Computer methods and programs in biomedicine最新文献

{"title":"Prey capture enhanced Harris hawks optimizer for wrapper-based feature selection in high-dimensional medical data","authors":"Mohammed Batis ,&nbsp;Yi Chen ,&nbsp;Lei Liu ,&nbsp;Ali Asghar Heidari ,&nbsp;Huiling Chen","doi":"10.1016/j.cmpb.2026.109237","DOIUrl":"10.1016/j.cmpb.2026.109237","url":null,"abstract":"<div><h3>Background and Objective</h3><div>While the Harris Hawks Optimizer (HHO) is widely utilized for wrapper-based Feature Selection (FS) due to its efficiency and ease of implementation, existing HHO-based FS approaches encounter challenges when handling high-dimensional datasets, such as falling into local optima and high computational costs. In the HHO algorithm, the Harris hawks engage in surprise attacks on the identified prey according to the prey's escape energy. However, there may be scenarios where the prey could escape due to the algorithm's limitations. To enhance the algorithm's prey-capture ability, this article introduces an enhanced HHO algorithm termed Prey Capture Harris Hawks Optimizer (PCHHO).</div></div><div><h3>Methods</h3><div>The prey capture strategy incorporates crossover and mutation operators to enhance the algorithm's exploratory-exploitative capabilities. The performance of PCHHO is evaluated on the CEC2017 benchmark suite, where it is compared to HHO, with three enhanced HHO algorithms, nine classical metaheuristic algorithms, and nine improved metaheuristic algorithms. The experimental comparison results are synthesized using the Wilcoxon signed-rank and Friedman tests. Ultimately, a binary form of PCHHO (bPCHHO) is designed for wrapper-based FS and compared with six excellent binary metaheuristics using 15 high-dimensional medical datasets.</div></div><div><h3>Results</h3><div>The results demonstrate the excellent performance of the proposed algorithm on the CEC2017 benchmark suite compared to other algorithms, as well as the effectiveness of bPCHHO in evolving a subset of features with 77% reduction in classification error, 8% reduction in computational time, and 73% fewer features selected compared to bHHO.</div></div><div><h3>Conclusions</h3><div>The proposed PCHHO and its binary variant bPCHHO exhibit superior performance in both benchmark optimization and wrapper-based FS for high-dimensional medical data, highlighting their potential for practical applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109237"},"PeriodicalIF":4.8,"publicationDate":"2026-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Liver cancer segmentator: Metadata-guided confidence scoring for reliable segmentation of colorectal liver metastases in CT","authors":"Mohammad Hamghalam ,&nbsp;Jacob J. Peoples ,&nbsp;Kaitlyn S.M. Kobayashi ,&nbsp;Grace Park ,&nbsp;Erin Kwak ,&nbsp;E. Claire Bunker ,&nbsp;Natalie Gangai ,&nbsp;Mithat Gonen ,&nbsp;Yun Shin Chun ,&nbsp;HyunSeon Christine Kang ,&nbsp;Richard K.G. Do ,&nbsp;Amber L. Simpson","doi":"10.1016/j.cmpb.2026.109233","DOIUrl":"10.1016/j.cmpb.2026.109233","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>This study introduces the liver cancer segmentator (LCS), a deep learning model designed for automatic and robust segmentation of liver parenchyma and tumors in abdominal contrast-enhanced computed tomography images from patients with colorectal liver metastases. The primary aim was to enhance confidence scoring for more reliable clinical segmentation assessment.</div></div><div><h3>Methods:</h3><div>In this retrospective study, 446 abdominal contrast-enhanced computed tomography examinations were collected; 355 (80%) were used for training and 91 for testing. Data originated from routine clinical cases at two institutions, representing diverse disease stages and treatment settings. A state-of-the-art neural network segmentation framework was trained on these cases, with performance evaluated using the Dice score and the normalized surface distance. An iterative training process, supported by an integrated annotation workflow, was employed to refine the training set. The final model was applied to the 91 test examinations to assess the impact of tumor volume and slice thickness on confidence scoring. Reliability was quantified through pairwise Dice score for failure detection and the area under the risk coverage curve.</div></div><div><h3>Results:</h3><div>The LCS achieved a Dice score of 0.9707 (95% CI: 0.9663–0.9751) for liver parenchyma and 0.7695 (95% CI: 0.7166–0.8224) for tumors. Normalized surface distance values at a 3-millimeter tolerance were 0.9605 (95% CI: 0.9539–0.9671) for parenchyma and 0.8412 (95% CI: 0.7928–0.8896) for tumors. Confidence scoring analysis demonstrated strong correlations between tumor volume, slice thickness, and segmentation reliability, reducing the area under the risk coverage curve from 16.7 to 10.3.</div></div><div><h3>Conclusions:</h3><div>The LCS achieved high segmentation accuracy in patients with colorectal liver metastases. Incorporating tumor volume and slice thickness into the confidence scoring process improved failure detection, enhanced reliability, and provided valuable insights for refining clinical deployment of automated segmentation algorithms.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109233"},"PeriodicalIF":4.8,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SegRenal: AI-Driven segmentation of frozen sections in transplant kidney biopsies — A comparative analysis of deep learning models","authors":"Ibrahim Yilmaz ,&nbsp;Heba M. Alazab ,&nbsp;Fatih Doganay ,&nbsp;Bryan Dangott ,&nbsp;Sam Albadri ,&nbsp;Aziza Nassar ,&nbsp;Fadi Salem ,&nbsp;Zeynettin Akkus","doi":"10.1016/j.cmpb.2025.109216","DOIUrl":"10.1016/j.cmpb.2025.109216","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Frozen section evaluation of donor kidney biopsies is vital for determining transplant suitability, yet remains challenging due to interobserver variability and freezing-related artifacts. While deep learning (DL) has been used for permanent sections, its application to frozen tissue is limited. We developed <em>SegRenal</em>, an artificial intelligence (AI)–based segmentation model for automated identification of glomeruli (non-sclerotic and sclerotic), arteries, and interstitial fibrosis and tubular atrophy (IFTA) in hematoxylin and eosin–stained frozen whole-slide images (WSIs). This study focuses on rigorous model adaptation, dataset development, cross-scanner performance evaluation, and integration into a clinical digital pathology workflow.</div></div><div><h3>Methods:</h3><div>A total of 183 frozen WSIs were collected from two scanners (GT450 and Grundium) and manually annotated by expert renal pathologists. Three encoder–decoder architectures (UNet, ResNet–UNet, and DenseNet–UNet) were trained on patch-level data to compare binary and multiclass segmentation strategies. The best-performing configuration — pre-trained DenseNet with multiclass output — was further evaluated on 21 unseen WSIs scanned with both platforms. Preprocessing included downsampling, patch extraction, and annotation refinement. Performance was assessed using Dice score, precision, recall, and intraclass correlation coefficient (ICC). Bland–Altman analysis and scanner variability experiments were conducted.</div></div><div><h3>Results:</h3><div>Pre-trained DenseNet multiclass segmentation yielded the best overall performance: Dice scores of 0.95 (glomeruli), 0.90 (sclerotic glomeruli), 0.80 (arteries), and 0.88 (IFTA). Recall reached 99.8% for glomeruli and 100% for arteries. Performance remained consistent across scanners. In several cases, the model detected structures initially missed by manual annotation, later confirmed by pathologists.</div></div><div><h3>Conclusions:</h3><div>SegRenal accurately segments key renal compartments in frozen biopsies and demonstrates robust cross-scanner performance. By automating tissue quantification, the model reduces variability and turnaround time, supporting fast and consistent intraoperative kidney transplant assessments.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109216"},"PeriodicalIF":4.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond a single mode: GAN ensembles for diverse medical data generation","authors":"Lorenzo Tronchin ,&nbsp;Tommy Löfstedt ,&nbsp;Paolo Soda ,&nbsp;Valerio Guarrasi","doi":"10.1016/j.cmpb.2026.109234","DOIUrl":"10.1016/j.cmpb.2026.109234","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>The advancement of generative AI in medical imaging faces the trilemma of simultaneously achieving high fidelity and diversity in synthetic data generation. Although Generative Adversarial Networks (GANs) have demonstrated significant potential, they are often hindered by limitations such as mode collapse and poor coverage of real data distributions. This study investigates the use of GAN ensembles as a solution to these challenges, with the goal of enhancing the quality and utility of synthetic medical images.</div></div><div><h3>Methods:</h3><div>We formulate a multi-objective optimisation problem to select an optimal ensemble of GANs that balances fidelity and diversity. The ensemble comprises models that contribute uniquely to the synthetic data space, ensuring minimal redundancy. A comprehensive evaluation was conducted using three distinct medical imaging datasets. We tested 22 GAN architectures, incorporating various loss functions and regularisation techniques. By sampling models at different training epochs, we crafted 110 unique configurations for ensemble selection.</div></div><div><h3>Results:</h3><div>The selected GAN ensembles demonstrated improved performance in generating synthetic medical images that closely resemble real data distributions. These ensembles preserved image fidelity while increasing diversity. In some settings, downstream models trained on synthetic data achieved slightly higher accuracy than those trained on real data alone. This effect arises because the synthetic images act as a targeted data augmentation mechanism that enhances class balance and diversity rather than replacing real data.</div></div><div><h3>Conclusions:</h3><div>GAN ensembles offer a robust solution to the fidelity–diversity–efficiency trade-off in medical image synthesis. By integrating multiple complementary models, the proposed approach improves the representativeness and utility of synthetic medical data, potentially advancing a wide range of clinical and research applications in diagnostic AI.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"277 ","pages":"Article 109234"},"PeriodicalIF":4.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145924499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ADHTransNet-based radiomics on multimodal pituitary MRI for non-invasive hormone prediction in children","authors":"Qiang Zheng ,&nbsp;Xiaolin Jiang ,&nbsp;Jianzheng Sun ,&nbsp;Limei Song ,&nbsp;Lin Zhang ,&nbsp;Jungang Liu","doi":"10.1016/j.cmpb.2026.109235","DOIUrl":"10.1016/j.cmpb.2026.109235","url":null,"abstract":"<div><h3>Background and Objective</h3><div>Growth hormone deficiency (GHD) and idiopathic central precocious puberty (ICPP) are typically diagnosed through invasive stimulation tests that require multiple blood samples collected over time. To reduce the need for such procedures, the study aims to establish an adjunctive tool by devising a fully automated pipeline for adenohypophysis segmentation and radiomics-based prediction of growth hormone (arg-pGH and ins-pGH in GHD) and gonadotropin (pLH and pLH/FSH in ICPP) levels in children.</div></div><div><h3>Methods</h3><div>A total of 274 subjects with 548 scans (T1-weighted and T2-weighted images, T1WI and T2WI) were identified, including GHD, ICPP, and normal control groups. MRI acquisition was performed 1 day prior to the hormone stimulation tests. The automated segmentation of adenohypophysis (ADH) on pituitary MRI was first achieved by the proposed ADHTransNet. Then, the radiomics features were extracted, and the consistency was assessed between manual and automated segmentations. Lastly, using a full-search feature selection strategy, we developed radiomics-based models to predict arginine-stimulated growth hormone (arg-pGH) and insulin-stimulated growth hormone (ins-pGH) levels in patients with GHD, as well as luteinizing hormone (pLH) levels and the pLH/FSH ratio in patients with ICPP.</div></div><div><h3>Results</h3><div>The superior ADH segmentation was achieved by ADHTransNet over other deep learning methods under comparison. The radiomics was validated with high measurement consistency and statistical consistency of the statistical T-values on both T1WI and T2WI images. Significant correlations were observed between truth hormone level and the predicted the peak GH of arginine stimulation test in GHD group (r=0.422, p&lt;0.001), the peak GH of insulin stimulation test in GHD group (r=0.359, p&lt;0.001), the peak luteinizing hormone (LH) in ICPP group (r=0.680, p&lt;0.001), and the ratio of peak LH to peak follicle-stimulating hormone (FSH) in ICPP group(r=0.766, p&lt;0.001).</div></div><div><h3>Conclusions</h3><div>This fully automated, multimodal, reproducible, and non-invasive pipeline shows promise in predicting GH and gonadotropin levels from MRI, reducing reliance on repeated blood tests, and enhancing assessment of hormone-related disorders.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109235"},"PeriodicalIF":4.8,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fitting high-dimensional mixture cure models using the hdcuremodels R package","authors":"Kellie J. Archer ,&nbsp;Han Fu","doi":"10.1016/j.cmpb.2025.109212","DOIUrl":"10.1016/j.cmpb.2025.109212","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Time-to-event outcomes are often of interest in biomedical studies. When the dataset includes long-term survivors or subjects who will not experience the event of interest, mixture cure models (MCMs) should be fit. Further, it is clinically relevant to identify molecular features from high-throughput assays that are associated with time-to-event outcomes, both to elucidate important pathways and to identify molecular features that may be therapeutic targets or for developing improved risk stratification systems. Herein, we describe our <strong>hdcuremodels</strong> <span>R</span> package that can be used to model right-censored time-to-event data when a cured fraction is present and the predictor space is high-dimensional.</div></div><div><h3>Methods:</h3><div>We implemented two different optimization methods, the expectation–maximization and generalized monotone incremental forward stagewise algorithms, for fitting high-dimensional penalized Weibull, exponential, and Cox mixture cure models. Cross-validation functions for each optimization method are provided that can be run with or without controlling the false discovery rate. The modeling functions are flexible in that there is no requirement for the predictors to be the same in the incidence and latency components of the model. The package also includes functions for testing mixture cure modeling assumptions, evaluating performance, and generic functions that can be used to extract meaningful results.</div></div><div><h3>Results:</h3><div>We demonstrate fitting a high-dimensional penalized mixture cure model to an acute myeloid leukemia dataset, which had strong predictive performance on an independent test set.</div></div><div><h3>Conclusion:</h3><div>Our <strong>hdcuremodels</strong> package fits penalized mixture cure models that can accommodate datasets where the number of predictors exceeds the sample size.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109212"},"PeriodicalIF":4.8,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data-driven bifurcation handling in physics-based reduced-order vascular hemodynamic models","authors":"Natalia L. Rubio ,&nbsp;Eric F. Darve ,&nbsp;Alison L. Marsden","doi":"10.1016/j.cmpb.2025.109230","DOIUrl":"10.1016/j.cmpb.2025.109230","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Three-dimensional (3D) computational fluid dynamics simulations of cardiovascular flows provide high-fidelity hemodynamic predictions to support cardiovascular medicine, but require substantial computational resources, limiting their clinical applicability. Reduced-order models (ROMs) offer computationally efficient alternatives but suffer from significant accuracy losses, particularly at vessel bifurcations where complex flow physics are inadequately captured by standard Poiseuille flow assumptions. This work presents an enhanced numerical framework that integrates machine learning-predicted bifurcation coefficients into 0D hemodynamic solvers to improve accuracy while maintaining computational efficiency.</div></div><div><h3>Methods:</h3><div>We develop a resistor–resistor–inductor (RRI) model that uses neural networks to predict pressure-flow relationships from bifurcation geometry, incorporating both linear and quadratic resistance terms along with inductive effects. The method employs physics-based non-dimensionalization to reduce training data requirements and includes flow split prediction for improved geometric characterization. We incorporate the RRI model into a zero-dimensional (0D) cardiovascular flow model using an optimization-based solution strategy. We validate the approach in isolated bifurcations and vascular trees containing up to 40 junctions across Reynolds numbers ranging from 0 to 5500, defining ROM accuracy by comparison to high-fidelity 3D finite element simulation results.</div></div><div><h3>Results:</h3><div>Results demonstrate substantial accuracy improvements: averaged across all trees and all Reynolds numbers, the RRI method reduces inlet pressure errors from 54 mmHg (45%) for standard 0D models to 25 mmHg (17%), while a simplified resistor-inductor (RI) variant achieves 31 mmHg (26%) error. The enhanced 0D models show particular effectiveness at high Reynolds numbers and in extensive vascular networks.</div></div><div><h3>Conclusions:</h3><div>This hybrid numerical approach enables accurate, real-time hemodynamic modeling suitable for clinical decision support, uncertainty quantification, and digital twin applications in cardiovascular biomedical engineering.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109230"},"PeriodicalIF":4.8,"publicationDate":"2025-12-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-assisted prognosis of multiple myeloma side population cells via SRGs and OCLR stemness index","authors":"Xufei Xiang ,&nbsp;Ruiyi Yang ,&nbsp;Jicong Li ,&nbsp;Brian Su ,&nbsp;Zefang Xu ,&nbsp;Yang An","doi":"10.1016/j.cmpb.2025.109211","DOIUrl":"10.1016/j.cmpb.2025.109211","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Relapse in Multiple Myeloma, driven by therapy-resistant cancer stem cells, necessitates the development of more specific and accurate prognostic models. Existing stemness indices often lack specificity for the unique biology of Multiple Myeloma. This study aimed to develop, validate, and optimize a novel prognostic gene signature derived from Side Population cells, a well-defined cancer stem cell-enriched subpopulation in Multiple Myeloma.</div></div><div><h3>Methods:</h3><div>A core stemness gene module was identified from Side Population cell transcriptomes (GSE109651) using Weighted Gene Co-expression Network Analysis, guided by a One-Class Logistic Regression-based stemness index. Stemness-Related Gene scores were computed from this module’s key pathways via single-sample Gene Set Enrichment Analysis. A nonlinear programming algorithm was then employed to create an optimally weighted prognostic model. The model’s performance was validated in independent cohorts (The Cancer Genome Atlas – Multiple Myeloma Research Foundation, GSE24080, GSE57317) using Cox proportional hazards modeling, and its clinical relevance was assessed via drug sensitivity (OncoPredict) and immunotherapy response (Tumor Immune Dysfunction and Exclusion) prediction.</div></div><div><h3>Results:</h3><div>The resulting Stemness-Related Gene score strongly correlated with the established mRNA stemness index (<span><math><mrow><mi>r</mi><mo>=</mo><mn>0</mn><mo>.</mo><mn>62</mn></mrow></math></span>, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>1</mn><mo>×</mo><mn>1</mn><msup><mrow><mn>0</mn></mrow><mrow><mo>−</mo><mn>82</mn></mrow></msup></mrow></math></span>). The <em>hsa05222</em> pathway was identified as the dominant prognostic component (<span><math><mrow><mi>HR</mi><mo>=</mo><mn>12</mn><mo>.</mo><mn>765</mn></mrow></math></span>, <span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>0001</mn></mrow></math></span>) and was found to specifically modulate chemoresistance. In contrast, the composite Stemness-Related Gene score better predicted immune evasion potential. The final optimally weighted model, integrating these distinct facets, demonstrated superior prognostic accuracy, consistently outperforming existing benchmarks and simpler models across all validation cohorts.</div></div><div><h3>Conclusions:</h3><div>This Side Population cell-derived, optimally weighted signature is a robust and multifaceted independent prognostic biomarker for Multiple Myeloma. By distinguishing between chemoresistance and immune evasion profiles, this framework provides a valuable tool to guide personalized, cancer stem cell-targeted therapeutic strategies.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109211"},"PeriodicalIF":4.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of blockchain-based digital twin technology in healthcare: A scoping review","authors":"You Yang,&nbsp;Mengying Liu,&nbsp;Haiying Chen,&nbsp;Li Chen","doi":"10.1016/j.cmpb.2025.109231","DOIUrl":"10.1016/j.cmpb.2025.109231","url":null,"abstract":"<div><h3>Background</h3><div>The integration of blockchain and digital twin (DT) technologies is expected to transform the healthcare sector. DTs are virtual representations of physical entities and allow for real-time monitoring of assets. Predictive analytics of equipment performance can also be supported. Data integrity, security, and trust can be strengthened by blockchain technology. However, the practical applicability and effectiveness of this combined approach in healthcare systems have not been fully established.</div></div><div><h3>Objective</h3><div>The aim of the present scoping review was to assess the practical applications and synergistic advantages of blockchain-based DT technology in healthcare, evaluate relevant implementation challenges, and provide a research agenda for future studies.</div></div><div><h3>Methods</h3><div>A scoping review was conducted. PubMed, Web of Science, Scopus, CINAHL, Embase, and OVID were searched systematically. Manual searches were also performed. Boolean operators and targeted keywords were used. Relevant studies were retrieved from database inception to May 20, 2025.</div></div><div><h3>Results</h3><div>Narrative findings were categorized into three main domains: 1) Technical foundations and core mechanisms for integrating blockchain and DT technologies were described; (2) Application scenarios of blockchain-based DT technology in healthcare were summarized; and (3) Implementation challenges and corresponding solutions for blockchain-based DT technology in healthcare were identified.</div></div><div><h3>Conclusion</h3><div>The innovative integration of blockchain and DT technologies has advanced the healthcare sector by reshaping the management, interaction, and security of medical data in the digital environment. This convergence establishes a strategic foundation for ongoing digital transformation within healthcare. Future research should prioritize the translation of these developed systems into real-world clinical applications and focus on optimizing their performance to better elucidate how emerging technologies can effectively address practical healthcare challenges.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109231"},"PeriodicalIF":4.8,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rethinking the value of dynamic and static feature planes in 4D reconstruction of deformable tissues","authors":"Ran Bu ,&nbsp;Chenwei Xu ,&nbsp;Runyi Liu,&nbsp;Yanzi Miao","doi":"10.1016/j.cmpb.2025.109232","DOIUrl":"10.1016/j.cmpb.2025.109232","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Reconstructing deformable tissues is crucial for medical image computing and robotic surgery, as it enhances the safety and efficacy of surgical procedures. However, current methods face significant challenges, including errors in tissue reconstruction at occluded regions and limitations in real-time accurate observation of complex structures.</div></div><div><h3>Methods:</h3><div>In this paper, we present a novel method called Rethink Plane (RPlane), an efficient framework based on Neural Radiance Fields (NeRF), designed to reconstruct global high-fidelity deformable tissues from binocular endoscopic videos efficiently. Our main contribution lies in rethinking the value of dynamic and static features that existing methods often overlook, and developing a Depth Uncertainty Filter. Throughout this work, the dynamic filter is an extremely important foundational component. Based on this, a Dynamic Feature Enhancement module is proposed to address the depth distortion problem caused by the occlusion of surgical instruments. Additionally, a Color Recurrent Refinement strategy is proposed to reduce dynamic blurring caused by instrument contact or tissue self-motion. We validate the effectiveness of RPlane on two datasets (ENDONERF and StereoMIS).</div></div><div><h3>Results:</h3><div>In all cases, RPlane achieves state-of-the-art (SOTA) performance in terms of tissue reconstruction quality and detail clarity (with a PSNR of 40.527 in ENDONERF and 36.267 in StereoMIS). Furthermore, RPlane demonstrates a 53.3% improvement in robustness without increasing training time or computational resources.</div></div><div><h3>Conclusions:</h3><div>RPlane addresses depth reconstruction errors caused by surgical instrument occlusion, which are common in existing algorithms. The Dynamic Feature Enhancement module is used to enhance geometric modeling in occluded areas, while the Dynamic Weight Generation &amp; Fusion and Color Recurrent Refinement strategies improve the texture details of tissues. This significant performance improvement promises to be an innovative solution for intraoperative applications.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109232"},"PeriodicalIF":4.8,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145910852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}