BMC biomedical engineering最新文献

{"title":"Fourteen days free-living evaluation of an open-source algorithm for counting steps in healthy adults with a large variation in physical activity level.","authors":"Ivar Holm,&nbsp;Jonatan Fridolfsson,&nbsp;Mats Börjesson,&nbsp;Daniel Arvidsson","doi":"10.1186/s42490-023-00071-9","DOIUrl":"https://doi.org/10.1186/s42490-023-00071-9","url":null,"abstract":"<p><strong>Background: </strong>The number of steps by an individual, has traditionally been assessed with a pedometer, but increasingly with an accelerometer. The ActiLife software (AL) is the most common way to process accelerometer data to steps, but it is not open source which could aid understanding of measurement errors. The aim of this study was to compare assessment of steps from the open-source algorithm part of the GGIR package and two closed algorithms, AL normal (n) and low frequency extension (lfe) algorithms to Yamax pedometer, as reference. Free-living in healthy adults with a wide range of activity level was studied.</p><p><strong>Results: </strong>A total 46 participants divided by activity level into a low-medium active group and a high active group, wore both an accelerometer and a pedometer for 14 days. In total 614 complete days were analyzed. A significant correlation between Yamax and all three algorithms was shown but all comparisons were significantly different with paired t-tests except for ALn vs Yamax. The mean bias shows that ALn slightly overestimated steps in the low-medium active group and slightly underestimated steps in high active group. The mean percentage error (MAPE) was 17% and 9% respectively. The ALlfe overestimated steps by approximately 6700/day in both groups and the MAPE was 88% in the low-medium active group and 43% in the high active group. The open-source algorithm underestimated steps with a systematic error related to activity level. The MAPE was 28% in the low-medium active group and 48% in the high active group.</p><p><strong>Conclusion: </strong>The open-source algorithm captures steps fairly well in low-medium active individuals when comparing with Yamax pedometer, but did not show satisfactory results in more active individuals, indicating that it must be modified before implemented in population research. The AL algorithm without the low frequency extension measures similar number of steps as Yamax in free-living and is a useful alternative before a valid open-source algorithm is available.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"5 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2023-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10103381/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9310777","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":"Parametric study of a bubble removing device for hemodialysis.","authors":"Poonnapa Chaichudchaval,&nbsp;Nunthapat Fuangkamonvet,&nbsp;Supajitra Piboonlapudom,&nbsp;Teeranoot Chanthasopeephan","doi":"10.1186/s42490-023-00069-3","DOIUrl":"https://doi.org/10.1186/s42490-023-00069-3","url":null,"abstract":"<p><strong>Background: </strong>This paper sets out to design a device for removing bubbles during the process of hemodialysis. The concept is to guide the bubbles while traveling through the device and eventually the bubbles can be collected. The design focuses on the analysis of various parameters i.e. inlet diameter, inlet velocity and size of the pitch. The initial diameters of Models 1 and 2 have thread regions of 6 and 10 mm, respectively.</p><p><strong>Parameters: </strong>Swirl number, Taylor number, Lift coefficient along with pressure field are also implemented.</p><p><strong>Results: </strong>Based on computational fluid dynamics analysis, the bubbles' average maximum equilibrium position for Model 1 reached 1.995 mm, being greater than that of Model 2, which attained 1.833 mm. Then, 16,000 bubbles were released into Model 1 to validate the performance of the model. This number of bubbles is typically found in the dialysis. Thus, it was found that 81.53% of bubbles passed through the radial region of 2.20 ± 0.30 mm. The appropriate collecting plane was at 100 mm, as measured from the inlet position along the axial axis. The Taylor number, Lift coefficient, and Swirl number proved to be significant parameters for describing the movement of the bubbles. Results were based on multiple inlet velocities. It is seen that Model 3, the improved model with unequal pitch, reached a maximum equilibrium position of 2.24 mm.</p><p><strong>Conclusion: </strong>Overall, results demonstrated that Model 1 was the best design compared to Models 2 and 3. Model 1 was found capable of guiding the bubbles to the edge location and did not generate extra bubbles. Thus, the parametric study, herein, can be used as a prototype for removing bubbles during the process of hemodialysis.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"5 1","pages":"2"},"PeriodicalIF":0.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10067188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9245808","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":"Wavelet image scattering based glaucoma detection.","authors":"Hafeez Alani Agboola,&nbsp;Jesuloluwa Emmanuel Zaccheus","doi":"10.1186/s42490-023-00067-5","DOIUrl":"https://doi.org/10.1186/s42490-023-00067-5","url":null,"abstract":"<p><strong>Background: </strong>The ever-growing need for cheap, simple, fast, and accurate healthcare solutions spurred a lot of research activities which are aimed at the reliable deployment of artificial intelligence in the medical fields. However, this has proved to be a daunting task especially when looking to make automated diagnoses using biomedical image data. Biomedical image data have complex patterns which human experts find very hard to comprehend. Against this backdrop, we applied a representation or feature learning algorithm: Invariant Scattering Convolution Network or Wavelet scattering Network to retinal fundus images and studied the the efficacy of the automatically extracted features therefrom for glaucoma diagnosis/detection. The influence of wavelet scattering network parameter settings as well as 2-D channel image type on the detection correctness is also examined. Our work is a distinct departure from the usual method where wavelet transform is applied to pre-processed retinal fundus images and handcrafted features are extracted from the decomposition results. Here, the RIM-ONE DL image dataset was fed into a wavelet scattering network developed in the Matlab environment to achieve a stage-wise decomposition process called wavelet scattering of the retinal fundus images thereby, automatically learning features from the images. These features were then used to build simple and computationally cheap classification algorithms.</p><p><strong>Results: </strong>Maximum detection correctness of 98% was achieved on the held-out test set. Detection correctness is highly sensitive to scattering network parameter setting and 2-D channel image type.</p><p><strong>Conclusion: </strong>A superficial comparison of the classification results obtained from our work and those obtained using a convolutional neural network underscores the potentiality of the proposed method for glaucoma detection.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"5 1","pages":"1"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9979468/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10826651","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":"Experimental validation of a portable tidal volume indicator for bag valve mask ventilation.","authors":"Benjamin S Maxey, Luke A White, Giovanni F Solitro, Steven A Conrad, J Steven Alexander","doi":"10.1186/s42490-022-00066-y","DOIUrl":"10.1186/s42490-022-00066-y","url":null,"abstract":"<p><strong>Introduction: </strong>Short-term emergency ventilation is most typically accomplished through bag valve mask (BVM) techniques. BVMs like the AMBU^® bag are cost-effective and highly portable but are also highly prone to user error, especially in high-stress emergent situations. Inaccurate and inappropriate ventilation has the potential to inflict great injury to patients through hyper- and hypoventilation. Here, we present the BVM Emergency Narration-Guided Instrument (BENGI) - a tidal volume feedback monitoring device that provides instantaneous visual and audio feedback on delivered tidal volumes, respiratory rates, and inspiratory/expiratory times. Providing feedback on the depth and regularity of respirations enables providers to deliver more consistent and accurate tidal volumes and rates. We describe the design, assembly, and validation of the BENGI as a practical tool to reduce manual ventilation-induced lung injury.</p><p><strong>Methods: </strong>The prototype BENGI was assembled with custom 3D-printed housing and commercially available electronic components. A mass flow sensor in the central channel of the device measures air flow, which is used to calculate tidal volume. Tidal volumes are displayed via an LED ring affixed to the top of the BENGI. Additional feedback is provided through a speaker in the device. Central processing is accomplished through an Arduino microcontroller. Validation of the BENGI was accomplished using benchtop simulation with a clinical ventilator, BVM, and manikin test lung. Known respiratory quantities were delivered by the ventilator which were then compared to measurements from the BENGI to validate the accuracy of flow measurements, tidal volume calculations, and audio cue triggers.</p><p><strong>Results: </strong>BENGI tidal volume measurements were found to lie within 4% of true delivered tidal volume values (95% CI of 0.53 to 3.7%) when breaths were delivered with 1-s inspiratory times, with similar performance for breaths delivered with 0.5-s inspiratory times (95% CI of 1.1 to 6.7%) and 2-s inspiratory times (95% CI of -1.1 to 2.3%). Audio cues \"Bag faster\" (1.84 to 2.03 s), \"Bag slower\" (0.35 to 0.41 s), and \"Leak detected\" (43 to 50%) were triggered close to target trigger values (2.00 s, 0.50 s, and 50%, respectively) across varying tidal volumes.</p><p><strong>Conclusions: </strong>The BENGI achieved its proposed goals of accurately measuring and reporting tidal volumes delivered through BVM systems, providing immediate feedback on the quality of respiratory performance through audio and visual cues. The BENGI has the potential to reduce manual ventilation-induced lung injury and improve patient outcomes by providing accurate feedback on ventilatory parameters.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":" ","pages":"9"},"PeriodicalIF":0.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9668705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40706274","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":"An open-access plug-in program for 3D modelling distinct material properties of cortical and trabecular bone.","authors":"Gregory R Roytman,&nbsp;Matan Cutler,&nbsp;Kenneth Milligan,&nbsp;Steven M Tommasini,&nbsp;Daniel H Wiznia","doi":"10.1186/s42490-022-00065-z","DOIUrl":"https://doi.org/10.1186/s42490-022-00065-z","url":null,"abstract":"<p><strong>Background: </strong>Finite element modelling the material behavior of bone in-silico is a powerful tool to predict the best suited surgical treatment for individual patients.</p><p><strong>Results: </strong>We demonstrate the development and use of a pre-processing plug-in program with a 3D modelling image processing software suite (Synopsys Simpleware, ScanIP) to assist with identifying, isolating, and defining cortical and trabecular bone material properties from patient specific computed tomography scans. The workflow starts by calibrating grayscale values of each constituent element with a phantom - a standardized object with defined densities. Using an established power law equation, we convert the apparent density value per voxel to a Young's Modulus. The resulting \"calibrated\" scan can be used for modeling and in-silico experimentation with Finite Element Analysis.</p><p><strong>Conclusions: </strong>This process allows for the creation of realistic and personalized simulations to inform a surgeon's decision-making. We have made this plug-in program open and accessible as a supplemental file.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"4 1","pages":"8"},"PeriodicalIF":0.0,"publicationDate":"2022-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9509591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10740442","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":"The effects of electrical stimulation on glial cell behaviour.","authors":"Christopher T Tsui,&nbsp;Preet Lal,&nbsp;Katelyn V R Fox,&nbsp;Matthew A Churchward,&nbsp;Kathryn G Todd","doi":"10.1186/s42490-022-00064-0","DOIUrl":"https://doi.org/10.1186/s42490-022-00064-0","url":null,"abstract":"<p><p>Neural interface devices interact with the central nervous system (CNS) to substitute for some sort of functional deficit and improve quality of life for persons with disabilities. Design of safe, biocompatible neural interface devices is a fast-emerging field of neuroscience research. Development of invasive implant materials designed to directly interface with brain or spinal cord tissue has focussed on mitigation of glial scar reactivity toward the implant itself, but little exists in the literature that directly documents the effects of electrical stimulation on glial cells. In this review, a survey of studies documenting such effects has been compiled and categorized based on the various types of stimulation paradigms used and their observed effects on glia. A hybrid neuroscience cell biology-engineering perspective is offered to highlight considerations that must be made in both disciplines in the development of a safe implant. To advance knowledge on how electrical stimulation affects glia, we also suggest experiments elucidating electrochemical reactions that may occur as a result of electrical stimulation and how such reactions may affect glia. Designing a biocompatible stimulation paradigm should be a forefront consideration in the development of a device with improved safety and longevity.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":" ","pages":"7"},"PeriodicalIF":0.0,"publicationDate":"2022-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40345852","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":"Biomechanical validation of additively manufactured artificial femoral bones.","authors":"F Metzner,&nbsp;C Neupetsch,&nbsp;A Carabello,&nbsp;M Pietsch,&nbsp;T Wendler,&nbsp;W-G Drossel","doi":"10.1186/s42490-022-00063-1","DOIUrl":"https://doi.org/10.1186/s42490-022-00063-1","url":null,"abstract":"<p><p>Replicating the mechanical behavior of human bones, especially cancellous bone tissue, is challenging. Typically, conventional bone models primarily consist of polyurethane foam surrounded by a solid shell. Although nearly isotropic foam components have mechanical properties similar to cancellous bone, they do not represent the anisotropy and inhomogeneity of bone architecture. To consider the architecture of bone, models were developed whose core was additively manufactured based on CT data. This core was subsequently coated with glass fiber composite. Specimens consisting of a gyroid-structure were fabricated using fused filament fabrication (FFF) techniques from different materials and various filler levels. Subsequent compression tests showed good accordance between the mechanical behavior of the printed specimens and human bone. The unidirectional fiberglass composite showed higher strength and stiffness than human cortical bone in 3-point bending tests, with comparable material behaviors being observed. During biomechanical investigation of the entire assembly, femoral prosthetic stems were inserted into both artificial and human bones under controlled conditions, while recording occurring forces and strains. All of the artificial prototypes, made of different materials, showed analogous behavior to human bone. In conclusion, it was shown that low-cost FFF technique can be used to generate valid bone models and selectively modify their properties by changing the infill.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"4 1","pages":"6"},"PeriodicalIF":0.0,"publicationDate":"2022-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9354338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10579198","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":"Gold-based nanoplatform for a rapid lateral flow immunochromatographic test assay for gluten detection","authors":"Arefe Momeni, M. Rostami-Nejad, R. Salarian, M. Rabiee, Elham Aghamohammadi, M. Zali, N. Rabiee, F. Tay, Pooyan Makvandi","doi":"10.1186/s42490-022-00062-2","DOIUrl":"https://doi.org/10.1186/s42490-022-00062-2","url":null,"abstract":"","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65794516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of glioma-subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors","authors":"Ali, Muhaddisa Barat, Gu, Irene Yu-Hua, Lidemar, Alice, Berger, Mitchel S., Widhalm, Georg, Jakola, Asgeir Store","doi":"10.1186/s42490-022-00061-3","DOIUrl":"https://doi.org/10.1186/s42490-022-00061-3","url":null,"abstract":"For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) is desirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help the classification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated data with ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consuming process with high demand on medical personnel. As an alternative automatic segmentation is often used. However, it does not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRI acquisition parameters across imaging centers, as segmentation is an ill-defined problem. Analogous to visual object tracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas in MR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding box areas (e.g. ellipse shaped boxes) for classification without a significant drop in performance. In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments were conducted on two datasets (US and TCGA) consisting of multi-modality MRI scans where the US dataset contained patients with diffuse low-grade gliomas (dLGG) exclusively. Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and 79.50% for IDH mutation/wild-type on TCGA dataset. Comparisons with that of using annotated GT tumor data for training showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype). Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for training a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With more data that can be made available, this may be a reasonable trade-off where decline in performance may be counteracted with more data.","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138516386","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The size of via holes influence the amplitude and selectivity of neural signals in Micro-ECoG arrays.","authors":"Manan Sethia,&nbsp;Mesut Sahin","doi":"10.1186/s42490-022-00060-4","DOIUrl":"https://doi.org/10.1186/s42490-022-00060-4","url":null,"abstract":"<p><strong>Background: </strong>Electrocorticography (ECoG) arrays are commonly used to record the brain activity both in animal and human subjects. There is a lack of guidelines in the literature as to how the array geometry, particularly the via holes in the substrate, affects the recorded signals. A finite element (FE) model was developed to simulate the electric field generated by neurons located at different depths in the rat brain cortex and a micro ECoG array (μECoG) was placed on the pia surface for recording the neural signal. The array design chosen was a typical array of 8 × 8 circular (100 μm in diam.) contacts with 500 μm pitch. The size of the via holes between the recording contacts was varied to see the effect.</p><p><strong>Results: </strong>The results showed that recorded signal amplitudes were reduced if the substrate was smaller than about four times the depth of the neuron in the gray matter. The signal amplitude profiles had dips around the via holes and the amplitudes were also lower at the contact sites as compared to the design without the holes; an effect that increased with the hole size. Another noteworthy result is that the spatial selectivity of the multi-contact recordings could be improved or reduced by the selection of the via hole sizes, and the effect depended on the distance between the neuron pair targeted for selective recording and its depth.</p><p><strong>Conclusions: </strong>The results suggest that the via-hole size clearly affects the recorded neural signal amplitudes and it can be leveraged as a parameter to reduce the inter-channel correlation and thus maximize the information content of neural signals with μECoG arrays.</p>","PeriodicalId":72425,"journal":{"name":"BMC biomedical engineering","volume":" ","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2022-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8935835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40310714","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}