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Featured Application: The inclusion of gait analysis, suitably combined with anatomical modelling derived from patient-specific medical imaging, may lead in the near future to more robust post-operative evaluations of personalized HTO to support the efficacy of this surgical technique, as well as innovative and more biomechanically based pre-operative planning. High tibial osteotomy (HTO) is intended to treat medial knee osteoarthritis by realigning the joint such that the loading in the knee during functional activity shifts laterally. The aim of this study was to use a novel methodology combining motion analysis and 3D modelling to assess the efficacy of this surgery in changing the loading location in the knee in a cohort of 25 patients treated with personalized HTO. Pre-operatively and at 6 months post-surgery, weight-bearing CT and gait analysis during level walking were performed on all patients, as well as clinical evaluations using KOOS and VAS scores. CT scans were used to generate a knee bone model and a virtual tibial plateau plane; the intersection pattern between this plane and the ground reaction force (GRF) vector was calculated in the pre- and post-operative gait analyses. Clinical scores improved significantly (p < 0.001) after surgery (pre-/post-operative KOOS and VAS: 56.2 ± 14.0/82.0 ± 8.3 and 6.3 ± 1.7/1.5 ± 1.7). Post-operative GRF-to-tibial plateau intersection patterns were significantly (p < 0.001) more lateral (31.9 ± 19.8% of tibial plateau width) than the pre-operative patterns. Personalized HTO successfully and consistently lateralizes the GRF at the knee, in association with significant improvements in function and pain. The novel combination of 3D bone modelling and motion analysis also has the potential to further aid HTO surgical planning. [ABSTRACT FROM AUTHOR]

High Tibial Osteotomy is frequently performed to correct varus knees misalignment and thus to prevent end-stage osteoarthritis. Traditional systems lack pre-surgical planning and custom-fit fixation plates. A new 3D printed system has been developed for a personalized surgical procedure. This starts with careful correction planning based on a standard preoperative long leg radiograph and a 3D scan of the knee by Cone-Beam CT, both in weight-bearing. From the latter, a 3D model of the proximal tibia is reconstructed, on which the surgery is planned. This allows the design of the surgical guide and fixation plate to match the tibial surface topology and 3D printed in medical grade titanium alloy using selective-laser-sintering. During surgery, the guided osteotomy and controlled opening mechanism ensure an accurate correction; this is stabilized with the custom-fit plate secured to the proximal tibia using locking screws of appropriate length. After a brief learning curve, the mean discrepancy between the plan and the achieved alignment was 1. 2 ∘ ± 1. 4 ∘ . The surgical time was reduced by an average of approximately 30%. From medical imaging of the patient to product delivery to the hospital, the overall timeframe was about 15 days. [ABSTRACT FROM AUTHOR]

High tibial osteotomy (HTO) is intended to treat medial knee osteoarthritis by realigning the joint such that the loading in the knee during functional activity shifts laterally. The aim of this study was to use a novel methodology combining motion analysis and 3D modelling to assess the efficacy of this surgery in changing the loading location in the knee in a cohort of 25 patients treated with personalized HTO. Pre-operatively and at 6 months post-surgery, weight-bearing CT and gait analysis during level walking were performed on all patients, as well as clinical evaluations using KOOS and VAS scores. CT scans were used to generate a knee bone model and a virtual tibial plateau plane; the intersection pattern between this plane and the ground reaction force (GRF) vector was calculated in the pre- and post-operative gait analyses. Clinical scores improved significantly (p < 0.001) after surgery (pre-/post-operative KOOS and VAS: 56.2 ± 14.0/82.0 ± 8.3 and 6.3 ± 1.7/1.5 ± 1.7). Post-operative GRF-to-tibial plateau intersection patterns were significantly (p < 0.001) more lateral (31.9 ± 19.8% of tibial plateau width) than the pre-operative patterns. Personalized HTO successfully and consistently lateralizes the GRF at the knee, in association with significant improvements in function and pain. The novel combination of 3D bone modelling and motion analysis also has the potential to further aid HTO surgical planning.

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics. [ABSTRACT FROM AUTHOR]

Personalization of gait neuroprosthetics is paramount to ensure their efficacy for users, who experience severe limitations in mobility without an assistive device. Our goal is to develop assistive devices that collaborate with and are tailored to their users, while allowing them to use as much of their existing capabilities as possible. Currently, personalization of devices is challenging, and technological advances are required to achieve this goal. Therefore, this paper presents an overview of challenges and research directions regarding an interface with the peripheral nervous system, an interface with the central nervous system, and the requirements of interface computing architectures. The interface should be modular and adaptable, such that it can provide assistance where it is needed. Novel data processing technology should be developed to allow for real-time processing while accounting for signal variations in the human. Personalized biomechanical models and simulation techniques should be developed to predict assisted walking motions and interactions between the user and the device. Furthermore, the advantages of interfacing with both the brain and the spinal cord or the periphery should be further explored. Technological advances of interface computing architecture should focus on learning on the chip to achieve further personalization. Furthermore, energy consumption should be low to allow for longer use of the neuroprosthesis. In-memory processing combined with resistive random access memory is a promising technology for both. This paper discusses the aforementioned aspects to highlight new directions for future research in gait neuroprosthetics.

Deep learning (DL) application has demonstrated its enormous potential in accomplishing biomedical tasks, such as vessel segmentation, brain visualization, and speech recognition. This review article has mainly covered recent advances in the principles of DL algorithms, existing DL software, and designing strategies of DL models. Latest progresses in cardiovascular devices, especially DL-based cardiovascular stent used for angioplasty, differential and advanced diagnostic means, and the treatment outcomes involved with coronary artery disease (CAD), are discussed. Also presented is DL-based discovery of new materials and future medical technologies that will facilitate the development of tailored and personalized treatment strategies by identifying and forecasting individual impending risks of cardiovascular diseases. [ABSTRACT FROM AUTHOR]

Introduction: As Additive Manufacturing (AM) in the health sector evolves to the point where products can be translated into the clinic, these manufactured goods need to be assessed by regulators in order for such products to be manufactured, sold, and used in accordance with the law. In this article, the authors argue that if AM products in the health sector are to be regulated in the near future, stakeholders involved in translational research need to understand the challenges faced by both regulators and industry. We portray different points of possible dissonance for AM medical products with existing regulatory frameworks. Hence, we advocate for stakeholders to proactively provide solutions for regulatory processes for products emerging from AM in the health sector. Areas covered: The publication discusses the need for clear definitions and standards to enable translation of AM research into the health sector. Key literature around legal and regulatory challenges applicable to this topic was synthesized. Expert opinion: We argue that stakeholders need to develop regulatory-rooted risk profiles of the respective AM medical products. The terminology must be defined clearly and used consistently. Standards need to be designed for the purpose of advancing regulatory processes. [ABSTRACT FROM AUTHOR]

Advances in genomic sequencing and bioinformatics have led to the prospect of precision medicine where therapeutics can be advised by the genetic background of individuals. For example, mapping cancer genomics has revealed numerous genes that affect the therapeutic outcome of a drug. Through materials and cell engineering, many opportunities exist for engineers to contribute to precision medicine, such as engineering biosensors for diagnosis and health status monitoring, developing smart formulations for the controlled release of drugs, programming immune cells for targeted cancer therapy, differentiating pluripotent stem cells into desired lineages, fabricating bioscaffolds that support cell growth, or constructing "organs‐on‐chips" that can screen the effects of drugs. Collective engineering efforts will help transform precision medicine into a more personalized and effective healthcare approach. As continuous progress is made in engineering techniques, more tools will be available to fully realize precision medicine's potential. Engineering can contribute to precision medicine by providing individualized health care advices or services that could complement the genomic profiling‐based method. Engineering approaches, including personalized immune cell engineering, personalized stem cell engineering, smart drug delivery systems, personalized tissue scaffold engineering, personalized implants, wearable devices, point of care devices, and organs‐on‐chips, are promising in providing personalized healthcare services or advices. [ABSTRACT FROM AUTHOR]

Each person's response to the therapies is different. In article number 1801039, Ali Khademhosseini and co‐workers discuss engineering approaches for precision medicine. Healthcare could be individualized through personalized cells therapies, controlled drug delivery systems, personalized scaffolds and implants, wearable sensors, point‐of‐care devices, as well as organs‐on‐chip systems. [ABSTRACT FROM AUTHOR]

Advances in genomic sequencing and bioinformatics have led to the prospect of precision medicine where therapeutics can be advised by the genetic background of individuals. For example, mapping cancer genomics has revealed numerous genes that affect the therapeutic outcome of a drug. Through materials and cell engineering, many opportunities exist for engineers to contribute to precision medicine, such as engineering biosensors for diagnosis and health status monitoring, developing smart formulations for the controlled release of drugs, programming immune cells for targeted cancer therapy, differentiating pluripotent stem cells into desired lineages, fabricating bioscaffolds that support cell growth, or constructing "organs-on-chips" that can screen the effects of drugs. Collective engineering efforts will help transform precision medicine into a more personalized and effective healthcare approach. As continuous progress is made in engineering techniques, more tools will be available to fully realize precision medicine's potential.