From Wearable Sensors to Smart Implants – Towards Pervasive and Personalised Healthcare

<p>Objective: This article discusses the evolution of pervasive healthcare from its inception for activity recognition using wearable sensors to the future of sensing implant deployment and data processing. Methods: We provide an overview of some of the past milestones and recent developments, categorised into different generations of pervasive sensing applications for health monitoring. This is followed by a review on recent technological advances that have allowed unobtrusive continuous sensing combined with diverse technologies to reshape the clinical workflow for both acute and chronic disease management. We discuss the opportunities of pervasive health monitoring through data linkages with other health informatics systems including the mining of health records, clinical trial databases, multi-omics data integration and social media. Conclusion: Technical advances have supported the evolution of the pervasive health paradigm towards preventative, predictive, personalised and participatory medicine. Significance: The sensing technologies discussed in this paper and their future evolution will play a key role in realising the goal of sustainable healthcare systems.</p> <p> </p

AN AUTOMATED, DEEP LEARNING APPROACH TO SYSTEMATICALLY & SEQUENTIALLY DERIVE THREE-DIMENSIONAL KNEE KINEMATICS DIRECTLY FROM TWO-DIMENSIONAL FLUOROSCOPIC VIDEO

Total knee arthroplasty (TKA), also known as total knee replacement, is a surgical procedure to replace damaged parts of the knee joint with artificial components. It aims to relieve pain and improve knee function. TKA can improve knee kinematics and reduce pain, but it may also cause altered joint mechanics and complications. Proper patient selection, implant design, and surgical technique are important for successful outcomes. Kinematics analysis plays a vital role in TKA by evaluating knee joint movement and mechanics. It helps assess surgery success, guides implant and technique selection, informs implant design improvements, detects problems early, and improves patient outcomes. However, evaluating the kinematics of patients using conventional approaches presents significant challenges. The reliance on 3D CAD models limits applicability, as not all patients have access to such models. Moreover, the manual and time-consuming nature of the process makes it impractical for timely evaluations. Furthermore, the evaluation is confined to laboratory settings, limiting its feasibility in various locations. This study aims to address these limitations by introducing a new methodology for analyzing in vivo 3D kinematics using an automated deep learning approach. The proposed methodology involves several steps, starting with image segmentation of the femur and tibia using a robust deep learning approach. Subsequently, 3D reconstruction of the implants is performed, followed by automated registration. Finally, efficient knee kinematics modeling is conducted. The final kinematics results showed potential for reducing workload and increasing efficiency. The algorithms demonstrated high speed and accuracy, which could enable real-time TKA kinematics analysis in the operating room or clinical settings. Unlike previous studies that relied on sponsorships and limited patient samples, this algorithm allows the analysis of any patient, anywhere, and at any time, accommodating larger subject populations and complete fluoroscopic sequences. Although further improvements can be made, the study showcases the potential of machine learning to expand access to TKA analysis tools and advance biomedical engineering applications

Multi-modal Image Registration

In different areas, particularly medical image analysis, there is a vital need to access and analyse dynamic three dimensional (3D) images of the anatomical structures of the human body. This can enable specialists to track events as well as clinically conduct and evaluate surgical and radio therapeutical procedures. For example, measuring the 3D kinematics of knee joints in a dynamic manner is essential for understanding their normal functions and diagnosing any pathology, such as ligament injury and osteoarthritis. For evaluations of subsequent treatments, such as surgery and rehabilitation, and designs of joint replacements, having knowledge of the movements of knee joints is necessary. Image registration is increasingly being applied to medical image analysis. Whereas in mono-modal registration, the images to be registered are acquired by the same sensor, in multi-modal image registration, they can be taken from different devices or imaging protocols which makes this registration process much more challenging. The invasive or non-invasive nature of the registration method used, the computational time it requires as well as its accuracy and robustness against a large range of initial displacements are the most important features used for its evaluation. As currently available approaches have limited capabilities to register images with large initial displacements and are either not sufficiently accurate or very computationally expensive, the objective of this research is to propose new registration methods, that provide dynamic 3D images, to address these issues. In the first part of this study, I conducted research on registering an individuals’ natural knee bones that can provide 3D information of knee joint kinematics which can be very helpful for improving the accuracy of diagnosis and enabling targeted treatments. A fast, accurate and robust hybrid rigid body registration method based on two different multi-modal similarity measures, the edge position difference (EPD) and sum-of-conditional variance (SCV), is proposed. It uses a gradient descent optimisation technique to register multi-modal images and determine the best transformation parameters. It helps to achieve a trade-off among different challenges, including time complexity, accuracy and robustness against a large range of initial displacements. To evaluate it, several experiments were performed on two different databases: one collected from the knee bones of four patients and the other from three knee cadavers installed on a mechanical positioning system, with the results showing that this method is accurate, fast and robust against large initial displacement. Then, I conducted research on registering implanted human knee joints and proposed a non-invasive, robust 3D-to-2D registration method which can be used for 3D evaluations of the status of knee implants after joint replacement surgeries. In this method, 3D models of the implants for an individual with the relevant post-operative fluoroscopy frames are able to be used in the registration process. As a result, it is possible to perform 3D analysis at any time after a surgery by simply taking single-plane radiographs. This approach uses the EPD multi-modal similarity measure together with a steepest descent optimisation method. It applies coarse-to-fine registration steps to determine the transformation parameters that lead to the best alignment between the model used and X-ray images to be registered. The experimental results showed that not only does the proposed registration method have a high success rate but that it is also much faster than the most relevant competitive approach. Although the experiments were designed for a 3D analysis of total knee arthroplasty (TKA) components, this proposed method can be applied to other joints such as the ankle or hip. In the final part of my research, I developed a multi-frame 2D fluoroscopy to 3D model registration method for measuring the kinematics of post-operative knee joints. It uses a coarse-to-fine approach and applies the normalised EPD (NEPD) and SCV similarity measures together with a gradient descent optimisation method and an interpolation estimation one. In order to measure the kinematics of post- operative knee joints, after a TKA surgery, a 3D knee implant model can be registered with a number of single-plane fluoroscopy frames of the patient’s knee. Generally, when this number is quite high, the computational cost for registering the frames and a 3D model is expensive. Therefore, in order to speed up the registration process, a cubic spline interpolation prediction method is applied to initialise and estimate the 3D positions of the 3D model in each fluoroscopy frame instead of applying a registration algorithm on all the frames, one after the other. The estimated 3D positions are then tuned using a registration improvement step. The experimental results demonstrated that the proposed registration method is much faster than the best existing one and achieves almost the same accuracy. It also provides smooth registration results which can lead to more natural 3D modelling of joint movements

1st EFORT European Consensus: Medical & Scientific Research Requirements for the Clinical Introduction of Artificial Joint Arthroplasty Devices

Innovations in Orthopaedics and Traumatology have contributed to the achievement of a high-quality level of care in musculoskeletal disorders and injuries over the past decades. The applications of new implants as well as diagnostic and therapeutic techniques in addition to implementation of clinical research, have significantly improved patient outcomes, reduced complication rates and length of hospital stay in many areas. However, the regulatory framework is extensive, and there is a lack of understanding and clarity in daily practice what the meaning of clinical &amp; pre‐clinical evidence as required by the MDR is. Thus, understanding and clarity are of utmost importance for introduction of new implants and implant-related instrumentation in combination with surgical technique to ensure a safe use of implants and treatment of patients. Therefore EFORT launched IPSI, The Implant and Patient Safety Initiative, which starting from an inaugural workshop in 2021 issued a set of recommendations, notably through a subsequent Delphi Process involving the National Member Societies of EFORT, European Specialty Societies as well as International Experts. These recommendations provide surgeons, researchers, implant manufacturers as well as patients and health authorities with a consensus of the development, implementation, and dissemination of innovation in the field of arthroplasty. The intended key outcomes of this 1st EFORT European Consensus on “Medical &amp; Scientific Research Requirements for the Clinical Introduction of Artificial Joint Arthroplasty Devices”are consented, practical pathways to maintain innovation and optimisation of orthopaedic products and workflows within the boundaries of MDR 2017/745. Open Access practical guidelines based on adequate, state of the art pre-clinical and clinical evaluation methodologies for the introduction of joint replacements and implant-related instrumentation shall provide hands-on orientation for orthopaedic surgeons, research institutes and laboratories, orthopaedic device manufacturers, Notified Bodies but also for National Institutes and authorities, patient representatives and further stakeholders. We would like to acknowledge and thank the Scientific Committee members, all International Expert Delegates, the Delegates from European National &amp; Specialty Societies and the Editorial Team for their outstanding contributions and support during this EFORT European Consensus

Total knee replacements: design and pre-clinical testing methods

Total knee replacement (TKR) is a common and successful treatment for severe osteoarthritis of the knee. However, a large minority of people remain dissatisfied after the operation, despite adequate pain relief. Over 50 designs of TKR are used in the UK each year, but differentiating between these devices in terms of patient function and making the right choice for each patient remains challenging. The aim of this research was to characterise designs of TKR in the laboratory, using pre-clinical testing methods, in order to better understand TKR function, and make suggestions for improved implant design and testing. Conventional, medial-pivot, guided-motion and bicruciate retaining (BCR) TKRs were tested. Standard ASTM test methods used for CE-marking purposes were demonstrated to differentiate between devices, but did not produce enough information to adequately understand how a new device will behave clinically, or what the potential benefits of a new device would be to patients. Guided-motion devices are meant to replicate normal knee motion, but there has been concern that they might cause too much rotation of the knee, leading to anterolateral knee pain. Results from cadaveric testing suggest that they do not adequately mimic normal knee motion and small design changes may have little impact on performance. A BCR TKR, designed to improve stability in the replaced knee joint, was also tested. Knee kinematics were measured for three design phases and surgical feasibility was also assessed for this more complicated procedure. BCR TKR was shown to lead to more normal levels of anteroposterior tibiofemoral laxity, compared to a conventional, anterior-cruciate-ligament-sacrificing TKR. Inherent variability between people’s anatomy and osteoarthritis pathology suggests there will never be a single, perfect, TKR, but more comprehensive pre-clinical testing could improve the regulatory approval process and inform better device selection, leading to improved patient outcomes.Open Acces

Patient-Specific Implants in Musculoskeletal (Orthopedic) Surgery

Most of the treatments in medicine are patient specific, aren’t they? So why should we bother with individualizing implants if we adapt our therapy to patients anyway? Looking at the neighboring field of oncologic treatment, you would not question the fact that individualization of tumor therapy with personalized antibodies has led to the thriving of this field in terms of success in patient survival and positive responses to alternatives for conventional treatments. Regarding the latest cutting-edge developments in orthopedic surgery and biotechnology, including new imaging techniques and 3D-printing of bone substitutes as well as implants, we do have an armamentarium available to stimulate the race for innovation in medicine. This Special Issue of Journal of Personalized Medicine will gather all relevant new and developed techniques already in clinical practice. Examples include the developments in revision arthroplasty and tumor (pelvic replacement) surgery to recreate individual defects, individualized implants for primary arthroplasty to establish physiological joint kinematics, and personalized implants in fracture treatment, to name but a few