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.
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ISSN: 2516-1091
Progress in Biomedical Engineering is a new interdisciplinary journal publishing high quality authoritative reviews and opinion pieces in the most significant and exciting areas of biomedical engineering research.
Published content by leading experts on the current state of the science and emerging trends aims to fuel discussion on the future direction of research.
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Can Cui et al 2023 Prog. Biomed. Eng. 5 022001
Andrea Marinelli et al 2023 Prog. Biomed. Eng. 5 012001
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.
Xiang Chen et al 2021 Prog. Biomed. Eng. 3 012003
Image registration is a fundamental task in multiple medical image analysis applications. With the advent of deep learning, there have been significant advances in algorithmic performance for various computer vision tasks in recent years, including medical image registration. The last couple of years have seen a dramatic increase in the development of deep learning-based medical image registration algorithms. Consequently, a comprehensive review of the current state-of-the-art algorithms in the field is timely, and necessary. This review is aimed at understanding the clinical applications and challenges that drove this innovation, analysing the functionality and limitations of existing approaches, and at providing insights to open challenges and as yet unmet clinical needs that could shape future research directions. To this end, the main contributions of this paper are: (a) discussion of all deep learning-based medical image registration papers published since 2013 with significant methodological and/or functional contributions to the field; (b) analysis of the development and evolution of deep learning-based image registration methods, summarising the current trends and challenges in the domain; and (c) overview of unmet clinical needs and potential directions for future research in deep learning-based medical image registration.
Tomas da Veiga et al 2020 Prog. Biomed. Eng. 2 032003
With the maturity of surgical robotic systems based on traditional rigid-link principles, the rate of progress slowed as limits of size and controllable degrees of freedom were reached. Continuum robots came with the potential to deliver a step change in the next generation of medical devices, by providing better access, safer interactions and making new procedures possible. Over the last few years, several continuum robotic systems have been launched commercially and have been increasingly adopted in hospitals. Despite the clear progress achieved, continuum robots still suffer from design complexity hindering their dexterity and scalability. Recent advances in actuation methods have looked to address this issue, offering alternatives to commonly employed approaches. Additionally, continuum structures introduce significant complexity in modelling, sensing, control and fabrication; topics which are of particular focus in the robotics community. It is, therefore, the aim of the presented work to highlight the pertinent areas of active research and to discuss the challenges to be addressed before the potential of continuum robots as medical devices may be fully realised.
Alessio P Buccino et al 2022 Prog. Biomed. Eng. 4 022005
Recording from a large neuronal population of neurons is a crucial challenge to unravel how information is processed by the brain. In this review, we highlight the recent advances made in the field of 'spike sorting', which is arguably a very essential processing step to extract neuronal activity from extracellular recordings. More specifically, we target the challenges faced by newly manufactured high-density multi-electrode array devices (HD-MEA), e.g. Neuropixels probes. Among them, we cover in depth the prominent problem of drifts (movements of the neurons with respect to the recording devices) and the current solutions to circumscribe it. In addition, we also review recent contributions making use of deep learning approaches for spike sorting, highlighting their advantages and disadvantages. Next, we highlight efforts and advances in unifying, validating, and benchmarking spike sorting tools. Finally, we discuss the spike sorting field in terms of its open and unsolved challenges, specifically regarding scalability and reproducibility. We conclude by providing our personal view on the future of spike sorting, calling for a community-based development and validation of spike sorting algorithms and fully automated, cloud-based spike sorting solutions for the neuroscience community.
Marta Cerina et al 2023 Prog. Biomed. Eng. 5 032002
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.
Ziyi Yu et al 2021 Prog. Biomed. Eng. 3 022004
For decades, diabetes mellitus has been of wide concern with its high global prevalence, resulting in increasing social and financial burdens for individuals, clinical systems and governments. Continuous glucose monitoring (CGM) has become a popular alternative to the portable finger-prick glucometers available in the market for the convenience of diabetic patients. Hence, it has attracted much interest in various glucose sensing technologies to develop novel glucose sensors with better performance and longer lifetime, especially non-invasive or minimally invasive glucose sensing. Effort has also been put into finding biocompatible materials for implantable applications to achieve effective in vivo CGM. Here, we review the state-of-the-art researches in the field of CGM. The currently commercially available CGM technologies have been analyzed and a summary is provided of the potential types of recently researched non-invasive glucose monitors. Furthermore, the challenges and advances towards implantable applications have also been introduced and discussed, especially the novel biocompatible hydrogel aimed at minimizing the adverse impact from foreign-body response. In addition, a large variety of promising glucose-sensing technologies under research have been reviewed, from traditional electrochemical-based glucose sensors to novel optical and other electrical glucose sensors. The recent development and achievement of the reviewed glucose sensing technologies are discussed, together with the market analysis in terms of the statistical data for the newly published patents in the related field. Thus, the promising direction for future work in this field could be concluded.
Ge Song et al 2021 Prog. Biomed. Eng. 3 032002
Optical coherence tomography (OCT) is a powerful optical imaging technique capable of visualizing the internal structure of biological tissues at near cellular resolution. For years, OCT has been regarded as the standard of care in ophthalmology, acting as an invaluable tool for the assessment of retinal pathology. However, the costly nature of most current commercial OCT systems has limited its general accessibility, especially in low-resource environments. It is therefore timely to review the development of low-cost OCT systems as a route for applying this technology to population-scale disease screening. Low-cost, portable and easy to use OCT systems will be essential to facilitate widespread use at point of care settings while ensuring that they offer the necessary imaging performances needed for clinical detection of retinal pathology. The development of low-cost OCT also offers the potential to enable application in fields outside ophthalmology by lowering the barrier to entry. In this paper, we review the current development and applications of low-cost, portable and handheld OCT in both translational and research settings. Design and cost-reduction techniques are described for general low-cost OCT systems, including considerations regarding spectrometer-based detection, scanning optics, system control, signal processing, and the role of 3D printing technology. Lastly, a review of clinical applications enabled by low-cost OCT is presented, along with a detailed discussion of current limitations and outlook.
Chukwuemeka Ochieze et al 2023 Prog. Biomed. Eng. 5 032003
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.
Mohamed E M K Abdelaziz et al 2021 Prog. Biomed. Eng. 3 032004
Interventional radiology and cardiology are rapidly growing areas of minimally invasive surgery, covering multiple diagnostic and interventional procedures. Treatment via endovascular techniques has become the go-to approach, thanks to its minimally invasive nature and its effectiveness in reducing hospitalisation and total time to recovery when compared to open surgery. Although x-ray fluoroscopy is currently the gold standard imaging technique for endovascular interventions, it presents occupational safety hazards to medical personnel and potential risks to patients, especially paediatric patients, because of its inherent ionising radiation. Magnetic resonance imaging (MRI), with its unique ability to provide radiation-free imaging, and acquiring morphologic and functional information, holds great promise in the advancement of image-guided navigation through the vasculature. Moreover, MRI has the potential to combine diagnosis, therapy and early evaluation of therapy in the same intervention. However, MR-guided interventions face a major challenge due to the presence of a large magnetic field (1.5/3 Tesla), which limits the set of materials suitable for the construction of key instrumentation (sheaths, catheters and guidewires). Despite these challenges, in recent years, significant progress has been made in the development of interventional devices, which comprise biocompatible, MR safe and MR visible materials. In an attempt to encourage and accelerate the development of MR-guided endovascular instrumentation, we present a systematic and illustrated overview of the plethora of work targeting to overcome the aforementioned limitations which are underpinned by the interdependent advancements in science, technology, engineering, mathematics and medicine (STEMM).
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Natarajan Sriraam et al 2024 Prog. Biomed. Eng. 6 023002
For prenatal screening, ultrasound (US) imaging allows for real-time observation of developing fetal anatomy. Understanding normal and aberrant forms through extensive fetal structural assessment enables for early detection and intervention. However, the reliability of anomaly diagnosis varies depending on operator expertise and device limits. First trimester scans in conjunction with circulating biochemical markers are critical in identifying high-risk pregnancies, but they also pose technical challenges. Recent engineering advancements in automated diagnosis, such as artificial intelligence (AI)-based US image processing and multimodal data fusion, are developing to improve screening efficiency, accuracy, and consistency. Still, creating trust in these data-driven solutions is necessary for integration and acceptability in clinical settings. Transparency can be promoted by explainable AI (XAI) technologies that provide visual interpretations and illustrate the underlying diagnostic decision making process. An explanatory framework based on deep learning is suggested to construct charts depicting anomaly screening results from US video feeds. AI modelling can then be applied to these charts to connect defects with probable deformations. Overall, engineering approaches that increase imaging, automation, and interpretability hold enormous promise for altering traditional workflows and expanding diagnostic capabilities for better prenatal care.
Yogendra Pratap Singh et al 2024 Prog. Biomed. Eng. 6 022002
Cartilage repair remains a significant clinical challenge in orthopedics due to its limited self- regeneration potential and often progresses to osteoarthritis which reduces the quality of life. 3D printing/bioprinting has received vast attention in biofabrication of functional tissue substitutes due to its ability to develop complex structures such as zonally structured cartilage and osteochondral tissue as per patient specifications with precise biomimetic control. Towards a suitable bioink development for 3D printing/bioprinting, silk fibroin has garnered much attention due to its advantageous characteristics such as shear thinning behavior, cytocompatibility, good printability, structural fidelity, affordability, and ease of availability and processing. This review attempts to provide an overview of current trends/strategies and recent advancements in utilizing silk-based bioinks/biomaterial-inks for cartilage bioprinting. Herein, the development of silk-based bioinks/biomaterial-inks, its components and the associated challenges, along with different bioprinting techniques have been elaborated and reviewed. Furthermore, the applications of silk-based bioinks/biomaterial-inks in cartilage repair followed by challenges and future directions are discussed towards its clinical translations and production of next-generation biological implants.
Fany Reffuveille et al 2024 Prog. Biomed. Eng. 6 023001
The lack of effective antibiotics for drug-resistant infections has led the World Health Organization to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts. Despite their significant clinical implications, bacterial biofilms have not received the attention they warrant, with no approved antibiotics specifically designed for their eradication. In this paper, we aim to shed light on recent advancements in antibiofilm strategies that offer compelling alternatives to traditional antibiotics. Additionally, we will briefly explore the potential synergy between computational approaches, including the emerging field of artificial intelligence, and the accelerated design and discovery of novel antibiofilm molecules in the years ahead.
Alfonso Maria Ponsiglione et al 2024 Prog. Biomed. Eng. 6 022001
Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.
Sayanti Shome et al 2024 Prog. Biomed. Eng. 6 012004
Platelet rich plasma (PRP) is a suspension of bioactive factors and chemokine enriched plasma. Platelets are a distinctive source of membrane bound and soluble proteins that are released upon their activation. The higher count of platelets renders PRP with an array of tissue regenerative abilities. PRP can be employed in the form of platelet containing plasma, platelet lysate plasma, or in the form of a pre-gelled fibrin matrix. PRP has been an essential alternative source of growth factors in the healing and regeneration of various tissues, such as musculoskeletal, cardiovascular, and dermal tissue, with additional applications in other tissues, such as hepatic and neural. A wide range of preparative and isolation strategies have been developed for various forms of PRP at laboratory and commercial scales. Concomitantly, PRP has found its applicability as an active component in several tissue regenerative approaches, including 3D printed/bioprinted constructs, injectable hydrogels, and crosslinked scaffolds. This review focuses on the various forms of PRP and their preparation methods, the latest tissue engineering applications of PRP, and the various tissue-specific clinical trials and findings conducted using PRP. We have further discussed the optimizations required in the methods of preparation, delivery, and long-term storage of PRP. Therefore, this review seeks to benefit the scope of research on PRP-based therapeutic agents in tissue engineering by providing comprehensive insights into the widespread application. We envisage PRP could be instrumental in future patient-specific tissue engineering applications in both pre-clinical and clinical settings.
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Yogendra Pratap Singh et al 2024 Prog. Biomed. Eng. 6 022002
Cartilage repair remains a significant clinical challenge in orthopedics due to its limited self- regeneration potential and often progresses to osteoarthritis which reduces the quality of life. 3D printing/bioprinting has received vast attention in biofabrication of functional tissue substitutes due to its ability to develop complex structures such as zonally structured cartilage and osteochondral tissue as per patient specifications with precise biomimetic control. Towards a suitable bioink development for 3D printing/bioprinting, silk fibroin has garnered much attention due to its advantageous characteristics such as shear thinning behavior, cytocompatibility, good printability, structural fidelity, affordability, and ease of availability and processing. This review attempts to provide an overview of current trends/strategies and recent advancements in utilizing silk-based bioinks/biomaterial-inks for cartilage bioprinting. Herein, the development of silk-based bioinks/biomaterial-inks, its components and the associated challenges, along with different bioprinting techniques have been elaborated and reviewed. Furthermore, the applications of silk-based bioinks/biomaterial-inks in cartilage repair followed by challenges and future directions are discussed towards its clinical translations and production of next-generation biological implants.
Alfonso Maria Ponsiglione et al 2024 Prog. Biomed. Eng. 6 022001
Simulation models and artificial intelligence (AI) are largely used to address healthcare and biomedical engineering problems. Both approaches showed promising results in the analysis and optimization of healthcare processes. Therefore, the combination of simulation models and AI could provide a strategy to further boost the quality of health services. In this work, a systematic review of studies applying a hybrid simulation models and AI approach to address healthcare management challenges was carried out. Scopus, Web of Science, and PubMed databases were screened by independent reviewers. The main strategies to combine simulation and AI as well as the major healthcare application scenarios were identified and discussed. Moreover, tools and algorithms to implement the proposed approaches were described. Results showed that machine learning appears to be the most employed AI strategy in combination with simulation models, which mainly rely on agent-based and discrete-event systems. The scarcity and heterogeneity of the included studies suggested that a standardized framework to implement hybrid machine learning-simulation approaches in healthcare management is yet to be defined. Future efforts should aim to use these approaches to design novel intelligent in-silico models of healthcare processes and to provide effective translation to the clinics.
Sayanti Shome et al 2024 Prog. Biomed. Eng. 6 012004
Platelet rich plasma (PRP) is a suspension of bioactive factors and chemokine enriched plasma. Platelets are a distinctive source of membrane bound and soluble proteins that are released upon their activation. The higher count of platelets renders PRP with an array of tissue regenerative abilities. PRP can be employed in the form of platelet containing plasma, platelet lysate plasma, or in the form of a pre-gelled fibrin matrix. PRP has been an essential alternative source of growth factors in the healing and regeneration of various tissues, such as musculoskeletal, cardiovascular, and dermal tissue, with additional applications in other tissues, such as hepatic and neural. A wide range of preparative and isolation strategies have been developed for various forms of PRP at laboratory and commercial scales. Concomitantly, PRP has found its applicability as an active component in several tissue regenerative approaches, including 3D printed/bioprinted constructs, injectable hydrogels, and crosslinked scaffolds. This review focuses on the various forms of PRP and their preparation methods, the latest tissue engineering applications of PRP, and the various tissue-specific clinical trials and findings conducted using PRP. We have further discussed the optimizations required in the methods of preparation, delivery, and long-term storage of PRP. Therefore, this review seeks to benefit the scope of research on PRP-based therapeutic agents in tissue engineering by providing comprehensive insights into the widespread application. We envisage PRP could be instrumental in future patient-specific tissue engineering applications in both pre-clinical and clinical settings.
Nandana Bhardwaj et al 2024 Prog. Biomed. Eng. 6 012003
In the past decade, the use of three-dimensional (3D) bioprinting technology for the development of in vitro tissue models has attracted a great deal of attention. This is due to its remarkable precision in constructing different functional tissues and organs, enabling studies of their biology. In addition, this high-throughput technology has been extended to therapeutics, as it provides an alternative functional platform for rapid drug screening and disease modelling. Functional tissue models fabricated using 3D bioprinting mimic native tissues and help in the development of platforms for personalized drug screening and disease modelling due to their high throughput and ease of customization. Moreover, bioprinted 3D tissue models mimic native tissues more closely and provide added advantages over earlier conventional tissue models, such as monoculture, co-culture, explants, etc. In this context, this review article provides an overview of different bioprinted in vitro tissue models of skin, bone, neural tissue, vascular tissue, cartilage, liver and cardiac tissue. This article explores advancements and innovations in these models in terms of developing improved therapeutic interventions. Herein, we provide an insight into the development of different bioprinted tissue models for applications in drug screening and disease modelling. The needs and advantages of bioprinted tissue models as compared with conventional in vitro models are discussed. Furthermore, the different biomaterials, cell sources and bioprinting techniques used to develop tissue models are briefly reviewed. Thereafter, different bioprinted tissue models, namely skin, liver, vascular, cardiac, cartilage, bone and neural tissue, are discussed in detail with a special emphasis on drug screening and disease modelling. Finally, challenges and future prospects are highlighted and discussed. Taken together, this review highlights the different approaches and strategies used for the development of different 3D bioprinted in vitro tissue models for improved therapeutic interventions.
George Ronan et al 2024 Prog. Biomed. Eng. 6 012002
In this article we review the microfabrication approaches, with a focus on bioprinting and organ-on-chip technologies, used to engineer cardiac tissue. First, we give a brief introduction to heart anatomy and physiology, and the developmental stages of the heart from fetal stages to adulthood. We also give information on the cardiac tissue microenvironment, including the cells residing in the heart, the biochemical composition and structural organization of the heart extracellular matrix, the signaling factors playing roles in heart development and maturation, and their interactions with one another. We then give a brief summary of both cardiovascular diseases and the current treatment methods used in the clinic to treat these diseases. Second, we explain how tissue engineering recapitulates the development and maturation of the normal or diseased heart microenvironment by spatially and temporally incorporating cultured cells, biomaterials, and growth factors (GF). We briefly expand on the cells, biomaterials, and GFs used to engineer the heart, and the limitations of their use. Next, we review the state-of-the-art tissue engineering approaches, with a special focus on bioprinting and heart-on-chip technologies, intended to (i) treat or replace the injured cardiac tissue, and (ii) create cardiac disease models to study the basic biology of heart diseases, develop drugs against these diseases, and create diagnostic tools to detect heart diseases. Third, we discuss the recent trends in cardiac tissue engineering, including the use of machine learning, CRISPR/Cas editing, exosomes and microRNAs, and immune modeling in engineering the heart. Finally, we conclude our article with a brief discussion on the limitations of cardiac tissue engineering and our suggestions to engineer more reliable and clinically relevant cardiac tissues.
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Piffer et al
Background: Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources. Researchers are facing with the problem of small datasets and have to apply tricks to fight overfitting.
Methods: 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31 July 2022 and articles were assessed by two independent reviewers. We followed the PRISMA guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review. Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis).
Results: To solve the small data issue transfer learning technique, basic data augmentation and GAN were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a binary classification given the data scarcity and the difficulty of the tasks. Concerning generalizability, only 4 studies explicitly stated an external validation of the developed model was carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD items).
Conclusion: The goal of this review is to provide a comprehensive survey of recent advancements in dealing with small medical images samples size. Transparency and improve quality in publications as well as follow existing reporting standards are also supported.
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Stefano Piffer et al 2024 Prog. Biomed. Eng.
Background: Though medical imaging has seen a growing interest in AI research, training models require a large amount of data. In this domain, there are limited sets of data available as collecting new data is either not feasible or requires burdensome resources. Researchers are facing with the problem of small datasets and have to apply tricks to fight overfitting.
Methods: 147 peer-reviewed articles were retrieved from PubMed, published in English, up until 31 July 2022 and articles were assessed by two independent reviewers. We followed the PRISMA guidelines for the paper selection and 77 studies were regarded as eligible for the scope of this review. Adherence to reporting standards was assessed by using TRIPOD statement (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis).
Results: To solve the small data issue transfer learning technique, basic data augmentation and GAN were applied in 75%, 69% and 14% of cases, respectively. More than 60% of the authors performed a binary classification given the data scarcity and the difficulty of the tasks. Concerning generalizability, only 4 studies explicitly stated an external validation of the developed model was carried out. Full access to all datasets and code was severely limited (unavailable in more than 80% of studies). Adherence to reporting standards was suboptimal (<50% adherence for 13 of 37 TRIPOD items).
Conclusion: The goal of this review is to provide a comprehensive survey of recent advancements in dealing with small medical images samples size. Transparency and improve quality in publications as well as follow existing reporting standards are also supported.
Fany Reffuveille et al 2024 Prog. Biomed. Eng. 6 023001
The lack of effective antibiotics for drug-resistant infections has led the World Health Organization to declare antibiotic resistance a global priority. Most bacterial infections are caused by microbes growing in structured communities called biofilms. Bacteria growing in biofilms are less susceptible to antibiotics than their planktonic counterparts. Despite their significant clinical implications, bacterial biofilms have not received the attention they warrant, with no approved antibiotics specifically designed for their eradication. In this paper, we aim to shed light on recent advancements in antibiofilm strategies that offer compelling alternatives to traditional antibiotics. Additionally, we will briefly explore the potential synergy between computational approaches, including the emerging field of artificial intelligence, and the accelerated design and discovery of novel antibiofilm molecules in the years ahead.
George Ronan et al 2024 Prog. Biomed. Eng. 6 012002
In this article we review the microfabrication approaches, with a focus on bioprinting and organ-on-chip technologies, used to engineer cardiac tissue. First, we give a brief introduction to heart anatomy and physiology, and the developmental stages of the heart from fetal stages to adulthood. We also give information on the cardiac tissue microenvironment, including the cells residing in the heart, the biochemical composition and structural organization of the heart extracellular matrix, the signaling factors playing roles in heart development and maturation, and their interactions with one another. We then give a brief summary of both cardiovascular diseases and the current treatment methods used in the clinic to treat these diseases. Second, we explain how tissue engineering recapitulates the development and maturation of the normal or diseased heart microenvironment by spatially and temporally incorporating cultured cells, biomaterials, and growth factors (GF). We briefly expand on the cells, biomaterials, and GFs used to engineer the heart, and the limitations of their use. Next, we review the state-of-the-art tissue engineering approaches, with a special focus on bioprinting and heart-on-chip technologies, intended to (i) treat or replace the injured cardiac tissue, and (ii) create cardiac disease models to study the basic biology of heart diseases, develop drugs against these diseases, and create diagnostic tools to detect heart diseases. Third, we discuss the recent trends in cardiac tissue engineering, including the use of machine learning, CRISPR/Cas editing, exosomes and microRNAs, and immune modeling in engineering the heart. Finally, we conclude our article with a brief discussion on the limitations of cardiac tissue engineering and our suggestions to engineer more reliable and clinically relevant cardiac tissues.
A Badano et al 2023 Prog. Biomed. Eng. 5 042002
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 and diseased status and examine briefly the role of augmentation methods. Finally, we discuss the trade-offs of four approaches for sampling digital cohorts and the associated potential for study bias with selecting specific patient distributions.
Zahra Miri et al 2023 Prog. Biomed. Eng. 5 042001
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.
Marta M Betcke and Carola-Bibiane Schönlieb 2023 Prog. Biomed. Eng. 5 043002
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.
Bara Levit et al 2023 Prog. Biomed. Eng. 5 043001
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.
S Dietsch et al 2023 Prog. Biomed. Eng. 5 033001
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.
Cristobal Rodero et al 2023 Prog. Biomed. Eng. 5 032004
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.
Chukwuemeka Ochieze et al 2023 Prog. Biomed. Eng. 5 032003
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.