Biomedical Engineering and Computational Biology最新文献

{"title":"Accuracy and Functional Performance of Artificial Intelligence-Based Automated Crown Design Systems: A Systematic Review and Meta-Analysis.","authors":"Ahmed A Holiel, Mounir M Al Nakouzi, Carlos Enrique Cuevas-Suárez, Abigailt Flores-Ledesma, Sofia Drouri, Rim Bourgi","doi":"10.1177/11795972261463709","DOIUrl":"10.1177/11795972261463709","url":null,"abstract":"<p><strong>Objectives: </strong>Artificial intelligence (AI)-driven automated crown design is rapidly transforming digital restorative dentistry by enabling anatomically precise and functionally integrated crowns. This systematic review and meta-analysis critically evaluate whether AI-assisted crown design systems, including machine learning (ML), deep learning (DL), generative adversarial networks (GANs), and diffusion models, produce restorations with comparable or superior morphological accuracy, occlusal integration, internal fit, and workflow efficiency relative to computer-aided design (CAD) or technician-driven workflows.</p><p><strong>Methods: </strong>A comprehensive search of MEDLINE (PubMed), Scopus, Web of Science, Embase, and Cochrane Library was conducted for studies published through 17 March 2026. Eligible studies included in vitro, in silico, and clinical investigations comparing AI-based crown design systems with conventional workflows. Primary outcomes were morphological accuracy root-mean-square (RMS) deviation, cusp morphology, volumetric/linear deviation, occlusal contact fidelity, and internal fit; secondary outcomes included marginal adaptation and restoration design time. Risk of bias was assessed using validated tools, and meta-analyses were conducted using random-effects models with standardized mean differences (SMDs).</p><p><strong>Results: </strong>Seventeen studies met the inclusion criteria, of which 13 were included in the quantitative synthesis. AI-based systems achieved clinically acceptable morphological accuracy, internal fit, and occlusal contact reproduction (RMS deviation: SMD = -0.15, 95% CI -1.04 to 0.74). Workflow efficiency improved significantly, with reductions in design time of 25-50% and enhanced precision in chamfer and marginal gaps (p < 0.001). DL and GAN-based platforms consistently produced crowns within clinically acceptable deviation ranges (<100-200 μm). Integration of patient-specific occlusal and mandibular dynamics further enhanced functional occlusal prediction. Expert technician refinement remained beneficial in anatomically complex cases.</p><p><strong>Conclusions: </strong>AI-assisted crown design demonstrates promising potential for providing reproducible, morphologically accurate, and functionally integrated restorations while potentially enhancing workflow efficiency. This review underscores the potential of AI systems to standardize restorative outcomes and reduce operator dependency, while combined human-AI workflows may enhance performance in complex cases. However, the current evidence is derived predominantly from in vitro and computational studies, with limited prospective clinical validation, limited integration of patient-specific dynamic occlusal data, and insufficient long-term follow-up evidence. Therefore, the findings should be interpreted cautiously and not considered definitive evidence of clinical superiority over conventional workflows. Standardized clinical protoc","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261463709"},"PeriodicalIF":2.9,"publicationDate":"2026-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13305636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346709","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":"Fractional Soliton Dynamics in Coupled Myelinated Fibers: Comparative Modeling With Beta, Caputo, and Atangana-Baleanu Derivatives.","authors":"Wulfran Fendzi Mbasso, Ambe Harrison, Monia Ferchichi, Muhammad Suhail Shaikh, Zokir Mamadiyarov, Saad F Al-Gahtani, Z M S Elbarbary","doi":"10.1177/11795972261462334","DOIUrl":"10.1177/11795972261462334","url":null,"abstract":"<p><strong>Background: </strong>fractional-order modeling provides a powerful framework for representing memory-dependent conduction in excitable biological media. However, existing soliton-based models of myelinated nerve fibers are often theoretical, operator-specific, and insufficiently benchmarked in terms of numerical reproducibility, physiological plausibility, and computational cost.</p><p><strong>Objectives: </strong>This study aims to compare the Liouville-Caputo, Atangana-Baleanu, and Beta fractional operators for modeling soliton-like action-potential propagation in ephaptically coupled myelinated nerve fibers, with emphasis on waveform stability, energy retention, biological consistency, computational efficiency, and adaptive parameter learning.</p><p><strong>Design: </strong>A comparative computational modeling study was conducted using a coupled fractional nonlinear partial differential equation framework, physiological parameter mapping, numerical sensitivity analysis, and physics-informed neural network-based parameter estimation.</p><p><strong>Methods: </strong>A coupled fractional Korteweg-de Vries-type system was solved under identical initial and boundary conditions for the three fractional operators. The time-fractional order α was varied over [0.6, 1.0], while the space-fractional order β was varied over [1.5, 2.0]. Simulations used a uniform spatial grid, fixed time step, localized sech² initial pulse, and Neumann boundary conditions. The operators were compared using soliton-like velocity, amplitude, pulse width, normalized energy retention, residual error, RMSE, MAE, and CPU runtime. A physics-informed neural network was further used to estimate model parameters while enforcing the fractional PDE residual.</p><p><strong>Results: </strong>The Beta derivative produced the most localized and stable soliton-like pulses, with stronger amplitude preservation, lower energy loss, and shorter runtime than the Liouville-Caputo and Atangana-Baleanu formulations under the tested settings. Increasing ephaptic coupling strength reduced pulse amplitude, whereas increasing α improved propagation velocity and increasing β enhanced waveform localization. Quantitative residual and error analyses confirmed that the Beta-based formulation maintained low numerical error while preserving biologically plausible conduction behavior.</p><p><strong>Conclusion: </strong>The results support the Beta derivative as a biologically plausible and computationally efficient approximation for soliton-like nerve-pulse propagation in coupled myelinated fibers. The Liouville-Caputo and Atangana-Baleanu operators remain valuable for long-memory and fading-memory regimes, respectively. Future work should integrate literature-constrained biological consistency assessment, stochastic ion-channel dynamics, and heterogeneous multidimensional nerve-bundle geometries.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261462334"},"PeriodicalIF":2.9,"publicationDate":"2026-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13305414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148346660","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":"Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment.","authors":"Wulfran Fendzi Mbasso, Ambe Harrison, Zokir Mamadiyarov, Zhe Liu, Raman Kumar, Muhammad Suhail Shaikh, Mohamed Metwally Mahmoud","doi":"10.1177/11795972261453621","DOIUrl":"10.1177/11795972261453621","url":null,"abstract":"<p><p>In medical image analysis, accurate skin lesion categorization is still a major difficulty particularly under limited data conditions and computational complexity. For automated skin cancer detection, in this work we present a scalable hybrid model combining a Convolutional Neural Network (CNN), the Harmonic Mean Optimizer (HMO), and a Support Vector Machine (SVM) classifier-termed HMO-CNN-SVM. Key CNN hyperparameters including learning rate, batch size, and kernel configuration are optimized using the HMO, so greatly boosting classification performance over manual or stationary settings. The model further uses SVM on CNN feature embeddings modified on HMO to improve decision boundary sharpness. Robust performance is shown by experiments carried out on the ACS skin lesion dataset validated by 5-fold cross-valuation and ISIC 2018 benchmarks with an accuracy of 95.02% and consistent generalizing over folds. Crucially, significant parallelism potential made possible by the population-based structure of HMO makes the framework fit for GPU clusters or cloud-based training pipelines. Computational benchmarks expose reasonable overhead in trade for best performance. Thus, the suggested system is a strong contender for implementation in high-performance and distributed computing contexts since it provides both diagnostic dependability and computational tractability.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261453621"},"PeriodicalIF":2.9,"publicationDate":"2026-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242590/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148206636","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":"Mechanistic Elucidation of Liujun Jiaoxian Tang in Management of Sepsis Through Metabolomics and Network Pharmacology.","authors":"Muzi Peng, Runjun Sun, Wenjuan Quan, Biao Deng, Zhenlong Li","doi":"10.1177/11795972261455351","DOIUrl":"10.1177/11795972261455351","url":null,"abstract":"<p><strong>Objective: </strong>This study was aimed to investigate the blood composition and potential mechanism of Liujun Jiaoxian Tang (LJJXT) for treating sepsis.</p><p><strong>Methods: </strong>After drug intervention in rats, the main active components in LJJXT liquid and serum were identified by UPLC-QE-MS analysis. The effective components and their targets of LJJXT were further screened through the TCMSP database; the disease-related action targets were retrieved by using the Disgenet and Genecards databases. The intersection of the two sets of targets was taken to construct the \"LJJXT-components-targets-diseases\" network, PPI diagram, GO and KEGG enrichment analysis diagram. Subsequently, molecular docking studies were conducted on the key targets for treating diseases screened by PPI and the corresponding effective components in LJJXT.</p><p><strong>Results: </strong>There were 2159 active ingredients in LJJXT, of which 90 were effective in blood. The 20 screened active ingredients matched 139 targets. There were a total of 2585 disease-related targets, and 76 targets shared by drugs and diseases. There were 2113 biological processes, 43 cell components, 211 molecular functions in GO analysis and 172 pathways obtained by KEGG analysis. The results showed that LJJXT may act on AKT1, TNF, PTGS2 and other targets through the active ingredients in blood such as terpenoids, flavonoids, phenols and alkaloids. It was involved in the regulation of lipid and atherosclerosis, toxoplasmosis, and other signaling pathways to play anti-inflammatory, immune enhancement, reduce oxidative stress and other effects, so as to exert drug efficacy and alleviate sepsis. Molecular docking results showed that kaempferol and vitamin A had high affinity with key therapeutic targets involved in lipid and atherosclerotic signaling pathways, and the combination of kaempferol and JUN was the best.</p><p><strong>Conclusions: </strong>This study revealed the effective ingredients and potential mechanisms of LJJXT for treating sepsis, providing sufficient theoretical basis for its clinical treatment of sepsis and subsequent basic research.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261455351"},"PeriodicalIF":2.9,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148044165","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":"Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia.","authors":"Md Nahid Hasan, Md Asaduzzaman Shishir, Kazi Md Mostafizur Rahman, Sm Bakhtiar Ul Islam, Manik Chandra Shill, Nayeema Bulbul, Ashrafus Safa, Jinath Sultana Jime, Md Fakruddin","doi":"10.1177/11795972261441396","DOIUrl":"10.1177/11795972261441396","url":null,"abstract":"<p><p>Anemia remains a critical global health burden, often driven by impaired erythropoietin (EPO) signaling, which reduces red blood cell production. While recombinant EPO therapy is effective, its high cost and associated safety concerns limit its accessibility. This study explores microbial metabolites as affordable and safe alternatives that act as EPO mimetics that can bind and activate the erythropoietin receptor (EPOR). A computational screening of 90 microbial bioactive compounds was conducted, and from those, 16 were selected for detailed analysis. The extracellular domain of EPOR (PDB: 1EBP) was used as the target protein. Molecular docking was performed using AutoDock, followed by ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profiling with SwissADME and ProTox-III. Protein-protein interaction (PPI) networks were also analyzed in Cytoscape, and the stability of the top complexes was validated via 100 ns molecular dynamics (MD) simulations. Docking results identified Abyssomicin W, Abyssomicin C, and Camptothecin as the top candidates with strong binding affinities (-7.60 kcal/mol) to EPOR. ADMET predictions confirmed their favorable drug-likeness and safety profiles, with Abyssomicin W exhibiting the most promising characteristics, including high gastrointestinal absorption, and no predicted hepatotoxicity or carcinogenicity. PPI network analysis underscored the functional relevance of EPOR in erythropoietic pathways, while molecular dynamics (MD) simulations revealed that Abyssomicin W and Camptothecin formed highly stable complexes with the receptor, whereas the Abyssomicin C complex was unstable. The integrated computational pipeline successfully identified Abyssomicin W as the most stable and promising EPO mimetic candidate. In conclusion, this study identifies Abyssomicin W as a potential and stable EPO mimetic candidate, highlighting the potential of microbial metabolites as cost-effective therapeutics for anemia. Further experimental validation, including direct binding and functional cell-based assays is recommended to confirm its efficacy and safety in biological systems.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261441396"},"PeriodicalIF":2.9,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13201932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148044153","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":"Evaluation of Mindful Breathing Method Using Tablet Devices Through Chaos Analysis and Frequency Analysis: A Pilot Randomized Controlled Trial.","authors":"Eiichi Togo","doi":"10.1177/11795972261452228","DOIUrl":"10.1177/11795972261452228","url":null,"abstract":"<p><strong>Introduction: </strong>University students face various stresses, including academic and career anxieties and a lack of interpersonal relationships. These stresses can elevate psychological burdens, negatively affecting their studies and daily lives.</p><p><strong>Objective: </strong>This pilot study aims to quantitatively evaluate the effects of mindful breathing exercises using tablet devices on autonomic nervous system activity in university students by analysis of finger plethysmogram (pulse wave amplitude values) and chaos analysis (Lyapunov exponent and fractal dimension).</p><p><strong>Methods: </strong>In this parallel-group randomized controlled trial, 18 nursing students (Mindful Breathing Group [Mi group], n = 9; control group [nMi group], n = 9) were randomly assigned. On the first day, the Mi group performed mindful breathing, the nMi group performed cross fixation, and finger plethysmogram<i>s</i> were measured. For the next 9 days, the Mi group performed mindful breathing at home before bedtime, while the nMi group performed cross gazing, and finger plethysmograms were measured on days 1 and 9. Data were analyzed using one-way analysis of variance and <i>t</i>-tests.</p><p><strong>Results: </strong>The Mi group showed a significant increase in pulse wave amplitude values over time (<i>P</i> = .001), whereas the nMi group showed a decrease (<i>P</i> = .001). Chaos analysis revealed no statistically significant differences between groups in the fractal dimension or Lyapunov exponent. Although descriptive differences were observed, these did not reach statistical significance. Both groups demonstrated positive Lyapunov exponents, suggesting nonlinear characteristics of the pulse wave signals.</p><p><strong>Conclusions: </strong>Mindful breathing using tablet devices may be associated with changes in pulse wave amplitude in university students, which could reflect alterations in peripheral autonomic activity under the present experimental conditions. However, no statistically significant differences were observed in chaos analysis indices. Further research with larger samples and additional physiological measures is required to clarify the relationship between mindful breathing and nonlinear autonomic dynamics.</p><p><strong>Trial registration: </strong>UMIN Clinical Trials Registry (UMIN000056166; Registered November 15, 2024).</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261452228"},"PeriodicalIF":2.9,"publicationDate":"2026-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13198649/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148017993","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":"Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI.","authors":"Farzaneh Keyvanfard, Abbas Nasiraei-Moghaddam","doi":"10.1177/11795972251404254","DOIUrl":"10.1177/11795972251404254","url":null,"abstract":"<p><strong>Introduction: </strong>Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to examine functional connectivity (FC) alterations in neurological disorders such as Alzheimer's disease (AD). Traditional studies either employ whole-brain analyses or focus on specific regions, yet the vast number of FCs and their interrelations complicate interpretation. This study adopts a data-driven, hypothesis-free approach to detect altered functional subnetworks in AD.</p><p><strong>Methods: </strong>Independent component analysis (ICA) was applied to FC matrices from 34 AD patients and 49 healthy controls (HCs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After pruning, significant subnetworks distinguishing AD from HC were identified. Graph theoretical parameters were computed for each subnetwork, and their associations with Mini-Mental State Examination (MMSE) scores were assessed.</p><p><strong>Results: </strong>Three subnetworks effectively differentiated AD patients from HCs. One subnetwork showed significant group differences in network strength, clustering coefficient, and local efficiency, despite no whole-brain differences. Abnormal functional lateralization also emerged within subnetworks. Moreover, FC weights in the identified subnetworks positively correlated with MMSE scores, linking cognitive performance to subnetwork connectivity.</p><p><strong>Conclusion: </strong>These results demonstrate the utility of a data-driven approach in detecting AD-specific altered subnetworks. By providing a modular perspective, this method facilitates targeted examination of connectivity changes, improves interpretability, and deepens understanding of functional disruptions in AD.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972251404254"},"PeriodicalIF":2.9,"publicationDate":"2026-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13180186/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976270","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":"Leveraging Clinical Data for Early Heart Disease Prediction: A Machine Learning Approach With Interpretability Analysis.","authors":"Emma Qumsiyeh, Qassam Al-Wirdian, Nur Sebnem Ersoz","doi":"10.1177/11795972261446822","DOIUrl":"10.1177/11795972261446822","url":null,"abstract":"<p><strong>Background: </strong>Heart disease remains one of the leading causes of mortality worldwide, highlighting the need for early and accurate diagnosis to support effective prevention and treatment strategies.</p><p><strong>Methods: </strong>This study presents a machine-learning-based approach for predicting heart disease using clinical and demographic data from a publicly available dataset. Four widely used classification algorithms-Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), and Decision Trees-were evaluated to identify the most effective predictive model. The dataset underwent comprehensive preprocessing, including handling missing values, categorical encoding, and feature normalization, to enhance data quality and model robustness. Model performance was assessed using accuracy, precision, recall, and AUC-ROC metrics.</p><p><strong>Results: </strong>Findings show that hyperparameter-optimized models, particularly Random Forest and KNN, demonstrated strong predictive performance. Explainability techniques, specifically SHapley Additive exPlanations (SHAP), were incorporated to improve interpretability, transparency, and clinical trust. SHAP values were used to analyze feature importance and provide explanations for individual predictions.</p><p><strong>Conclusion: </strong>The results underscore the potential of interpretable machine-learning models as valuable tools for early diagnosis, risk stratification, and clinical decision support. Future research should employ larger datasets and investigate real-time predictive applications further to enhance the generalizability and clinical utility of these models.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261446822"},"PeriodicalIF":2.9,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13180069/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976217","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":"Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.","authors":"Alejandro Jerónimo, Ignacio Rojas, Olga Valenzuela","doi":"10.1177/11795972261431934","DOIUrl":"10.1177/11795972261431934","url":null,"abstract":"<p><strong>Background: </strong>Deep learning has transformed medical imaging by enabling earlier and more accurate disease diagnosis. Lesion and tumor segmentation, essential for analyzing and tracking morphological changes, is commonly done with U-Net variants, though these often lack cross-domain generalization and do not fully leverage foundation models like the Segment Anything Model (SAM), which still requires manual intervention to define the region of interest (ROI).</p><p><strong>Objectives: </strong>To enhance generalization and reduce manual intervention by combining the automatic optimization of nnU-Net with the precision of SAM.</p><p><strong>Design: </strong>Experimental evaluation of a hybrid segmentation framework for lung nodule analysis.</p><p><strong>Methods: </strong>We propose a novel approach integrating the automatic optimization capabilities of nnU-Net for lesion detection with the high-precision segmentation of SAM, eliminating the need for manual intervention by the clinician. The method was evaluated on the LIDC-IDRI dataset, a widely recognized benchmark for lung nodule segmentation.</p><p><strong>Results: </strong>Our approach produces more anatomically coherent segmentations than nnU-Net alone. In many cases, the resulting boundaries more closely reflect true nodule morphology than individual expert annotations, despite high inter-expert variability.</p><p><strong>Conclusion: </strong>The proposed integration of nnU-Net with SAM enables fully automated lesion segmentation without manual intervention. The method improves generalization and accuracy across medical imaging domains, achieving expert-level performance in pulmonary nodule segmentation.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261431934"},"PeriodicalIF":2.9,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13157531/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147876005","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":"Entropy as a Tool for Quantifying Biomedical Signals: Gaps and Opportunities in Clinical Methodological Approaches-A Scoping Review.","authors":"Luis Gabriel Gómez Acosta, Max Chacón Pacheco","doi":"10.1177/11795972261438666","DOIUrl":"10.1177/11795972261438666","url":null,"abstract":"<p><p>In biomedical and engineering research, entropy metrics have become well-established tools for assessing the complexity of dynamic and physiological systems. This scoping review examines the relationship between information theory quantifiers (ITQs), bioelectrical signals, and time series, and the limited diagnostic value of these measures in functional dyspepsia (FD). Three main variables were defined in this study: entropy, health experiments, and FD. Eighty-five academic documents were analyzed using the PRISMA methodology, following 4 phases: (i) heuristic; (ii) classification and systematic review; (iii) hermeneutic analysis; and (iv) presentation of results. ITQs are currently applied in the study of neurodegenerative diseases, cardiological conditions, and, to a lesser extent, gastric disorders, thereby opening new avenues for diagnosis and comprehensive clinical management. The review of the documents shows that, despite the methodological robustness, statistical testing, classification approaches, and the breadth of entropy measures employed, significant challenges remain when integrating these techniques due to the intrinsic complexity and heterogeneity of bioelectrical signals in FD. Furthermore, knowledge gaps persist, particularly in digestive disorders such as FD, underscoring the need to deepen and diversify analytical methodologies.</p>","PeriodicalId":42484,"journal":{"name":"Biomedical Engineering and Computational Biology","volume":"17 ","pages":"11795972261438666"},"PeriodicalIF":2.9,"publicationDate":"2026-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13158506/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147934344","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}