrate that SAMmorph outperforms state-of-the-art methods across various metrics on the <span></span><math>\u0000 <semantics>\u0000 <mi>μ</mi>\u0000 <annotation>$mu$</annotation>\u0000 </semantics></math>-RegPro dataset, achieving a dice similarity coefficient (DSC) of 0.906, target registration error (TRE) of 6.06 mm, compared to those being 0.789 and 7.632 mm achieved by the strongest tested baseline method CorrMLP, with statistical significance confirmed by Wilcoxon signed-rank tests (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>p</mi>\u0000 <mo>&lt;</mo>\u0000 <mn>0.01</mn>\u0000 </mrow>\u0000 <annotation>$p&lt;0.01$</annotation>\u0000 </semantics></math>). This work presents a promising approach for improving medical image registration accuracy and efficiency, with potential applications in diverse medical imaging scenarios. The source codes have been released at: https://github.com/qilin-world/SAMmorph.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145739595","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soft Tissue Simulation and Force Estimation From Heterogeneous Structures Using Equivariant Graph Neural Networks","authors":"Madina Kojanazarova,&nbsp;Sidaty El Hadramy,&nbsp;Jack Wilkie,&nbsp;Georg Rauter,&nbsp;Philippe C. Cattin","doi":"10.1049/htl2.70042","DOIUrl":"10.1049/htl2.70042","url":null,"abstract":"<p>Accurately simulating soft tissue deformation is crucial for surgical training, pre-operative planning, and real-time haptic feedback systems. While physics-based models such as the finite element method (FEM) provide high-fidelity results, they are often computationally expensive and require extensive preprocessing. We propose a graph neural network (GNN) architecture that predicts both tissue surface deformation and applied force from sparse point clouds. The model incorporates internal anatomical information through binary tissue profiles beneath each point and leverages E(n)-equivariant message passing to improve robustness. We collected experimental data that comprises a real silicone and bone-like phantom, and complemented it with synthetic simulations generated using FEM. Our model achieves a comparable performance to a baseline GNN on standard test cases and significantly outperforms it in rotated and cross-resolution scenarios, showing a strong generalization to unseen orientations and point densities. It also achieves a significant speed improvement, offering a solution for real-time applications. When fine-tuned on experimental data, the model maintains sub-millimeter deformation accuracy despite limited sample size and measurement noise. The results demonstrate that our approach offers an efficient, data-driven alternative to traditional simulations, capable of generalizing across anatomical configurations and supporting interactive surgical environments.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70042","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145686474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Landmark-Free 3D–2D Rigid Liver Registration via Point Cloud Matching for Laparoscopic Surgery","authors":"Binyan Huang,&nbsp;Xiangyue Yang,&nbsp;Fucang Jia","doi":"10.1049/htl2.70030","DOIUrl":"10.1049/htl2.70030","url":null,"abstract":"<p>Real-time registration of preoperative 3D liver models to intraoperative 2D laparoscopic images is essential for augmented reality navigation in minimally invasive liver surgery. However, 3D–2D registration typically depends on anatomical landmarks extraction and pose estimation based on iterative projection-based landmark distance computation, which is time-consuming. Unlike iterative pose refinement strategies, our method treats liver pose estimation as a partial-to-complete point matching problem. First, our method leverages monocular depth estimation to reconstruct partial intraoperative point clouds from a single RGB image. Then, a two-stage point matching framework establishes dense 3D–3D correspondences, ultimately inferring the 6-DoF rigid pose by solving a weighted SVD over the matched point pairs. The experiments on the P2ILF dataset have a reprojection error of 126.37 ± 48.98 pixels and a target registration error of 25.20 mm on the LLR-LUS dataset. These results indicate that our method achieves promising accuracy and efficiency in aligning preoperative models to intraoperative scenes, suggesting its potential for practical rigid alignment in near real-time laparoscopic liver AR navigation.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12673278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COVID-19 Persian Misinformation Detection on Instagram: A Comparative Analysis of Machine Learning and Deep Learning Methods","authors":"Mohammad Javad Shayegan,&nbsp;Elahe Moradi Pirbalouti,&nbsp;Mo Saraee","doi":"10.1049/htl2.70036","DOIUrl":"10.1049/htl2.70036","url":null,"abstract":"<p>The proliferation of misinformation on social media platforms like Instagram poses a significant threat, eroding public trust and potentially endangering public health. While research in this area has grown considerably, a notable gap remains in addressing misinformation in languages other than English. This study bridges that gap by establishing a comprehensive performance benchmark for detecting misinformation on Instagram, focusing specifically on Persian-language content related to COVID-19. To support this research, we constructed a novel labelled dataset of 27,000 Persian comments collected from Instagram. We employed five established machine learning and deep learning approaches for misinformation analysis: XGBoost (with TF-IDF embedding), LSTM (with GloVe embedding), CNN, KNN and BERT. To prevent overfitting, we conducted model training and validation, evaluating performance using confusion matrices. The results demonstrate that the LSTM model achieved the best classification performance, with an accuracy of 0.97, precision of 0.91, recall of 0.85 and an F1-score of 0.85. This outcome establishes the first high-performance baseline for deep learning methods on this unique Persian misinformation dataset. Furthermore, the training and validation curves for the LSTM approach indicate its effectiveness in mitigating overfitting.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12668897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145670102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented Reality in Outpatient Care: A Narrative Review","authors":"Archan Khandekar,&nbsp;Aryan Shah,&nbsp;Luis A. Esparza Miranda,&nbsp;Frida Toscano Bello,&nbsp;Bruno Liebl,&nbsp;Jonathan Ryan,&nbsp;Pedro Angelo Basei de Paula,&nbsp;Ansh Bhatia,&nbsp;Timothy Guerard,&nbsp;Dipen J. Parekh","doi":"10.1049/htl2.70025","DOIUrl":"10.1049/htl2.70025","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Augmented reality (AR) is seeing an increase in its applications in healthcare, but its reach in outpatient care remains undefined. Patients in outpatient settings face poor medical understanding. AR may help address this gap between patients and physicians through immersive and interactive models and supporting tools. This narrative review aims to evaluate the status of AR in outpatient care, categorise its applications, and identify limitations and future research needs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Four databases–PubMed, Embase, Web of Science and Cochrane Library–were conducted for peer-reviewed studies published from January 2015 to February 2025. Studies were included if they regarded AR interventions in outpatient care settings. Studies were analysed and grouped thematically into five clinical domains of AR intervention.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After review, 19 studies–spanning 987 participants–were included. AR applications were categorised into patient education and engagement (n = 3), cognitive and functional assessment (n = 3), device interaction and remote monitoring (n = 3), procedural guidance in outpatient interventions (n = 5), and rehabilitation and functional recovery support (n = 5). Most included studies were pilot studies (n = 6) and had relatively small sample sizes (median = 28). Studies proved that AR interventions consistently improved patient understanding, engagement and procedural support. Nevertheless, studies faced limitations including the need for specialised and bulky hardware–which affected patient comfort as well–reliability issues, technical difficulties and platform-specific inconsistencies.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>AR has been proved to have the potential to improve outpatient care across five main areas: patient education, cognitive and functional assessment, medical device interaction, procedural guidance and rehabilitation. Studies consistently support that AR enhances patient comprehension, engagement and procedural accuracy while allowing for remote monitoring and personalised therapy. Furthermore, AR interventions demonstrate high usability and clinical relevance. Nevertheless, limitations such as hardware complexity and inconsistent technical performance remain. Future research should prioritise large-scale RCTs and strategies to integrate AR into pre-existing digital workflows.</p>\u0000 </section>\u0000 </div>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12640284/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145597454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Augmented Reality With Dynamic Anatomy Modelling for Knee Arthroscopy","authors":"Deokgi Jeung,&nbsp;Hyun-Joo Lee,&nbsp;Hee-June Kim,&nbsp;Hyunseok Choi,&nbsp;Jaesung Hong","doi":"10.1049/htl2.70034","DOIUrl":"https://doi.org/10.1049/htl2.70034","url":null,"abstract":"<p>Research on augmented reality (AR) for knee arthroscopy has not adequately focused on knee flexion during surgery. To overcome major AR errors caused by knee movement, this study presents an association model between the finite-element models of the knee surface and bones to enable dynamic anatomy modelling. The association model allows the displacement of the knee surface elements and the reaction force of the bone elements to interact with each other. During knee flexion, the real-time shape of the knee is captured with a colour and depth camera, and the association model deforms accordingly from the extension to the flexion state. The proposed model was evaluated using computed tomography data from the knees of six participants. The results showed that the association model successfully compensates for the movement of the femur and tibia within an error margin of only 3.85 mm around the drilling area. The proposed model could therefore enable effective AR-based surgical navigation during knee surgeries.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/htl2.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145581165","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Planning Non-Linear Trajectories for No-Touch Thermal Ablation Using Passive Steerable Needle With Controlled Stiffness and PSAM","authors":"Antoine Morin,&nbsp;Juan Manuel Verde,&nbsp;Lennart Rubbert,&nbsp;Alain Garcia,&nbsp;Caroline Essert","doi":"10.1049/htl2.70033","DOIUrl":"10.1049/htl2.70033","url":null,"abstract":"<p>Percutaneous thermal ablation using a passive steering needle has the potential to help reduce procedure risks and enable multiple ablations with a single insertion. However, manual planning of such procedures is challenging, time-consuming, and typically done using CT-scans or tools designed for rigid or active steering needles. We introduce the PSAM approach which is based on an enhanced version of the particle swarm optimization with a memory component. This tool calculates optimal trajectories for no-touch thermal ablations, considering safety distances from obstacles such as bones and organs, and the amount of ablation affecting healthy tissue. Experiments were conducted on a passive steerable needle with controlled stiffness named ARC needle recently developed. The results are then compared with a semi-exhaustive Monte-Carlo approach and a manual planning by a trained surgeon using conventional rigid needles, and ranked by a second expert. The results demonstrate the interest and potential of the ARC needle and the PSAM approach to achieve the no-touch approach and avoid multiple insertions.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12629100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145565872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Annotations by Augmented Reality-Enabled Laparoscopic Surgery","authors":"Alexander Winkler,&nbsp;Christian Heiliger,&nbsp;Thomas Heiliger,&nbsp;Ulrich Eck,&nbsp;Konrad Karcz,&nbsp;Nassir Navab","doi":"10.1049/htl2.70031","DOIUrl":"10.1049/htl2.70031","url":null,"abstract":"<p>Accurate labels of surgical procedures such as image segmentations or interaction labels are paramount for many of today's medical image computing tasks. Creating a dataset with these labels requires a great deal of manual work and relies on the involvement of medical experts, which is very time-consuming and costly. We propose a pathway for the automatic generation of such labels utilizing the spatial and temporal registration between a patient, the anatomical model, tracked surgical instruments, and the surgeon's view of the patient. These requirements for the automatic generation of labels are identical to the requirements of many navigated and augmented reality (AR) enabled surgeries. The AR system, through 3D registration, has the defining ability to accurately overlay real objects with their virtual counterparts. Our approach collects the complete raw data (e.g. video, tracking data, calibrations etc.) that feeds a live laparoscopic AR system for later analysis. By converting these complete recordings of the surgery into different representations, the AR system generates valuable datasets as mere by-products. Additionally, as our approach does not rely on visual input alone but on additional 3D information, the system can create labels even if the visual input is occluded or a tool interacts with tissue outside of the view of the laparoscopic camera. In this paper, we present a realization of this concept, then evolve this foundational idea into an interactive system that assists users in annotating surgical data. Finally, we gather and analyse feedback from six participants to evaluate the efficacy and user-friendliness of our system.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12628009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145565882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Point-Guided Latent Diffusion Model for Novel View Synthesis in Laparoscopic Liver Surgery","authors":"Wenzhe Tang,&nbsp;Tao Chen,&nbsp;Yamid Espinel,&nbsp;Shahid Farid,&nbsp;Emmanuel BUC,&nbsp;Adrien Bartoli,&nbsp;Sharib Ali","doi":"10.1049/htl2.70032","DOIUrl":"10.1049/htl2.70032","url":null,"abstract":"<p>Despite recent progress in diffusion-based video synthesis, synthesizing accurate novel views from sparse input frames in laparoscopic liver surgery remains challenging due to occlusions, complex shape of anatomical structures and limited field of views. We propose point-guided latent diffusion model, specifically designed for generating high-quality intermediate frames in laparoscopic liver surgery from only the first and last video frames. Our method leverages the powerful generative capability of latent diffusion models combined with geometric cues from 3D point clouds reconstructed via dense stereo matching. To robustly handle occlusions and shape deformation, we use an adaptive camera trajectory planning strategy based on next-best-view algorithms. Furthermore, we introduce a spatial-transformer enhanced decoder to effectively preserve detailed anatomical features from reference frames and minimize visual artefacts in generated views. Extensive experiments on the clinically relevant P2ILF challenge dataset validate our method's effectiveness and superior performance in producing visually coherent and structurally accurate novel views, highlighting its ability for enhancing the quality of surgical scene reconstruction.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"12 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2025-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12627760/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145565826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}