Pub Date : 2023-11-09DOI: 10.1088/2516-1091/ad0b19
Martin Niemiec, Kyungjin Kim
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-10-18DOI: 10.1088/2516-1091/ad04c0
Aldo Badano, MIguel Lago, Elena Sizikova, Jana Delfino, Shuyue Guan, Mark A Anastasio, Berkman Sahiner
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-08-11DOI: 10.1088/2516-1091/acef84
Z. Miri, S. Faré, Qianli Ma, H. Haugen
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-07-06DOI: 10.1088/2516-1091/ace508
B. Levit, Shira Klorfeld-Auslender, Y. Hanein
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-07-01DOI: 10.1088/2516-1091/acdc71
Cristobal Rodero, Tiffany M G Baptiste, Rosie K Barrows, Hamed Keramati, Charles P Sillett, Marina Strocchi, Pablo Lamata, Steven A Niederer
Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico 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 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 of ISCTs. The specific software used was not reported in of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only of the studies. In 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.
{"title":"A systematic review of cardiac <i>in-silico</i> clinical trials.","authors":"Cristobal Rodero, Tiffany M G Baptiste, Rosie K Barrows, Hamed Keramati, Charles P Sillett, Marina Strocchi, Pablo Lamata, 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":null,"pages":null},"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}
Pub Date : 2023-07-01DOI: 10.1088/2516-1091/acdc70
S. Dietsch, L. Lindenroth, A. Stilli, D. Stoyanov
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-05-26DOI: 10.1088/2516-1091/acd973
M. Betcke, C. Schönlieb
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.
{"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":null,"pages":null},"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}
Pub Date : 2023-05-04DOI: 10.1088/2516-1091/acd28b
Benjamin Killeen, Sue Min Cho, M. Armand, Russell H. Taylor, M. Unberath
To mitigate the challenges of operating through narrow incisions under image guidance, there is a desire to develop intelligent systems that assist decision making and spatial reasoning in minimally invasive surgery (MIS). In this context, machine learning-based systems for interventional image analysis are receiving considerable attention because of their flexibility and the opportunity to provide immediate, informative feedback to clinicians. It is further believed that learning-based image analysis may eventually form the foundation for semi- or fully automated delivery of surgical treatments. A significant bottleneck in developing such systems is the availability of annotated images with sufficient variability to train generalizable models, particularly the most recently favored deep convolutional neural networks or transformer architectures. A popular alternative to acquiring and manually annotating data from the clinical practice is the simulation of these data from human-based models. Simulation has many advantages, including the avoidance of ethical issues, precisely controlled environments, and the scalability of data collection. Here, we survey recent work that relies on in silico training of learning-based MIS systems, in which data are generated via computational simulation. For each imaging modality, we review available simulation tools in terms of compute requirements, image quality, and usability, as well as their applications for training intelligent systems. We further discuss open challenges for simulation-based development of MIS systems, such as the need for integrated imaging and physical modeling for non-optical modalities, as well as generative patient models not dependent on underlying computed tomography, MRI, or other patient data. In conclusion, as the capabilities of in silico training mature, with respect to sim-to-real transfer, computational efficiency, and degree of control, they are contributing toward the next generation of intelligent surgical systems.
{"title":"In silico simulation: a key enabling technology for next-generation intelligent surgical systems","authors":"Benjamin Killeen, Sue Min Cho, M. Armand, Russell H. Taylor, M. Unberath","doi":"10.1088/2516-1091/acd28b","DOIUrl":"https://doi.org/10.1088/2516-1091/acd28b","url":null,"abstract":"To mitigate the challenges of operating through narrow incisions under image guidance, there is a desire to develop intelligent systems that assist decision making and spatial reasoning in minimally invasive surgery (MIS). In this context, machine learning-based systems for interventional image analysis are receiving considerable attention because of their flexibility and the opportunity to provide immediate, informative feedback to clinicians. It is further believed that learning-based image analysis may eventually form the foundation for semi- or fully automated delivery of surgical treatments. A significant bottleneck in developing such systems is the availability of annotated images with sufficient variability to train generalizable models, particularly the most recently favored deep convolutional neural networks or transformer architectures. A popular alternative to acquiring and manually annotating data from the clinical practice is the simulation of these data from human-based models. Simulation has many advantages, including the avoidance of ethical issues, precisely controlled environments, and the scalability of data collection. Here, we survey recent work that relies on in silico training of learning-based MIS systems, in which data are generated via computational simulation. For each imaging modality, we review available simulation tools in terms of compute requirements, image quality, and usability, as well as their applications for training intelligent systems. We further discuss open challenges for simulation-based development of MIS systems, such as the need for integrated imaging and physical modeling for non-optical modalities, as well as generative patient models not dependent on underlying computed tomography, MRI, or other patient data. In conclusion, as the capabilities of in silico training mature, with respect to sim-to-real transfer, computational efficiency, and degree of control, they are contributing toward the next generation of intelligent surgical systems.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48183611","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}
Pub Date : 2023-04-20DOI: 10.1088/2516-1091/acceeb
Eileen R. Wallace, Z. Yue, M. Dottori, F. Wood, M. Fear, G. Wallace, S. Beirne
In the quest to improve both aesthetic and functional outcomes for patients, the clinical care of full-thickness cutaneous wounds has undergone significant development over the past decade. A shift from replacement to regeneration has prompted the development of skin substitute products, however, inaccurate replication of host tissue properties continues to stand in the way of realising the ultimate goal of scar-free healing. Advances in three-dimensional (3D) bioprinting and biomaterials used for tissue engineering have converged in recent years to present opportunities to progress this field. However, many of the proposed bioprinting strategies for wound healing involve lengthy in-vitro cell culture and construct maturation periods, employ complex deposition technologies, and lack credible point of care (POC) delivery protocols. In-situ bioprinting is an alternative strategy which can combat these challenges. In order to survive the journey to bedside, printing protocols must be curated, and biomaterials/cells selected which facilitate intraoperative delivery. In this review, the current status of in-situ 3D bioprinting systems for wound healing applications is discussed, highlighting the delivery methods employed, biomaterials/cellular components utilised and anticipated translational challenges. We believe that with the growth of collaborative networks between researchers, clinicians, commercial, ethical, and regulatory experts, in-situ 3D bioprinting has the potential to transform POC wound care treatment.
{"title":"Point of care approaches to 3D bioprinting for wound healing applications","authors":"Eileen R. Wallace, Z. Yue, M. Dottori, F. Wood, M. Fear, G. Wallace, S. Beirne","doi":"10.1088/2516-1091/acceeb","DOIUrl":"https://doi.org/10.1088/2516-1091/acceeb","url":null,"abstract":"In the quest to improve both aesthetic and functional outcomes for patients, the clinical care of full-thickness cutaneous wounds has undergone significant development over the past decade. A shift from replacement to regeneration has prompted the development of skin substitute products, however, inaccurate replication of host tissue properties continues to stand in the way of realising the ultimate goal of scar-free healing. Advances in three-dimensional (3D) bioprinting and biomaterials used for tissue engineering have converged in recent years to present opportunities to progress this field. However, many of the proposed bioprinting strategies for wound healing involve lengthy in-vitro cell culture and construct maturation periods, employ complex deposition technologies, and lack credible point of care (POC) delivery protocols. In-situ bioprinting is an alternative strategy which can combat these challenges. In order to survive the journey to bedside, printing protocols must be curated, and biomaterials/cells selected which facilitate intraoperative delivery. In this review, the current status of in-situ 3D bioprinting systems for wound healing applications is discussed, highlighting the delivery methods employed, biomaterials/cellular components utilised and anticipated translational challenges. We believe that with the growth of collaborative networks between researchers, clinicians, commercial, ethical, and regulatory experts, in-situ 3D bioprinting has the potential to transform POC wound care treatment.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42040025","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}
Pub Date : 2023-04-18DOI: 10.1088/2516-1091/acce12
Marta Cerina, M. Piastra, M. Frega
In vitro neuronal models have become an important tool to study healthy and diseased neuronal circuits. The growing interest of neuroscientists to explore the dynamics of neuronal systems and the increasing need to observe, measure and manipulate not only single neurons but populations of cells pushed for technological advancement. In this sense, micro-electrode arrays (MEAs) emerged as a promising technique, made of cell culture dishes with embedded micro-electrodes allowing non-invasive and relatively simple measurement of the activity of neuronal cultures at the network level. In the past decade, MEAs popularity has rapidly grown. MEA devices have been extensively used to measure the activity of neuronal cultures mainly derived from rodents. Rodent neuronal cultures on MEAs have been employed to investigate physiological mechanisms, study the effect of chemicals in neurotoxicity screenings, and model the electrophysiological phenotype of neuronal networks in different pathological conditions. With the advancements in human induced pluripotent stem cells (hiPSCs) technology, the differentiation of human neurons from the cells of adult donors became possible. hiPSCs-derived neuronal networks on MEAs have been employed to develop patient-specific in vitro platforms to characterize the pathophysiological phenotype and to test drugs, paving the way towards personalized medicine. In this review, we first describe MEA technology and the information that can be obtained from MEA recordings. Then, we give an overview of studies in which MEAs have been used in combination with different neuronal systems (i.e. rodent 2D and three-dimensional (3D) neuronal cultures, organotypic brain slices, hiPSCs-derived 2D and 3D neuronal cultures, and brain organoids) for biomedical research, including physiology studies, neurotoxicity screenings, disease modeling, and drug testing. We end by discussing potential, challenges and future perspectives of MEA technology, and providing some guidance for the choice of the neuronal model and MEA device, experimental design, data analysis and reporting for scientific publications.
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