Pub Date : 2023-04-12DOI: 10.1088/2516-1091/accc62
Stephen Payne, T. Józsa, W. El-Bouri
In this review, we provide a summary of the state-of-the-art in the in silico modelling of cerebral blood flow (CBF) and its application in in silico clinical trials. CBF plays a key role in the transport of nutrients, including oxygen and glucose, to brain cells, and the cerebral vasculature is a highly complex, multi-scale, dynamic system that acts to ensure that supply and demand of these nutrients are continuously balanced. It also plays a key role in the transport of other substances, such as recombinant tissue-plasminogen activator, to brain tissue. Any dysfunction in CBF can rapidly lead to cell death and permanent damage to brain regions, leading to loss of bodily functions and death. The complexity of the cerebral vasculature and the difficulty in obtaining accurate anatomical information combine to make mathematical models of CBF key in understanding brain supply, diagnosis of cerebrovascular disease, quantification of the effects of thrombi, selection of the optimum intervention, and neurosurgical planning. Similar in silico models have now been widely applied in a variety of body organs (most notably in the heart), but models of CBF are still far behind. The increased availability of experimental data in the last 15 years however has enabled these models to develop more rapidly and this progress is the focus of this review. We thus present a brief review of the cerebral vasculature and the mathematical foundations that underpin CBF in both the microvasculature and the macrovasculature. We also demonstrate how such models can be applied in the context of cerebral diseases and show how this work has recently been expanded to in silico trials for the first time. Most work to date in this context has been performed for ischaemic stroke or cerebral aneurysms, but these in-silico models have many other applications in neurodegenerative diseases where mathematical models have a vital role to play in testing hypotheses and providing test beds for clinical interventions.
{"title":"Review of in silico models of cerebral blood flow in health and pathology","authors":"Stephen Payne, T. Józsa, W. El-Bouri","doi":"10.1088/2516-1091/accc62","DOIUrl":"https://doi.org/10.1088/2516-1091/accc62","url":null,"abstract":"In this review, we provide a summary of the state-of-the-art in the in silico modelling of cerebral blood flow (CBF) and its application in in silico clinical trials. CBF plays a key role in the transport of nutrients, including oxygen and glucose, to brain cells, and the cerebral vasculature is a highly complex, multi-scale, dynamic system that acts to ensure that supply and demand of these nutrients are continuously balanced. It also plays a key role in the transport of other substances, such as recombinant tissue-plasminogen activator, to brain tissue. Any dysfunction in CBF can rapidly lead to cell death and permanent damage to brain regions, leading to loss of bodily functions and death. The complexity of the cerebral vasculature and the difficulty in obtaining accurate anatomical information combine to make mathematical models of CBF key in understanding brain supply, diagnosis of cerebrovascular disease, quantification of the effects of thrombi, selection of the optimum intervention, and neurosurgical planning. Similar in silico models have now been widely applied in a variety of body organs (most notably in the heart), but models of CBF are still far behind. The increased availability of experimental data in the last 15 years however has enabled these models to develop more rapidly and this progress is the focus of this review. We thus present a brief review of the cerebral vasculature and the mathematical foundations that underpin CBF in both the microvasculature and the macrovasculature. We also demonstrate how such models can be applied in the context of cerebral diseases and show how this work has recently been expanded to in silico trials for the first time. Most work to date in this context has been performed for ischaemic stroke or cerebral aneurysms, but these in-silico models have many other applications in neurodegenerative diseases where mathematical models have a vital role to play in testing hypotheses and providing test beds for clinical interventions.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42143049","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 : 2023-04-11DOI: 10.1088/2516-1091/acc2fe
Can Cui, Haichun Yang, Yaohong Wang, Shilin Zhao, Zuhayr Asad, Lori A Coburn, Keith T Wilson, Bennett A Landman, Yuankai Huo
The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.
{"title":"Deep multimodal fusion of image and non-image data in disease diagnosis and prognosis: a review.","authors":"Can Cui, Haichun Yang, Yaohong Wang, Shilin Zhao, Zuhayr Asad, Lori A Coburn, Keith T Wilson, Bennett A Landman, Yuankai Huo","doi":"10.1088/2516-1091/acc2fe","DOIUrl":"10.1088/2516-1091/acc2fe","url":null,"abstract":"<p><p>The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on various images (e.g. radiology, pathology and camera images) and non-image data (e.g. clinical data and genomic data). However, such decision-making procedures can be subjective, qualitative, and have large inter-subject variabilities. With the recent advances in multimodal deep learning technologies, an increasingly large number of efforts have been devoted to a key question: how do we extract and aggregate multimodal information to ultimately provide more objective, quantitative computer-aided clinical decision making? This paper reviews the recent studies on dealing with such a question. Briefly, this review will include the (a) overview of current multimodal learning workflows, (b) summarization of multimodal fusion methods, (c) discussion of the performance, (d) applications in disease diagnosis and prognosis, and (e) challenges and future directions.</p>","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"5 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9715702","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 : 2023-03-29DOI: 10.1088/2516-1091/acc8a9
R. Hernández, P. A. Roberts, W. El-Bouri
Treating retinal diseases to prevent sight loss is an increasingly important challenge. Thanks to the configuration of the eye, the retina can be examined relatively easily in situ. Owing to recent technological development in scanning devices, much progress has been made in understanding the structure of the retina and characterising retinal biomarkers. However, treatment options remain limited and are often of low efficiency and efficacy. In recent years, the concept of in silico clinical trials (ISCTs) has been adopted by many pharmaceutical companies to optimise and accelerate the development of therapeutics. ISCTs rely on the use of mathematical models based on the physical and biochemical mechanisms underpinning a biological system. With appropriate simplifications and assumptions, one can generate computer simulations of various treatment regimens, new therapeutic molecules, delivery strategies and so forth, rapidly and at a fraction of the cost required for the equivalent experiments. Such simulations have the potential not only to hasten the development of therapies and strategies but also to optimise the use of existing therapeutics. In this paper, we review the state-of-the-art in in silico models of the retina for mathematicians, biomedical scientists and clinicians, highlighting the challenges to developing ISCTs. Throughout this paper, we highlight key findings from in silico models about the physiology of the retina in health and disease. We describe the main building blocks of ISCTs and identify challenges to developing ISCTs of retinal diseases.
{"title":"Advancing treatment of retinal disease through in silico trials","authors":"R. Hernández, P. A. Roberts, W. El-Bouri","doi":"10.1088/2516-1091/acc8a9","DOIUrl":"https://doi.org/10.1088/2516-1091/acc8a9","url":null,"abstract":"Treating retinal diseases to prevent sight loss is an increasingly important challenge. Thanks to the configuration of the eye, the retina can be examined relatively easily in situ. Owing to recent technological development in scanning devices, much progress has been made in understanding the structure of the retina and characterising retinal biomarkers. However, treatment options remain limited and are often of low efficiency and efficacy. In recent years, the concept of in silico clinical trials (ISCTs) has been adopted by many pharmaceutical companies to optimise and accelerate the development of therapeutics. ISCTs rely on the use of mathematical models based on the physical and biochemical mechanisms underpinning a biological system. With appropriate simplifications and assumptions, one can generate computer simulations of various treatment regimens, new therapeutic molecules, delivery strategies and so forth, rapidly and at a fraction of the cost required for the equivalent experiments. Such simulations have the potential not only to hasten the development of therapies and strategies but also to optimise the use of existing therapeutics. In this paper, we review the state-of-the-art in in silico models of the retina for mathematicians, biomedical scientists and clinicians, highlighting the challenges to developing ISCTs. Throughout this paper, we highlight key findings from in silico models about the physiology of the retina in health and disease. We describe the main building blocks of ISCTs and identify challenges to developing ISCTs of retinal diseases.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43960746","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 : 2023-03-23DOI: 10.1088/2516-1091/acc70a
Chukwuemeka Ochieze, Soroush Zare, Ye Sun
Wearable robotics, also called exoskeletons, have been engineered for human-centered assistance for decades. They provide assistive technologies for maintaining and improving patients’ natural capabilities towards self-independence and also enable new therapy solutions for rehabilitation towards pervasive health. Upper limb exoskeletons can significantly enhance human manipulation with environments, which is crucial to patients’ independence, self-esteem, and quality of life. For long-term use in both in-hospital and at-home settings, there are still needs for new technologies with high comfort, biocompatibility, and operability. The recent progress in soft robotics has initiated soft exoskeletons (also called exosuits), which are based on controllable and compliant materials and structures. Remarkable literature reviews have been performed for rigid exoskeletons ranging from robot design to different practical applications. Due to the emerging state, few have been focused on soft upper limb exoskeletons. This paper aims to provide a systematic review of the recent progress in wearable upper limb robotics including both rigid and soft exoskeletons with a focus on their designs and applications in various pervasive healthcare settings. The technical needs for wearable robots are carefully reviewed and the assistance and rehabilitation that can be enhanced by wearable robotics are particularly discussed. The knowledge from rigid wearable robots may provide practical experience and inspire new ideas for soft exoskeleton designs. We also discuss the challenges and opportunities of wearable assistive robotics for pervasive health.
{"title":"Wearable upper limb robotics for pervasive health: a review","authors":"Chukwuemeka Ochieze, Soroush Zare, Ye Sun","doi":"10.1088/2516-1091/acc70a","DOIUrl":"https://doi.org/10.1088/2516-1091/acc70a","url":null,"abstract":"Wearable robotics, also called exoskeletons, have been engineered for human-centered assistance for decades. They provide assistive technologies for maintaining and improving patients’ natural capabilities towards self-independence and also enable new therapy solutions for rehabilitation towards pervasive health. Upper limb exoskeletons can significantly enhance human manipulation with environments, which is crucial to patients’ independence, self-esteem, and quality of life. For long-term use in both in-hospital and at-home settings, there are still needs for new technologies with high comfort, biocompatibility, and operability. The recent progress in soft robotics has initiated soft exoskeletons (also called exosuits), which are based on controllable and compliant materials and structures. Remarkable literature reviews have been performed for rigid exoskeletons ranging from robot design to different practical applications. Due to the emerging state, few have been focused on soft upper limb exoskeletons. This paper aims to provide a systematic review of the recent progress in wearable upper limb robotics including both rigid and soft exoskeletons with a focus on their designs and applications in various pervasive healthcare settings. The technical needs for wearable robots are carefully reviewed and the assistance and rehabilitation that can be enhanced by wearable robotics are particularly discussed. The knowledge from rigid wearable robots may provide practical experience and inspire new ideas for soft exoskeleton designs. We also discuss the challenges and opportunities of wearable assistive robotics for pervasive health.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47172917","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 : 2023-03-21DOI: 10.1088/2516-1091/acc625
N. Lan, Jie Zhang, Zhuozhi Zhang, Chih-hong Chou, W. Rymer, C. Niu, Peng Fang
Significant advances have been made to improve control and to provide sensory functions for bionic hands. However, great challenges remain, limiting wide acceptance of bionic hands due to inadequate bidirectional neural compatibility with human users. Recent research has brought to light the necessity for matching neuromechanical behaviors between the prosthesis and the sensorimotor system of amputees. A novel approach to achieving greater neural compatibility leverages the technology of biorealistic modeling with real-time computation. These studies have demonstrated a promising outlook that this unique approach may transform the performance of hand prostheses. Simultaneously, a noninvasive technique of somatotopic sensory feedback has been developed based on evoked tactile sensation (ETS) for conveying natural, intuitive, and digit-specific tactile information to users. This paper reports the recent work on these two important aspects of sensorimotor functions in prosthetic research. A background review is presented first on the state of the art of bionic hand and the various techniques to deliver tactile sensory information to users. Progress in developing the novel biorealistic hand prosthesis and the technique of noninvasive ETS feedback is then highlighted. Finally, challenges to future development of the biorealistic hand prosthesis and implementing the ETS feedback are discussed with respect to shaping a next-generation hand prosthesis.
{"title":"Biorealistic hand prosthesis with compliance control and noninvasive somatotopic sensory feedback","authors":"N. Lan, Jie Zhang, Zhuozhi Zhang, Chih-hong Chou, W. Rymer, C. Niu, Peng Fang","doi":"10.1088/2516-1091/acc625","DOIUrl":"https://doi.org/10.1088/2516-1091/acc625","url":null,"abstract":"Significant advances have been made to improve control and to provide sensory functions for bionic hands. However, great challenges remain, limiting wide acceptance of bionic hands due to inadequate bidirectional neural compatibility with human users. Recent research has brought to light the necessity for matching neuromechanical behaviors between the prosthesis and the sensorimotor system of amputees. A novel approach to achieving greater neural compatibility leverages the technology of biorealistic modeling with real-time computation. These studies have demonstrated a promising outlook that this unique approach may transform the performance of hand prostheses. Simultaneously, a noninvasive technique of somatotopic sensory feedback has been developed based on evoked tactile sensation (ETS) for conveying natural, intuitive, and digit-specific tactile information to users. This paper reports the recent work on these two important aspects of sensorimotor functions in prosthetic research. A background review is presented first on the state of the art of bionic hand and the various techniques to deliver tactile sensory information to users. Progress in developing the novel biorealistic hand prosthesis and the technique of noninvasive ETS feedback is then highlighted. Finally, challenges to future development of the biorealistic hand prosthesis and implementing the ETS feedback are discussed with respect to shaping a next-generation hand prosthesis.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46884755","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 : 2023-03-16DOI: 10.1088/2516-1091/acc4fc
A. Van Camp, K. Houbrechts, L. Cockmartin, H. Woodruff, P. Lambin, N. Marshall, H. Bosmans
Simulated breast lesion models, including microcalcification clusters and masses, have been used in several studies. Realistic lesion models are required for virtual clinical trials to be representative of clinical performance. Multiple methods exist to generate breast lesion models with various levels of realism depending on the application. First, lesion models can be obtained using mathematical methods, such as approximating a lesion with 3D geometric shapes or using algorithmic techniques such as iterative processes to grow a lesion. On the other hand, lesion models can be based on patient data. They can be either created starting from characteristics of real lesions or they can be a replica of clinical lesions by segmenting real cancer cases. Next, various approaches exist to embed these lesions into breast structures to create tumour cases. The simplest method, typically used for calcifications, is intensity scaling. Two other common approaches are the hybrid and total simulation method, in which the lesion model is inserted into a real breast image or a 3D breast model, respectively. In addition, artificial intelligence-based approaches can directly grow breast lesions in breast images. This article provides a review of the literature available on the development of lesion models, simulation methods to insert them into background structures and their applications, including optimisation studies, performance evaluation of software and education.
{"title":"The creation of breast lesion models for mammographic virtual clinical trials: a topical review","authors":"A. Van Camp, K. Houbrechts, L. Cockmartin, H. Woodruff, P. Lambin, N. Marshall, H. Bosmans","doi":"10.1088/2516-1091/acc4fc","DOIUrl":"https://doi.org/10.1088/2516-1091/acc4fc","url":null,"abstract":"Simulated breast lesion models, including microcalcification clusters and masses, have been used in several studies. Realistic lesion models are required for virtual clinical trials to be representative of clinical performance. Multiple methods exist to generate breast lesion models with various levels of realism depending on the application. First, lesion models can be obtained using mathematical methods, such as approximating a lesion with 3D geometric shapes or using algorithmic techniques such as iterative processes to grow a lesion. On the other hand, lesion models can be based on patient data. They can be either created starting from characteristics of real lesions or they can be a replica of clinical lesions by segmenting real cancer cases. Next, various approaches exist to embed these lesions into breast structures to create tumour cases. The simplest method, typically used for calcifications, is intensity scaling. Two other common approaches are the hybrid and total simulation method, in which the lesion model is inserted into a real breast image or a 3D breast model, respectively. In addition, artificial intelligence-based approaches can directly grow breast lesions in breast images. This article provides a review of the literature available on the development of lesion models, simulation methods to insert them into background structures and their applications, including optimisation studies, performance evaluation of software and education.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43811196","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 : 2023-01-23DOI: 10.1088/2516-1091/acb57a
Debora Zrinscak, Lucrezia Lorenzon, M. Maselli, M. Cianchetti
In recent years, soft robotics technologies enabled the development of a new generation of biomedical devices. The combination of elastomeric materials with tunable properties and muscle-like motions paved the way toward more realistic phantoms and innovative soft active implants as artificial organs or assistive mechanisms. This review collects the most relevant studies in the field, giving some insights about their distribution in the past 10 years, their level of development and opening a discussion about the most commonly employed materials and actuating technologies. The reported results show some promising trends, highlighting that the soft robotics approach can help replicate specific material characteristics in the case of static or passive organs but also reproduce peculiar natural motion patterns for the realization of dynamic phantoms or implants. At the same time, some important challenges still need to be addressed. However, by joining forces with other research fields and disciplines, it will be possible to get one step closer to the development of complex, active, self-sensing and deformable structures able to replicate as closely as possible the typical properties and functionalities of our natural body organs.
{"title":"Soft robotics for physical simulators, artificial organs and implantable assistive devices","authors":"Debora Zrinscak, Lucrezia Lorenzon, M. Maselli, M. Cianchetti","doi":"10.1088/2516-1091/acb57a","DOIUrl":"https://doi.org/10.1088/2516-1091/acb57a","url":null,"abstract":"In recent years, soft robotics technologies enabled the development of a new generation of biomedical devices. The combination of elastomeric materials with tunable properties and muscle-like motions paved the way toward more realistic phantoms and innovative soft active implants as artificial organs or assistive mechanisms. This review collects the most relevant studies in the field, giving some insights about their distribution in the past 10 years, their level of development and opening a discussion about the most commonly employed materials and actuating technologies. The reported results show some promising trends, highlighting that the soft robotics approach can help replicate specific material characteristics in the case of static or passive organs but also reproduce peculiar natural motion patterns for the realization of dynamic phantoms or implants. At the same time, some important challenges still need to be addressed. However, by joining forces with other research fields and disciplines, it will be possible to get one step closer to the development of complex, active, self-sensing and deformable structures able to replicate as closely as possible the typical properties and functionalities of our natural body organs.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46700065","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 : 2023-01-20DOI: 10.1088/2516-1091/acb51c
Elisa Donati, G. Indiveri
Bioelectronic medicine treats chronic diseases by sensing, processing, and modulating the electronic signals produced in the nervous system of the human body, labeled ‘neural signals’. While electronic circuits have been used for several years in this domain, the progress in microelectronic technology is now allowing increasingly accurate and targeted solutions for therapeutic benefits. For example, it is now becoming possible to modulate signals in specific nerve fibers, hence targeting specific diseases. However, to fully exploit this approach it is crucial to understand what aspects of the nerve signals are important, what is the effect of the stimulation, and what circuit designs can best achieve the desired result. Neuromorphic electronic circuits represent a promising design style for achieving this goal: their ultra-low power characteristics and biologically plausible time constants make them the ideal candidate for building optimal interfaces to real neural processing systems, enabling real-time closed-loop interactions with the biological tissue. In this paper, we highlight the main features of neuromorphic circuits that are ideally suited for interfacing with the nervous system and show how they can be used to build closed-loop hybrid artificial and biological neural processing systems. We present examples of neural computational primitives that can be implemented for carrying out computation on the signals sensed in these closed-loop systems and discuss the way to use their outputs for neural stimulation. We describe examples of applications that follow this approach, highlight open challenges that need to be addressed, and propose actions required to overcome current limitations.
{"title":"Neuromorphic bioelectronic medicine for nervous system interfaces: from neural computational primitives to medical applications","authors":"Elisa Donati, G. Indiveri","doi":"10.1088/2516-1091/acb51c","DOIUrl":"https://doi.org/10.1088/2516-1091/acb51c","url":null,"abstract":"Bioelectronic medicine treats chronic diseases by sensing, processing, and modulating the electronic signals produced in the nervous system of the human body, labeled ‘neural signals’. While electronic circuits have been used for several years in this domain, the progress in microelectronic technology is now allowing increasingly accurate and targeted solutions for therapeutic benefits. For example, it is now becoming possible to modulate signals in specific nerve fibers, hence targeting specific diseases. However, to fully exploit this approach it is crucial to understand what aspects of the nerve signals are important, what is the effect of the stimulation, and what circuit designs can best achieve the desired result. Neuromorphic electronic circuits represent a promising design style for achieving this goal: their ultra-low power characteristics and biologically plausible time constants make them the ideal candidate for building optimal interfaces to real neural processing systems, enabling real-time closed-loop interactions with the biological tissue. In this paper, we highlight the main features of neuromorphic circuits that are ideally suited for interfacing with the nervous system and show how they can be used to build closed-loop hybrid artificial and biological neural processing systems. We present examples of neural computational primitives that can be implemented for carrying out computation on the signals sensed in these closed-loop systems and discuss the way to use their outputs for neural stimulation. We describe examples of applications that follow this approach, highlight open challenges that need to be addressed, and propose actions required to overcome current limitations.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46992338","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 : 2023-01-03DOI: 10.1088/2516-1091/acafbf
P. Myles, Johan Ordish, A. Tucker
In silico trial methods promise to improve the path to market for both medicines and medical devices, targeting the development of products, reducing reliance on animal trials, and providing adjunct evidence to bolster regulatory submissions. In silico trials are only as good as the simulated data which underpins them, consequently, often the most difficult challenge when creating robust in silico models is the generation of simulated measurements or even virtual patients that are representative of real measurements and patients. This article digests the current state of the art for generating synthetic patient data outside the context of in silico trials and outlines potential synergies to unlock the potential of in silico trials using virtual populations, by exploiting synthetic patient data to model effects on a more diverse and representative population. Synthetic data could be defined as artificial data that mimic the properties and relationships in real data. Recent advances in synthetic data generation methodologies have allowed for the generation of high-fidelity synthetic data that are both statistically and clinically, indistinguishable from real patient data. Other experimental work has demonstrated that synthetic data generation methods can be used for selective sample boosting of underrepresented groups. This article will provide a brief outline of synthetic data generation approaches and discuss how evaluation frameworks developed to assess synthetic data fidelity and utility could be adapted to evaluate the similarity of virtual patients used for in silico trials, to real patients. The article will then discuss outstanding challenges and areas for further research that would advance both synthetic data generation methods and in silico trial methods. Finally, the article will also provide a perspective on what evidence will be required to facilitate wider acceptance of in silico trials for regulatory evaluation of medicines and medical devices, including implications for post marketing safety surveillance.
{"title":"The potential synergies between synthetic data and in silico trials in relation to generating representative virtual population cohorts","authors":"P. Myles, Johan Ordish, A. Tucker","doi":"10.1088/2516-1091/acafbf","DOIUrl":"https://doi.org/10.1088/2516-1091/acafbf","url":null,"abstract":"In silico trial methods promise to improve the path to market for both medicines and medical devices, targeting the development of products, reducing reliance on animal trials, and providing adjunct evidence to bolster regulatory submissions. In silico trials are only as good as the simulated data which underpins them, consequently, often the most difficult challenge when creating robust in silico models is the generation of simulated measurements or even virtual patients that are representative of real measurements and patients. This article digests the current state of the art for generating synthetic patient data outside the context of in silico trials and outlines potential synergies to unlock the potential of in silico trials using virtual populations, by exploiting synthetic patient data to model effects on a more diverse and representative population. Synthetic data could be defined as artificial data that mimic the properties and relationships in real data. Recent advances in synthetic data generation methodologies have allowed for the generation of high-fidelity synthetic data that are both statistically and clinically, indistinguishable from real patient data. Other experimental work has demonstrated that synthetic data generation methods can be used for selective sample boosting of underrepresented groups. This article will provide a brief outline of synthetic data generation approaches and discuss how evaluation frameworks developed to assess synthetic data fidelity and utility could be adapted to evaluate the similarity of virtual patients used for in silico trials, to real patients. The article will then discuss outstanding challenges and areas for further research that would advance both synthetic data generation methods and in silico trial methods. Finally, the article will also provide a perspective on what evidence will be required to facilitate wider acceptance of in silico trials for regulatory evaluation of medicines and medical devices, including implications for post marketing safety surveillance.","PeriodicalId":74582,"journal":{"name":"Progress in biomedical engineering (Bristol, England)","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41723490","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 : 2022-12-16DOI: 10.1088/2516-1091/acac57
A. Marinelli, N. Boccardo, F. Tessari, Dario Di Domenico, G. Caserta, M. Canepa, G. Gini, G. Barresi, M. Laffranchi, L. de Michieli, M. Semprini
The journey of a prosthetic user is characterized by the opportunities and the limitations of a device that should enable activities of daily living (ADL). In particular, experiencing a bionic hand as a functional (and, advantageously, embodied) limb constitutes the premise for promoting the practice in using the device, mitigating the risk of its abandonment. In order to achieve such a result, different aspects need to be considered for making the artificial limb an effective solution to accomplish ADL. According to such a perspective, this review aims at presenting the current issues and at envisioning the upcoming breakthroughs in upper limb prosthetic devices. We first define the sources of input and feedback involved in the system control (at user-level and device-level), alongside the related algorithms used in signal analysis. Moreover, the paper focuses on the user-centered design challenges and strategies that guide the implementation of novel solutions in this area in terms of technology acceptance, embodiment, and, in general, human-machine integration based on co-adaptive processes. We here provide the readers (belonging to the target communities of researchers, designers, developers, clinicians, industrial stakeholders, and end-users) with an overview of the state-of-the-art and the potential innovations in bionic hands features, hopefully promoting interdisciplinary efforts for solving current issues of upper limb prostheses. The integration of different perspectives should be the premise to a transdisciplinary intertwining leading to a truly holistic comprehension and improvement of the bionic hands design. Overall, this paper aims to move the boundaries in prosthetic innovation beyond the development of a tool and toward the engineering of human-centered artificial limbs.
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