Computer methods and programs in biomedicine最新文献

{"title":"An In Silico Platform for 3D Ultrasound and Collateral Ligament Mechanics to Validate 3D Speckle Tracking","authors":"Lucas Milakovic , Marcus Ingram , Félix Dandois , Jan D’hooge , Lennart Scheys","doi":"10.1016/j.cmpb.2025.109213","DOIUrl":"10.1016/j.cmpb.2025.109213","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Quantifying ligament strain with three-dimensional ultrasound can improve assessment of the medial and lateral collateral ligaments and support soft-tissue balancing during knee arthroplasty. However, accurate motion and strain tracking remain challenging because these ligaments are small, anisotropic, and subject to out-of-plane motion. Limited availability of volumetric ultrasound systems and the absence of paired ground-truth strain fields further hinder systematic algorithm development. An in silico framework combining realistic biomechanics with controlled ultrasound image formation can accelerate the optimization and validation of strain-tracking techniques.</div></div><div><h3>Methods:</h3><div>We developed an in silico platform coupling finite element simulations of ligament deformation with volumetric ultrasound synthesized using the Field II simulation program. To demonstrate its utility, we designed a ligament-specific three-dimensional speckle-tracking pipeline. Displacements were estimated using normalized cross-correlation, and infinitesimal strain tensors were computed by distance-weighted least-squares fitting of local displacement gradients. Two cadaveric knee specimens were simulated under varus and valgus loading. Agreement with the finite element ground truth was assessed using the Pearson correlation coefficient, root mean square error, and Bland–Altman analysis.</div></div><div><h3>Results:</h3><div>Across the two specimens, the medial collateral ligament achieved a Pearson correlation coefficient of <span><math><mrow><mn>0</mn><mo>.</mo><mn>992</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>008</mn></mrow></math></span> with a root mean square error of <span><math><mrow><mn>0</mn><mo>.</mo><mn>276</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>323</mn></mrow></math></span> for maximal principal strain, and <span><math><mrow><mn>0</mn><mo>.</mo><mn>990</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>011</mn></mrow></math></span> with <span><math><mrow><mn>0</mn><mo>.</mo><mn>295</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>363</mn></mrow></math></span> for minimal principal strain. The lateral collateral ligament achieved <span><math><mrow><mn>0</mn><mo>.</mo><mn>988</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>006</mn></mrow></math></span> with <span><math><mrow><mn>0</mn><mo>.</mo><mn>421</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>344</mn></mrow></math></span> for maximal and <span><math><mrow><mn>0</mn><mo>.</mo><mn>942</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>040</mn></mrow></math></span> with <span><math><mrow><mn>0</mn><mo>.</mo><mn>376</mn><mo>±</mo><mn>0</mn><mo>.</mo><mn>060</mn></mrow></math></span> for minimal principal strain. Bland–Altman analysis indicated small biases, with wider limits of agreement for the lateral collateral ligament in compression.</div></div><div><h3>Conclusion:</h3><div>The proposed framework accurately quantifies ligament strains derived from three-dimensional ultrasound and provides a controll","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109213"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145877992","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":"Oblique lateral interbody fusion: role of the elastic modulus of the cage material in mechanically induced osteogenesis","authors":"Teng Lu ,&nbsp;Zhongwei Sun ,&nbsp;Xijing He","doi":"10.1016/j.cmpb.2026.109242","DOIUrl":"10.1016/j.cmpb.2026.109242","url":null,"abstract":"<div><h3>Background and objective</h3><div>The elastic modulus of cage material (cage-E) is a key determinant of fusion outcomes in oblique lateral interbody fusion (OLIF), as it modulates the efficiency of mechanically induced osteogenesis (EMIO). Here, we establish a logarithmic predictive model linking cage-E to EMIO and delineate the underlying biomechanical mechanisms via computational biomechanical analysis.</div></div><div><h3>Methods</h3><div>A customized mechano-regulation algorithm was applied to finite element models of the L4/5 OLIF construct to simulate the iteration of tissue differentiation and regeneration, which was driven by mechanical stimulation (MechSt). The regenerative bone fraction at the final iteration was defined as EMIO. A total of 23 cage-E values ranging from 0.1 GPa to 110 GPa were evaluated.</div></div><div><h3>Results</h3><div>As cage-E increased from 0.1 GPa to 110 GPa, the OLIF construct stiffness increased from 3.29 to 6.02 N/mm to 4.95–6.13 N/mm across iterations; the stress-shielding MechSt region expanded from 0 to 0.92% to 9.75–53.67%, whereas the stress-growth MechSt region contracted from 100 to 99.08% to 90.25–46.33%. Correspondingly, EMIO declined from 92.05% to 55.44%. Logarithmic regression revealed strong correlations (R²=0.72–0.89) between cage-E and construct stiffness, MechSt distribution, and tissue regeneration.</div></div><div><h3>Conclusions</h3><div>Reduced cage-E enhances OLIF EMIO via a defined cascade biomechanical mechanism: cage-E logarithmically regulates construct stiffness, with lower cage-E mitigating stress shielding and preserving the osteogenic MechSt domain, in turn promoting osteoblastic differentiation of mesenchymal stem cells and bone regeneration. The established logarithmic model characterizes the cage-E–EMIO relationship and serves as a potential tool for cage-E screening to optimize OLIF fusion outcomes.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109242"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145921989","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":"Biomarker discovery study design consistent with the receiver-operator characteristic","authors":"Joakim Ekström,&nbsp;Ivaylo Stoimenov,&nbsp;Jim Åkerrén Ögren,&nbsp;Tobias Sjöblom","doi":"10.1016/j.cmpb.2025.109215","DOIUrl":"10.1016/j.cmpb.2025.109215","url":null,"abstract":"<div><h3>Background and Objective</h3><div>The field of early biomarker discovery is characterized by a lack of consensus on the choice of statistical methodology, which may impede later progress towards clinically useful biomarkers. The Receiver-Operator Characteristic (ROC) is a <em>de facto</em> standard for determining the performance of In Vitro Diagnostic (IVD) devices. In this study, we aimed to systematically identify and mitigate prevalent pitfalls in biomarker discovery efforts and propose a best-practice guideline based on a ROC analysis framework.</div></div><div><h3>Methods</h3><div>By maintaining a careful alignment to the study objectives through a sample procurement plan, study size determination and data analysis by the ROC framework, we formulated a biomarker discovery protocol. We performed Monte Carlo simulations to inform the investigator on the suitable number of study participants, the statistical power and sample bin allocation strategy. The main concept is illustrated using proteomic data of newly diagnosed cancer cases and concurrent external controls.</div></div><div><h3>Results</h3><div>The work demonstrates a regulatory-adherent pipeline to achieve an effect superior to the current best biomarker used as a predicate medical device. In our proof-of-concept ROC-based analysis in samples from a publicly available dataset, we detected statistically significant composite biomarkers, of which we validated a subset in an independent dataset acquired using the same proteomic analysis method. Intriguingly, commonly used feature selection methods do not identify the same composite biomarkers from the same data, and their selections show limited overlap with the ROC-based analysis.</div></div><div><h3>Conclusion</h3><div>The proposed approach can facilitate translation of scientific discoveries into regulatory approved biomarker tests fit for use in clinical medicine.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109215"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145827149","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":"MSDRN: Multi-scale deep residual network for fluorescence molecular tomography","authors":"Xin Zhao ,&nbsp;Liuyuan Zhang ,&nbsp;Chunyu Qiu ,&nbsp;Heng Zhang ,&nbsp;Xiaowei He ,&nbsp;Xin Leng ,&nbsp;Xuelei He ,&nbsp;Hongbo Guo","doi":"10.1016/j.cmpb.2025.109217","DOIUrl":"10.1016/j.cmpb.2025.109217","url":null,"abstract":"<div><h3>Background and Objective:</h3><div>Fluorescence molecular tomography (FMT) is a promising imaging technique that can quantify the internal distribution of tumor in the early stage. However, due to the ill-posed inverse problem caused by the severe photon scattering effect, the promotion of efficiency and accuracy is still an issue for FMT and the reconstruction of the morphological performance is still difficult to meet the practical requirement.</div></div><div><h3>Methods:</h3><div>In this paper, FMT was employed and a deep-learning-based Multi-Scale Deep Residual Network (MSDRN) is proposed to enhance the reconstruction resolution. During reconstruction, MSDRN maps the measured boundary data into multi-channel feature representations and leverages cascaded residual blocks to deepen the network architecture, thereby enabling comprehensive feature extraction and high-resolution recovery. Specifically, a dual-branch dilated-convolution structure is adopted to enlarge the receptive field, alleviating resolution degradation in multi-source scenarios. A spatial-attention mechanism is further introduced to emphasize the structural similarity of fluorophore distributions. Moreover, an enhanced residual module is designed to accelerate convergence and suppress gradient vanishing. Consequently, MSDRN achieves accurate and high-resolution fluorescent source reconstruction.</div></div><div><h3>Results:</h3><div>To evaluate the performance of the proposed MSDRN, comprehensive numerical simulations and <em>in-vivo</em> experiments were conducted. The effectiveness of the proposed method is verified in simulation and <em>in-vivo</em> experiments. The results show that the reconstruction accuracy of the proposed method is significantly improved compared with the existing methods, in which Location Error (LE) is reduced by <span><math><mrow><mn>0</mn><mo>.</mo><mn>45</mn><mspace></mspace><mi>mm</mi></mrow></math></span> and Dice Similarity Coefficient (Dice) is increased by 42%. The results demonstrate that MSDRN consistently surpasses state-of-the-art approaches in morphological fidelity, localization accuracy, multi-source resolution, and practical <em>in-vivo</em> applicability.</div></div><div><h3>Conclusion:</h3><div>The proposed MSDRN exhibits superior capabilities in both localizing and recovering the morphological characteristics of fluorescent sources, thereby holding significant potential for advancing the pre-clinical and clinical translation of FMT in early-stage tumor detection.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109217"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145818381","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":"2026-03-01","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":"The role of plaque morphology and composition in vulnerability assessment: Computational analysis using CT images and elastography","authors":"Nicoletta Curcio ,&nbsp;Giulia Matrone ,&nbsp;Michele Conti ,&nbsp;Giovanni Nano ,&nbsp;Paolo Righini ,&nbsp;Vlasta Bari ,&nbsp;Daniela Mazzaccaro","doi":"10.1016/j.cmpb.2025.109229","DOIUrl":"10.1016/j.cmpb.2025.109229","url":null,"abstract":"<div><h3>Objective</h3><div>This study seeks to assess the influence of using patient-specific data from different imaging methods on evaluating carotid plaque vulnerability via finite element analysis (FEA) instead of using data derived from the literature.</div></div><div><h3>Methods</h3><div>54 patients were considered in this analysis, who preoperatively underwent computed tomography angiography (CTA) and ultrasound (US) imaging evaluations. The composition (i.e. calcific, lipidic and mixed) and vulnerability (i.e. stable or vulnerable) of their plaques were evaluated by macroscopic and histologic assessment post-endarterectomy. In particular, the plaques of these 54 patients were classified as mixed. 3D reconstructions of the carotid artery were generated from CTA scans, and computational analyses were performed using two different simulation settings for material properties and loads: a) the material properties of the plaque components were set as an average of values available in the literature (LIT-based); b) the material property of the plaque fibrous content was modified using stiffness data derived from US shear-wave elastography imaging (SWE-based). Statistical analyses were conducted to compare stress parameters obtained from the different simulations within groups of vulnerable and stable plaques.</div></div><div><h3>Results</h3><div>Comparisons between LIT-based and SWE-based FEA revealed notable differences in stress parameters associated with plaque vulnerability. In particular, the stress values derived from SWE-based simulations provided distinct stratification of vulnerable versus stable plaques, whereas LIT-based models showed limited differentiation. Significant variations in von Mises (<em>p</em> = 0.015, <em>p</em> = 0.037) and maximum principal stress (<em>p</em> = 0.014) distributions were observed in SWE-based FEA.</div></div><div><h3>Conclusions</h3><div>Patient-specific modelling and computational analysis integrating CTA-derived morphological with US-derived biomechanical data could improve the assessment of plaque vulnerability in mixed-composition carotid plaques.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109229"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145881251","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":"2026-03-01","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":"Automatic construction of interconnected cable models of cardiac propagation on a surface","authors":"Elham Zakeri Zafarghandi,&nbsp;Vincent Jacquemet","doi":"10.1016/j.cmpb.2025.109228","DOIUrl":"10.1016/j.cmpb.2025.109228","url":null,"abstract":"<div><h3>Background and objective:</h3><div>Cardiac fibers may be represented by a network of interconnected cables for simulating electrical propagation. The lack of automatic cable mesh generation tool has hampered this modeling approach. We aim to provide and evaluate an algorithmic solution to this problem.</div></div><div><h3>Methods:</h3><div>We developed an open-source C++/Python package for the construction of a monolayer interconnected cable model from a triangulated surface with fiber orientation, targeting a given longitudinal and transverse space step. The workflow of the algorithm starts with the generation of evenly spaced streamlines aligned with fiber orientation. Another set of streamlines, orthogonal to the fibers, is used to specify lateral connections. The intersection between the two sets of streamlines gives the vertices of the cable mesh, determines its connectivity, and defines a polygonal tessellation of the surface that can be triangulated. Finite differences can then be applied to solve a reaction–diffusion equation on the cable mesh.</div></div><div><h3>Results:</h3><div>The approach was validated in increasingly complex configurations and up to near-cellular resolutions (20 to <span><math><mrow><mn>200</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>). Fiber orientation noise, singularities and abrupt changes in orientation reduced the local coupling by altering the microstructure of the tissue. The pipeline for mesh generation was tested using a publicly available cohort of 98 patient-specific geometries. The stability limit of the numerical scheme was assessed by spectral analysis of the diffusion matrix and was compared to triangular meshes and cartesian grids.</div></div><div><h3>Conclusion:</h3><div>This physiologically based mesh generation tool may be used as a building block for the construction of multilayer three-dimensional models of the atria for the simulation of discrete propagation.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"276 ","pages":"Article 109228"},"PeriodicalIF":4.8,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145838858","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":"INTELLI-PVA: Informative sample annotation-based contrastive active learning for cross-domain patient-ventilator asynchrony detection","authors":"Lingwei Zhang ,&nbsp;Xue Feng ,&nbsp;Fei Lu ,&nbsp;Zepeng Ding ,&nbsp;Jiayi Yang ,&nbsp;Luping Fang ,&nbsp;Gangmin Ning ,&nbsp;Shuohui Yuan ,&nbsp;Huiqing Ge ,&nbsp;Qing Pan","doi":"10.1016/j.cmpb.2025.109203","DOIUrl":"10.1016/j.cmpb.2025.109203","url":null,"abstract":"<div><h3>Background and objective</h3><div>Patient-ventilator asynchrony (PVA) is prevalent in mechanically ventilated patients and adversely impacts clinical outcomes, but its real-time detection remains challenging. While artificial intelligence (AI) systems show promise for PVA detection, their cross-domain generalization faces two major limitations: variability in patient-ventilator interactions across different clinical settings, and morphological overlap between PVA types. These challenges necessitate specialized AI solutions rather than conventional re-annotation approaches.</div></div><div><h3>Methods</h3><div>We present the INTELLI-PVA framework for efficient cross-domain PVA detection on eight types. First, a hybrid two-stage PVA classifier was developed. A deep learning model, pre-trained on unannotated data using contrastive learning and fine-tuned using annotated data, identified four morphologically defined compound PVA types, each encompassing a reverse triggering (RT) and a non-RT type. A subsequent rule-based algorithm differentiated the subtypes within each compound type according to their triggering signatures. Then, the model was adapted to the target domain through an iterative active learning cycle, which selected the most informative samples for expert annotation and used them to fine-tune the model.</div></div><div><h3>Results</h3><div>Established and validated on data from two centers encompassing 1190 patients and 124.975 million respiratory cycles, INTELLI-PVA demonstrates superior detection performance (average F1-score: 0.849) in classifying the eight PVA classes using only 1000 annotated samples per target domain, and achieves respiratory therapist-level recognition ability (average Cohen's κ=0.850) across unseen ventilator configurations and patient demographics.</div></div><div><h3>Conclusions</h3><div>INTELLI-PVA achieves high-accuracy, cross-domain PVA detection with minimal annotation burden, establishing a practical and efficient pathway for deploying AI-assisted ventilation monitoring in diverse clinical settings.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"275 ","pages":"Article 109203"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145734016","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":"CoroSAM: adaptation of the Segment Anything Model for interactive segmentation in Coronary angiograms","authors":"Michela Ferrari ,&nbsp;Mario Urtis ,&nbsp;Edoardo Spairani ,&nbsp;Antonio Tescari ,&nbsp;Francesca Sessa ,&nbsp;Maurizia Grasso ,&nbsp;Francesco Prati ,&nbsp;Eloisa Arbustini ,&nbsp;Giovanni Magenes","doi":"10.1016/j.cmpb.2025.109172","DOIUrl":"10.1016/j.cmpb.2025.109172","url":null,"abstract":"<div><h3>Background and objectives</h3><div>X-ray Coronary Angiography (XCA) enables visualization of coronary arteries for disease and morphology assessment. Accurate segmentation of major coronary vessels is crucial for automated analysis of their geometric features but presents challenges due to anatomical complexity. This study introduces CoroSAM, an adaptation of the LiteMedSAM Foundation Model, employing parameter-efficient fine-tuning for interactive coronary artery segmentation in XCA images.</div></div><div><h3>Methods</h3><div>The proposed approach incorporates Convolutional Adapter layers within the image encoder's TinyViT blocks to enhance domain-specific feature extraction while maintaining computational efficiency. A point-based prompting strategy directly encodes vessel endpoints and branch points as additional input channels. Model evaluation employed 5-fold cross-validation on the ARCADE dataset and zero-shot testing on external datasets (XCAD, DCA1). Performance was compared with state-of-the-art models for user-guided segmentation.</div></div><div><h3>Results</h3><div>CoroSAM demonstrated superior performance on the ARCADE test set (Dice=0.87, Precision=0.86, Recall=0.89) while requiring fewer trainable parameters compared to competitive models. Statistical analysis confirmed significant improvements over alternative Adapter configurations. Zero-shot generalization yielded competitive performance on external datasets (XCAD: Dice=0.82; DCA1: Dice=0.73), demonstrating robust transferability across different image qualities.</div></div><div><h3>Conclusions</h3><div>Integrating specialized Convolutional Adapters and channel-encoded point prompts enables accurate delineation of major coronary vessels with minimal user intervention. CoroSAM's architecture facilitates efficient inference on standard computing hardware without dedicated GPUs, providing a practical tool for clinical applications. This approach establishes an adaptation framework that effectively balances segmentation accuracy with computational efficiency, making it suitable for routine analysis workflows.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"274 ","pages":"Article 109172"},"PeriodicalIF":4.8,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145602812","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}