Bioengineering最新文献

{"title":"Spectral Analysis of Extrahepatic Bile Ducts During Normothermic Liver Machine Perfusion.","authors":"Philipp Zelger, Benjamin Jenewein, Magdalena Sovago, Felix J Krendl, Andras T Meszaros, Benno Cardini, Philipp Gehwolf, Johannes D Pallua, Simone Graf, Stefan Schneeberger, Margot Fodor, Rupert Oberhuber","doi":"10.3390/bioengineering12090966","DOIUrl":"10.3390/bioengineering12090966","url":null,"abstract":"<p><p><b>Background</b>: Biliary complications (BC) affect 5-32% of liver transplant (LT) patients and include strictures, leaks, stones, and disease recurrence. Their risk increases with extended criteria donor (ECD) livers, contributing to early graft dysfunction. Normothermic liver machine perfusion (NLMP) helps reduce bile duct (BD) damage overall, but anastomotic region issues persist. This study assessed hyperspectral imaging (HSI) as a non-invasive method to evaluate BD viability during NLMP. <b>Methods</b>: Eleven donor livers underwent NLMP with HSI at the start and end. Seven were transplanted; four were discarded. HSI measured tissue oxygenation, perfusion, and composition. The spectral data were analyzed using ANOVA, post hoc t-tests, and multifactorial ANOVA to assess spectral changes related to BD position, transplant status, and occurrence of BC. <b>Results</b>: Significant spectral changes were found in the BD region during NLMP. Transplanted livers that developed BC showed changes between 525 and 850 nm, while discarded ones had changes between 625 and 725 nm. Specific spectral bands (500-575 nm, 775-1000 nm) were linked to transplant outcomes and BC. <b>Conclusions:</b> HSI shows promise as a non-invasive tool to assess BD viability during NLMP and may help predict post-transplant BC.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligence Architectures and Machine Learning Applications in Contemporary Spine Care.","authors":"Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman","doi":"10.3390/bioengineering12090967","DOIUrl":"10.3390/bioengineering12090967","url":null,"abstract":"<p><p>The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from reactive to predictive management models in spine surgery. We also highlight multi-omics platforms and federated learning frameworks that support integrative, privacy-preserving analytics at scale. Despite these advances, challenges remain-including algorithmic opacity, regulatory fragmentation, data heterogeneity, and limited generalizability across populations and clinical settings. Through a multidimensional lens, this review outlines not only current capabilities but also future directions to ensure safe, equitable, and high-fidelity AI deployment in spine care delivery.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12466956/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Instrument Data Utilization Based on Laser-Induced Breakdown Spectroscopy (LIBS) for the Identification of <i>Akebia</i> Species.","authors":"Yuge Liu, Qianqian Wang, Tianzhong Luo, Zhifang Zhao, Leifu Wang, Shuai Xu, Hao Zhou, Jiquan Zhao, Zixiao Zhou, Geer Teng","doi":"10.3390/bioengineering12090964","DOIUrl":"10.3390/bioengineering12090964","url":null,"abstract":"<p><p>New technologies and equipment for medicine analysis and diagnostics have always been critical in clinical medication and pharmaceutical production. Especially in the field of traditional Chinese medicine (TCM) where the chemical composition is not fully clear, cross-device analysis and identification using the same technology can sometimes even lead to misjudgments. Akebia species, capable of inducing heat clearing, diuresis, and anti-inflammatory effects, show great potential in clinical applications. However, the three commonly used species differ in pharmacological effects and therefore should not be used interchangeably. We proposed a method combining LIBS with random forest for species identification and established a modeling and verification scheme across device platforms. Spectra of three Akebia species were collected using two LIBS systems equipped with spectrometers of different resolutions. The data acquired from the low-resolution spectrometer were used for model training, while the data from the high-resolution spectrometers were used for testing. A spectral correction and feature selection (SCFS) method was proposed, in which spectral data were first corrected using a standard lamp, followed by feature selection via analysis of variance (ANOVA) to determine the optimal number of discriminative features. The highest classification accuracy of 80.61% was achieved when 28 features were used. Finally, a post-processing (PP) strategy was applied, where abnormal spectra in the test set were removed using density-based spatial clustering of applications with noise (DBSCAN), resulting in a final classification accuracy of 85.50%. These results demonstrate that the proposed \"SCFS-PP\" framework effectively enhances the reliability of cross-instrument data utilization and expands the applicability of LIBS in the field of TCM.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clinical Evidence of Wear Occurrence in CFR-PEEK and Metallic Osteosynthesis Implants: A Systematic Literature Review.","authors":"Remco Doodkorte, Rachèl Kuske, Jacobus Arts","doi":"10.3390/bioengineering12090965","DOIUrl":"10.3390/bioengineering12090965","url":null,"abstract":"<p><p>Carbon fiber-reinforced polyetheretherketone (CFR-PEEK) as an alternative to metallics in orthopedic implants offers biomechanical and radiological advantages. However, the extent of wear particle generation and its clinical impact are unclear. This systematic review evaluates clinical evidence of wear in fracture fixation devices. A systematic search was conducted to identify clinical studies reporting wear of metallic and CFR-PEEK implants used in extremities. Nineteen studies were included: three prospective cohorts, eight retrospective cohorts, one case series, and six case reports. Among 208 fixation plates, 43 were CFR-PEEK and all 93 intramedullary nails were metallic. Risk of bias ranged from low to serious, mainly due to selection bias. Wear-related complications were reported for both materials. Metallic implants showed elevated serum ion levels, metallic debris in tissues, and, in some cases, metallosis. CFR-PEEK implants showed limited evidence of carbon fiber fragments near implants. One comparative study reported higher inflammatory responses in CFR-PEEK explants, though no direct link between debris and implant removal was found. Both metallic and CFR-PEEK fracture fixation devices generate wear particles, which may induce biological responses. However, wear-related complications appear rare, especially with validated implant designs, and clinical significance of wear debris remains limited.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467306/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172832","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integration of EHR and ECG Data for Predicting Paroxysmal Atrial Fibrillation in Stroke Patients.","authors":"Alireza Vafaei Sadr, Manvita Mareboina, Diana Orabueze, Nandini Sarkar, Seyyed Sina Hejazian, Ajith Vemuri, Ravi Shah, Ankit Maheshwari, Ramin Zand, Vida Abedi","doi":"10.3390/bioengineering12090961","DOIUrl":"10.3390/bioengineering12090961","url":null,"abstract":"<p><p>Predicting paroxysmal atrial fibrillation (PAF) is challenging due to its transient nature. Existing methods often rely solely on electrocardiogram (ECG) waveforms or Electronic Health Record (EHR)-based clinical risk factors. We hypothesized that explicitly balancing the contributions of these heterogeneous data sources could improve prediction accuracy. We developed a Transformer-based deep learning model that integrates 12-lead ECG signals and 47 structured EHR variables from 189 patients with cryptogenic stroke, including 49 with PAF. By systematically varying the relative contributions of ECG and EHR data, we identified an optimal ratio for prediction. Best performance (accuracy: 0.70, sensitivity: 0.72, specificity: 0.87, Area Under Curve - Receiver Operating Characteristics (AUROC): 0.65, Area Under the Precision-Recall Curve (AUPRC): 0.43) was achieved using a 5-fold cross-validation when EHR data contributed one-third and ECG data two-thirds of the model's input. This multimodal approach outperformed unimodal models, improving accuracy by 35% over EHR-only and 5% over ECG-only methods. Our results support the value of combining ECG and structured EHR information to improve accuracy and sensitivity in this pilot cohort, motivating validation in larger studies.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467541/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Precision-Controlled Bionic Lung Simulator for Dynamic Respiration Simulation.","authors":"Rong-Heng Zhao, Shuai Ren, Yan Shi, Mao-Lin Cai, Tao Wang, Zu-Jin Luo","doi":"10.3390/bioengineering12090963","DOIUrl":"10.3390/bioengineering12090963","url":null,"abstract":"<p><p>Mechanical ventilation is indispensable for patients with severe respiratory conditions, and high-fidelity lung simulators play a pivotal role in ventilator testing, clinical training, and respiratory research. However, most existing simulators are passive, single-lung models with limited and discrete control over respiratory mechanics, which constrains their ability to reproduce realistic breathing dynamics. To overcome these limitations, this study presents a dual-chamber lung simulator that can operate in both active and passive modes. The system integrates a sliding mode controller enhanced by a linear extended state observer, enabling the accurate replication of complex respiratory patterns. In active mode, the simulator allows for the precise tuning of respiratory muscle force profiles, lung compliance, and airway resistance to generate physiologically accurate flow and pressure waveforms. Notably, it can effectively simulate pathological conditions such as acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) by adjusting key parameters to mimic the characteristic respiratory mechanics of these disorders. Experimental results show that the absolute flow error remains within ±3 L/min, and the response time is under 200 ms, ensuring rapid and reliable performance. In passive mode, the simulator emulates ventilator-dependent conditions, providing continuous adjustability of lung compliance from 30 to 100 mL/cmH2O and airway resistance from 2.01 to 14.67cmH2O/(L/s), with compliance deviations limited to ±5%. This design facilitates fine, continuous modulation of key respiratory parameters, making the system well-suited for evaluating ventilator performance, conducting human-machine interaction studies, and simulating pathological respiratory states.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Current Topics in OCT Applications in Vitreoretinal Surgery.","authors":"Shintaro Horie, Takeshi Yoshida, Kyoko Ohno-Matsui","doi":"10.3390/bioengineering12090962","DOIUrl":"10.3390/bioengineering12090962","url":null,"abstract":"<p><p>Optical coherence tomography (OCT) is an indispensable tool in modern ophthalmology, where it is used in prior examinations, among various instruments, to assess macular or vitreoretinal diseases. Pathological macular/retinal conditions are almost always examined and evaluated with OCT before and after treatment. Vitreoretinal surgery is one of the most effective treatment options for vitreoretinal diseases. OCT data collected during the treatment of these diseases has accumulated, leading to the reporting of a variety of novel biomarkers and valuable findings related to OCT usage. Recent substantial developments in technology have brought ultra-high-resolution spectral domain/swept source OCT, ultra-widefield OCT, and OCT angiography into the retinal clinic. Here, we review the basic development of the instrument and general applications of OCT in ophthalmology. Subsequently, we provide up-to-date OCT topics based on observations in vitreoretinal surgery.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance Insights in Speed Climbing: Quantitative and Qualitative Analysis of Key Movement Metrics.","authors":"Dominik Pandurević, Paweł Draga, Alexander Sutor, Klaus Hochradel","doi":"10.3390/bioengineering12090957","DOIUrl":"10.3390/bioengineering12090957","url":null,"abstract":"<p><p>This study presents a comprehensive analysis of Speed Climbing athletes by examining motion parameters critical to elite performance. As such, several key values are extracted from about 900 competition recordings in order to generate a dataset for the identification of patterns in athletes' technique and efficiency. A CNN-based framework is used to automate the detection of human keypoints and features, enabling a large-scale evaluation of climbing dynamics. The results revealed significant variations in performance for single sections of the wall, particularly in relation to start reaction times (with differences of up to 0.27 s) and increased split times the closer the athletes are to the end of the Speed Climbing wall (from 0.39 s to 0.45 s). In addition, a more detailed examination of the movement sequences was carried out by analyzing the velocity trajectories of hands and feet. The results showed that coordinated and harmonic movements, especially of the lower limbs, correlate strongly with the performance outcome. To ensure an individualized view of the data points, a comparison was made between multiple athletes, revealing insights into the influence of individual biomechanics on the efficiency of movements. The findings provide both trainers and athletes with interesting insights in relation to tailoring training methods by including split time benchmarks and limb coordination.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467755/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Evaluation of a Deep Learning Approach to Automatic Segmentation of Teeth and Shade Guides for Tooth Shade Matching Using the SAM2 Algorithm.","authors":"KyeongHwan Han, JaeHyung Lim, Jin-Soo Ahn, Ki-Sun Lee","doi":"10.3390/bioengineering12090959","DOIUrl":"10.3390/bioengineering12090959","url":null,"abstract":"<p><p>Accurate shade matching is essential in restorative and prosthetic dentistry yet remains difficult due to subjectivity in visual assessments. We develop and evaluate a deep learning approach for the simultaneous segmentation of natural teeth and shade guides in intraoral photographs using four fine-tuned variants of Segment Anything Model 2 (SAM2: tiny, small, base plus, and large) and a UNet baseline trained under the same protocol. The spatial performance was assessed using the Dice Similarity Coefficient (DSC), the Intersection over the Union (IoU), and the 95th-percentile Hausdorff distance normalized by the ground-truth equivalent diameter (HD95). The color consistency within masks was quantified by the coefficient of variation (CV) of the CIELAB components (L*, a*, b*). The perceptual color difference was measured using CIEDE2000 (ΔE00). On a held-out test set, all SAM2 variants achieved a high overlap accuracy; SAM2-large performed best (DSC: 0.987 ± 0.006; IoU: 0.975 ± 0.012; HD95: 1.25 ± 1.80%), followed by SAM2-small (0.987 ± 0.008; 0.974 ± 0.014; 2.96 ± 11.03%), SAM2-base plus (0.985 ± 0.011; 0.971 ± 0.021; 1.71 ± 3.28%), and SAM2-tiny (0.979 ± 0.015; 0.959 ± 0.028; 6.16 ± 11.17%). UNet reached a DSC = 0.972 ± 0.020, an IoU = 0.947 ± 0.035, and an HD95 = 6.54 ± 16.35%. The CV distributions for all of the prediction models closely matched the ground truth (e.g., GT L*: 0.164 ± 0.040; UNet: 0.144 ± 0.028; SAM2-small: 0.164 ± 0.038; SAM2-base plus: 0.162 ± 0.039). The full-mask ΔE00 was low across models, with the summary statistics reported as the median (mean ± SD): UNet: 0.325 (0.487 ± 0.364); SAM2-tiny: 0.162 (0.410 ± 0.665); SAM2-small: 0.078 (0.126 ± 0.166); SAM2-base plus: 0.072 (0.198 ± 0.417); SAM2-large: 0.065 (0.167 ± 0.257). These ΔE00 values lie well below the ≈1 just noticeable difference threshold on average, indicating close chromatic agreement between the predictions and annotations. Within a single dataset and training protocol, fine-tuned SAM2, especially its larger variants, provides robust spatial accuracy, boundary reliability, and color fidelity suitable for clinical shade-matching workflows, while UNet offers a competitive convolutional baseline. These results indicate technical feasibility rather than clinical validation; broader baselines and external, multi-center evaluations are needed to determine its suitability for routine shade-matching workflows.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467639/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Neural-Operator Surrogate for Platelet Deformation Across Capillary Numbers.","authors":"Marco Laudato","doi":"10.3390/bioengineering12090958","DOIUrl":"10.3390/bioengineering12090958","url":null,"abstract":"<p><p>Reliable multiscale models of thrombosis require platelet-scale fidelity at organ-scale cost, a gap that scientific machine learning has the potential to narrow. We trained a DeepONet surrogate on platelet dynamics generated with LAMMPS for platelets spanning ten elastic moduli and capillary numbers (0.07-0.77). The network takes as input the wall shear stress, bond stiffness, time, and initial particle coordinates and returns the full three-dimensional deformation of the membrane. Mean-squared-error minimization with Adam and adaptive learning-rate decay yields a median displacement error below 1%, a 90th percentile below 3%, and a worst case below 4% over the entire calibrated range while accelerating computation by four to five orders of magnitude. Leave-extremes-out retraining shows acceptable extrapolation: the held-out stiffest and most compliant platelets retain sub-3% median error and an 8% maximum. Error peaks coincide with transient membrane self-contact, suggesting improvements via graph neural trunks and physics-informed torque regularization. These results represent a first demonstration of how the surrogate has the potential for coupling with continuum CFD, enabling future platelet-resolved hemodynamic simulations in patient-specific geometries and opening new avenues for predictive thrombosis modeling.</p>","PeriodicalId":8874,"journal":{"name":"Bioengineering","volume":"12 9","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145172306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}