Progress in biomedical engineering (Bristol, England)最新文献

{"title":"6G networks for the operating room of the future.","authors":"Franziska Jurosch, Nicolai Kröger, Sven Kolb, Fidan Mehmeti, Eimo Martens, Stefanie Speidel, Wolfgang Kellerer, Dirk Wilhelm, Jonas Fuchtmann","doi":"10.1088/2516-1091/ad819c","DOIUrl":"10.1088/2516-1091/ad819c","url":null,"abstract":"<p><p>Technical setups in today's operating rooms (ORs) are becoming increasingly complex, especially with the integration of applications which rely on the fusion of multiple information sources. While manufacturers have already started to make use of such approaches, the quest for fully integrated ORs becoming standard is still ongoing. We describe a variety of state-of-the-art projects that envision an OR of the future in order to identify missing building blocks. While these initial implementations of sensor fused ORs have shown to be promising, all current proposals lack a scalable networking backbone that serves the needs of future applications. We therefore discuss how the coming 6G standard's envisioned advancements can provide a flexible and intelligent platform to enable the fully integrated OR of the future.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"6 4","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803796","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":"Systematic review of experimental paradigms and deep neural networks for electroencephalography-based cognitive workload detection.","authors":"Vishnu K N, Cota Navin Gupta","doi":"10.1088/2516-1091/ad8530","DOIUrl":"10.1088/2516-1091/ad8530","url":null,"abstract":"<p><p>This article summarizes a systematic literature review of deep neural network-based cognitive workload (CWL) estimation from electroencephalographic (EEG) signals. The focus of this article can be delineated into two main elements: first is the identification of experimental paradigms prevalently employed for CWL induction, and second, is an inquiry about the data structure and input formulations commonly utilized in deep neural networks (DNN)-based CWL detection. The survey revealed several experimental paradigms that can reliably induce either graded levels of CWL or a desired cognitive state due to sustained induction of CWL. This article has characterized them with respect to the number of distinct CWL levels, cognitive states, experimental environment, and agents in focus. Further, this literature analysis found that DNNs can successfully detect distinct levels of CWL despite the inter-subject and inter-session variability typically observed in EEG signals. Several methodologies were found using EEG signals in its native representation of a two-dimensional matrix as input to the classification algorithm, bypassing traditional feature selection steps. More often than not, researchers used DNNs as black-box type models, and only a few studies employed interpretable or explainable DNNs for CWL detection. However, these algorithms were mostly post hoc data analysis and classification schemes, and only a few studies adopted real-time CWL estimation methodologies. Further, it has been suggested that using interpretable deep learning methodologies may shed light on EEG correlates of CWL, but this remains mostly an unexplored area. This systematic review suggests using networks sensitive to temporal dependencies and appropriate input formulations for each type of DNN architecture to achieve robust classification performance. An additional suggestion is to utilize transfer learning methods to achieve high generalizability across tasks (task-independent classifiers), while simple cross-subject data pooling may achieve the same for subject-independent classifiers.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"6 4","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803710","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":"Enhancing mammography: a comprehensive review of computer methods for improving image quality.","authors":"Joana Cristo Santos, Miriam Seoane Santos, Pedro Henriques Abreu","doi":"10.1088/2516-1091/ad776b","DOIUrl":"10.1088/2516-1091/ad776b","url":null,"abstract":"<p><p>Mammography imaging remains the gold standard for breast cancer detection and diagnosis, but challenges in image quality can lead to misdiagnosis, increased radiation exposure, and higher healthcare costs. This comprehensive review evaluates traditional and machine learning-based techniques for improving mammography image quality, aiming to benefit clinicians and enhance diagnostic accuracy. Our literature search, spanning 2015 - 2024, identified 115 articles focusing on contrast enhancement and noise reduction methods, including histogram equalization, filtering, unsharp masking, fuzzy logic, transform-based techniques, and advanced machine learning approaches. Machine learning, particularly architectures integrating denoising autoencoders with convolutional neural networks, emerged as highly effective in enhancing image quality without compromising detail. The discussion highlights the success of these techniques in improving mammography images' visual quality. However, challenges such as high noise ratios, inconsistent evaluation metrics, and limited open-source datasets persist. Addressing these issues offers opportunities for future research to further advance mammography image enhancement methodologies.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"6 4","pages":""},"PeriodicalIF":5.0,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142803798","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":"Lifetime engineering of bioelectronic implants with mechanically reliable thin film encapsulations","authors":"Martin Niemiec, Kyungjin Kim","doi":"10.1088/2516-1091/ad0b19","DOIUrl":"https://doi.org/10.1088/2516-1091/ad0b19","url":null,"abstract":"Abstract While the importance of thin form factor and mechanical tissue biocompatibility has been made clear for next generation bioelectronic implants, material systems meeting these criteria still have not demonstrated sufficient long-term durability. This review provides an update on the materials used in modern bioelectronic implants as substrates and protective encapsulations, with a particular focus on flexible and conformable devices. We review how thin film encapsulations are known to fail due to mechanical stresses and environmental surroundings under processing and operating conditions. This information is then reflected in recommending state-of-the-art encapsulation strategies for designing mechanically reliable thin film bioelectronic interfaces. Finally, we assess the methods used to evaluate novel bioelectronic implant devices and the current state of their longevity based on encapsulation and substrate materials. We also provide insights for future testing to engineer long-lived bioelectronic implants more effectively and to make implantable bioelectronics a viable option for chronic diseases in accordance with each patient’s therapeutical timescale.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135192315","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 stochastic digital human is now enrolling for in silico imaging trials – Methods and tools for generating digital cohorts","authors":"Aldo Badano, MIguel Lago, Elena Sizikova, Jana Delfino, Shuyue Guan, Mark A Anastasio, Berkman Sahiner","doi":"10.1088/2516-1091/ad04c0","DOIUrl":"https://doi.org/10.1088/2516-1091/ad04c0","url":null,"abstract":"Abstract Randomized clinical trials, while often viewed as the highest evidentiary bar by which to judge the quality of a medical intervention, are far from perfect. In silico imaging trials are computational studies that seek to ascertain the performance of a medical device by collecting this information entirely via computer simulations. The benefits of in silico trials for evaluating new technology include significant resource and time savings, minimization of subject risk, the ability to study devices that are not achievable in the physical world, allow for the rapid and effective investigation of new technologies and ensure representation from all relevant subgroups. To conduct in silico trials, digital representations of humans are needed. We review the latest developments in methods and tools for obtaining digital humans for in silico imaging studies. First, we introduce terminology and a classification of digital human models. Second, we survey available methodologies for generating digital humans with healthy status and for generating diseased cases and discuss briefly the role of augmentation methods. Finally, we discuss approaches for sampling digital cohorts and understanding the trade-offs and potential for study bias associated with selecting specific patient distributions.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135824591","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":"Updates on polyurethane and its multifunctional applications in biomedical engineering","authors":"Z. Miri, S. Faré, Qianli Ma, H. Haugen","doi":"10.1088/2516-1091/acef84","DOIUrl":"https://doi.org/10.1088/2516-1091/acef84","url":null,"abstract":"Polyurethanes (PUs) have properties that make them promising in biomedical applications. PU is recognized as one of the main families of blood and biocompatible materials. PU plays a vital role in the design of medical devices in various medical fields. The structure of PU contains two segments: soft and hard. Its elastomeric feature is due to its soft segment, and its excellent and high mechanical property is because of its hard segment. It is possible to achieve specific desirable and targeted properties by changing the soft and hard chemical structures and the ratio between them. The many properties of PU each draw the attention of different medical fields. This work reviews PU highlighted properties, such as biodegradability, biostability, shape memory, and improved antibacterial activity. Also, because PU has a variety of applications, this review restricts its focus to PU’s prominent applications in tissue engineering, cardiovascular medicine, drug delivery, and wound healing. In addition, it contains a brief review of PU’s applications in biosensors and oral administration.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44625083","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":"Wearable facial electromyography: in the face of new opportunities","authors":"B. Levit, Shira Klorfeld-Auslender, Y. Hanein","doi":"10.1088/2516-1091/ace508","DOIUrl":"https://doi.org/10.1088/2516-1091/ace508","url":null,"abstract":"Facial muscles play an important role in a vast range of physiological functions, ranging from mastication to communication. Any disruption in their normal function may lead to serious negative effects on human well-being. A very wide range of medical disorders and conditions in psychology, neurology, psychiatry, and cosmetic surgery are related to facial muscles, and scientific explorations spanning over decades exposed many fascinating phenomena. For example, expansive evidence implicates facial muscle activation with the expression of emotions. Yet, the exact manner by which emotions are expressed is still debated: whether facial expressions are universal, how gender and cultural differences shape facial expressions and if and how facial muscle activation shape the internal emotional state. Surface electromyography (EMG) is one of the best tools for direct investigation of facial muscle activity and can be applied for medical and research purposes. The use of surface EMG has been so far restricted, owing to limited resolution and cumbersome setups. Current technologies are inconvenient, interfere with the subject normal behavior, and require know-how in proper electrode placement. High density electrode arrays based on soft skin technology is a recent development in the realm of surface EMG. It opens the door to perform facial EMG (fEMG) with high signal quality, while maintaining significantly more natural environmental conditions and higher data resolution. Signal analysis of multi-electrode recordings can also reduce crosstalk to achieve single muscle resolution. This perspective paper presents and discusses new opportunities in mapping facial muscle activation, brought about by this technological advancement. The paper briefly reviews some of the main applications of fEMG and presents how these applications can benefit from a more precise and less intrusive technology.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47508498","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":"A systematic review of cardiac <i>in-silico</i> clinical trials.","authors":"Cristobal Rodero,&nbsp;Tiffany M G Baptiste,&nbsp;Rosie K Barrows,&nbsp;Hamed Keramati,&nbsp;Charles P Sillett,&nbsp;Marina Strocchi,&nbsp;Pablo Lamata,&nbsp;Steven A Niederer","doi":"10.1088/2516-1091/acdc71","DOIUrl":"https://doi.org/10.1088/2516-1091/acdc71","url":null,"abstract":"<p><p>Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through <i>in-silico</i> clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In <math><mn>75</mn><mi>%</mi></math> of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in <math><mn>19</mn><mi>%</mi></math> of ISCTs. The specific software used was not reported in <math><mn>14</mn><mi>%</mi></math> of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with <math><mn>28</mn><mi>%</mi></math> of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only <math><mn>19</mn><mi>%</mi></math> of the studies. In <math><mn>97</mn><mi>%</mi></math> of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"5 3","pages":"032004"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10286106/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9754758","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":"Imaging skins: stretchable and conformable on-organ beta particle detectors for radioguided surgery","authors":"S. Dietsch, L. Lindenroth, A. Stilli, D. Stoyanov","doi":"10.1088/2516-1091/acdc70","DOIUrl":"https://doi.org/10.1088/2516-1091/acdc70","url":null,"abstract":"While radioguided surgery (RGS) traditionally relied on detecting gamma rays, direct detection of beta particles could facilitate the detection of tumour margins intraoperatively by reducing radiation noise emanating from distant organs, thereby improving the signal-to-noise ratio of the imaging technique. In addition, most existing beta detectors do not offer surface sensing or imaging capabilities. Therefore, we explore the concept of a stretchable scintillator to detect beta-particles emitting radiotracers that would be directly deployed on the targeted organ. Such detectors, which we refer to as imaging skins, would work as indirect radiation detectors made of light-emitting agents and biocompatible stretchable material. Our vision is to detect scintillation using standard endoscopes routinely employed in minimally invasive surgery. Moreover, surgical robotic systems would ideally be used to apply the imaging skins, allowing for precise control of each component, thereby improving positioning and task repeatability. While still in the exploratory stages, this innovative approach has the potential to improve the detection of tumour margins during RGS by enabling real-time imaging, ultimately improving surgical outcomes.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49340207","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":"Mathematics of biomedical imaging today—a perspective","authors":"M. Betcke, C. Schönlieb","doi":"10.1088/2516-1091/acd973","DOIUrl":"https://doi.org/10.1088/2516-1091/acd973","url":null,"abstract":"Biomedical imaging is a fascinating, rich and dynamic research area, which has huge importance in biomedical research and clinical practice alike. The key technology behind the processing, and automated analysis and quantification of imaging data is mathematics. Starting with the optimisation of the image acquisition and the reconstruction of an image from indirect tomographic measurement data, all the way to the automated segmentation of tumours in medical images and the design of optimal treatment plans based on image biomarkers, mathematics appears in all of these in different flavours. Non-smooth optimisation in the context of sparsity-promoting image priors, partial differential equations for image registration and motion estimation, and deep neural networks for image segmentation, to name just a few. In this article, we present and review mathematical topics that arise within the whole biomedical imaging pipeline, from tomographic measurements to clinical support tools, and highlight some modern topics and open problems. The article is addressed to both biomedical researchers who want to get a taste of where mathematics arises in biomedical imaging as well as mathematicians who are interested in what mathematical challenges biomedical imaging research entails.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42684165","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}