CAD-Based Porous Scaffold Design of Intervertebral Discs in Tissue Engineering

With the development and maturity of three-dimensional (3D) printing technology over the past decade, 3D printing has been widely investigated and applied in the field of tissue engineering to repair damaged tissues or organs, such as muscles, skin, and bones, Although a number of automated fabrication methods have been developed to create superior bio-scaffolds with specific surface properties and porosity, the major challenges still focus on how to fabricate 3D natural biodegradable scaffolds that have tailor properties such as intricate architecture, porosity, and interconnectivity in order to provide the needed structural integrity, strength, transport, and ideal microenvironment for cell- and tissue-growth. In this dissertation, a robust pipeline of fabricating bio-functional porous scaffolds of intervertebral discs based on different innovative porous design methodologies is illustrated. Firstly, a triply periodic minimal surface (TPMS) based parameterization method, which has overcome the integrity problem of traditional TPMS method, is presented in Chapter 3. Then, an implicit surface modeling (ISM) approach using tetrahedral implicit surface (TIS) is demonstrated and compared with the TPMS method in Chapter 4. In Chapter 5, we present an advanced porous design method with higher flexibility using anisotropic radial basis function (ARBF) and volumetric meshes. Based on all these advanced porous design methods, the 3D model of a bio-functional porous intervertebral disc scaffold can be easily designed and its physical model can also be manufactured through 3D printing. However, due to the unique shape of each intervertebral disc and the intricate topological relationship between the intervertebral discs and the spine, the accurate localization and segmentation of dysfunctional discs are regarded as another obstacle to fabricating porous 3D disc models. To that end, we discuss in Chapter 6 a segmentation technique of intervertebral discs from CT-scanned medical images by using deep convolutional neural networks. Additionally, some examples of applying different porous designs on the segmented intervertebral disc models are demonstrated in Chapter 6

Statistical shape model reconstruction with sparse anomalous deformations: application to intervertebral disc herniation

Many medical image processing techniques rely on accurate shape modeling of anatomical features. The presence of shape abnormalities challenges traditional processing algorithms based on strong morphological priors. In this work, a sparse shape reconstruction from a statistical shape model is presented. It combines the advantages of traditional statistical shape models (defining a ‘normal’ shape space) and previously presented sparse shape composition (providing localized descriptors of anomalies). The algorithm was incorporated into our image segmentation and classification software. Evaluation was performed on simulated and clinical MRI data from 22 sciatica patients with intervertebral disc herniation, containing 35 herniated and 97 normal discs. Moderate to high correlation (R = 0.73) was achieved between simulated and detected herniations. The sparse reconstruction provided novel quantitative features describing the herniation morphology and MRI signal appearance in three dimensions (3D). The proposed descriptors of local disc morphology resulted to the 3D segmentation accuracy of 1.07 ± 1.00 mm (mean absolute vertex-to-vertex mesh distance over the posterior disc region), and improved the intervertebral disc classification from 0.888 to 0.931 (area under receiver operating curve). The results show that the sparse shape reconstruction may improve computer-aided diagnosis of pathological conditions presenting local morphological alterations, as seen in intervertebral disc herniation

Machine Learning towards General Medical Image Segmentation

The quality of patient care associated with diagnostic radiology is proportionate to a physician\u27s workload. Segmentation is a fundamental limiting precursor to diagnostic and therapeutic procedures. Advances in machine learning aims to increase diagnostic efficiency to replace single applications with generalized algorithms. We approached segmentation as a multitask shape regression problem, simultaneously predicting coordinates on an object\u27s contour while jointly capturing global shape information. Shape regression models inherent point correlations to recover ambiguous boundaries not supported by clear edges and region homogeneity. Its capabilities was investigated using multi-output support vector regression (MSVR) on head and neck (HaN) CT images. Subsequently, we incorporated multiplane and multimodality spinal images and presented the first deep learning multiapplication framework for shape regression, the holistic multitask regression network (HMR-Net). MSVR and HMR-Net\u27s performance were comparable or superior to state-of-the-art algorithms. Multiapplication frameworks bridges any technical knowledge gaps and increases workflow efficiency

AI MSK clinical applications: spine imaging

Recent investigations have focused on the clinical application of artificial intelligence (AI) for tasks specifically addressing the musculoskeletal imaging routine. Several AI applications have been dedicated to optimizing the radiology value chain in spine imaging, independent from modality or specific application. This review aims to summarize the status quo and future perspective regarding utilization of AI for spine imaging. First, the basics of AI concepts are clarified. Second, the different tasks and use cases for AI applications in spine imaging are discussed and illustrated by examples. Finally, the authors of this review present their personal perception of AI in daily imaging and discuss future chances and challenges that come along with AI-based solutions

CT Scanning

Since its introduction in 1972, X-ray computed tomography (CT) has evolved into an essential diagnostic imaging tool for a continually increasing variety of clinical applications. The goal of this book was not simply to summarize currently available CT imaging techniques but also to provide clinical perspectives, advances in hybrid technologies, new applications other than medicine and an outlook on future developments. Major experts in this growing field contributed to this book, which is geared to radiologists, orthopedic surgeons, engineers, and clinical and basic researchers. We believe that CT scanning is an effective and essential tools in treatment planning, basic understanding of physiology, and and tackling the ever-increasing challenge of diagnosis in our society

IMAGE ANALYSIS FOR SPINE SURGERY: DATA-DRIVEN DETECTION OF SPINE INSTRUMENTATION & AUTOMATIC ANALYSIS OF GLOBAL SPINAL ALIGNMENT

Spine surgery is a therapeutic modality for treatment of spine disorders, including spinal deformity, degeneration, and trauma. Such procedures benefit from accurate localization of surgical targets, precise delivery of instrumentation, and reliable validation of surgical objectives – for example, confirming that the surgical implants are delivered as planned and desired changes to the global spinal alignment (GSA) are achieved. Recent advances in surgical navigation have helped to improve the accuracy and precision of spine surgery, including intraoperative imaging integrated with real-time tracking and surgical robotics. This thesis aims to develop two methods for improved image-guided surgery using image analytic techniques. The first provides a means for automatic detection of pedicle screws in intraoperative radiographs – for example, to streamline intraoperative assessment of implant placement. The algorithm achieves a precision and recall of 0.89 and 0.91, respectively, with localization accuracy within ~10 mm. The second develops two algorithms for automatic assessment of GSA in computed tomography (CT) or cone-beam CT (CBCT) images, providing a means to quantify changes in spinal curvature and reduce the variability in GSA measurement associated with manual methods. The algorithms demonstrate GSA estimates with 93.8% of measurements within a 95% confidence interval of manually defined truth. Such methods support the goals of safe, effective spine surgery and provide a means for more quantitative intraoperative quality assurance. In turn, the ability to quantitatively assess instrument placement and changes in GSA could represent important elements of retrospective analysis of large image datasets, improved clinical decision support, and improved patient outcomes

Low Back Pain (LBP)

Low back pain (LBP) is a major public health problem, being the most commonly reported musculoskeletal disorder (MSD) and the leading cause of compromised quality of life and work absenteeism. Indeed, LBP is the leading worldwide cause of years lost to disability, and its burden is growing alongside the increasing and aging population. The etiology, pathogenesis, and occupational risk factors of LBP are still not fully understood. It is crucial to give a stronger focus to reducing the consequences of LBP, as well as preventing its onset. Primary prevention at the occupational level remains important for highly exposed groups. Therefore, it is essential to identify which treatment options and workplace-based intervention strategies are effective in increasing participation at work and encouraging early return-to-work to reduce the consequences of LBP. The present Special Issue offers a unique opportunity to update many of the recent advances and perspectives of this health problem. A number of topics will be covered in order to attract high-quality research papers, including the following major areas: prevalence and epidemiological data, etiology, prevention, assessment and treatment approaches, and health promotion strategies for LBP. We have received a wide range of submissions, including research on the physical, psychosocial, environmental, and occupational perspectives, also focused on workplace interventions

Immunotherapy as a novel therapeutic approach for intervertebral disc herniation: an in vitro study

A dor lombar é um sintoma prevalente na população, pois aproximadamente 80% da mesma sofre um episódio pelo menos uma vez na vida. A hérnia discal é relativamente comum, com 5 a 20 casos por 1.000 adultos anualmente. Os tratamentos conservador e cirúrgico têm demonstrado recentemente resultados equivalentes a médio e longo prazo. Além disso, o tratamento inadequado pode provocar danos irreversíveis duradouros nos nervos e dor neuropática nos pacientes com compressão severa da raiz do nervo. A terapia proposta nesta tese, é uma tentativa de abordagem de um tratamento menos invasivo que a cirurgia, mas mais rápido em termos de recuperação em comparação com o tratamento conservador, utilizando as propriedades naturais das células imunes do corpo humano para acelerar a reabsorção do disco intervertebral herniado. Para que esta terapia surja como uma solução possível e viável, primeiramente avaliamos as diferentes caracterizações dos macrófagos, que são os principais atores da reabsorção da hérnia discal, e avaliámos o impacto de diferentes polarizações no poder fagocítico destas células. Com o intuito de escolher os fenótipos ideais para dar um passo em frente no desenvolvimento desta terapia, também comparámos as diferentes polarizações em coculturas indiretas com tecido herniado para compreender o comportamento das diferentes populações de macrófagos no contexto do microambiente da hérnia discal. No final, os fenótipos escolhidos, foram: “M1sLPS”, “M2a”, e “M2c” com a finalidade de melhor compreender e caracterizar as populações escolhidas, e avaliar mais aprofundadamente num futuro próximo o contato destes fenótipos com as amostras de hérnia, possivelmente em contato direto.Low back pain is a prevalent symptom, as approximately 80% of the population sustains an episode once in their lifetime. Disc degeneration is usually associated with disc herniation. Lumbar disc herniation isrelatively common, with 5 to 20 cases per 1000 adults annually. Over 85 to 90% of patients with an acute herniated disc experience relief of symptoms within 6 to 12 weeks without any treatments. Conservative and surgical treatment have recently demonstrated equivalent outcomes in the medium and long term. Furthermore, inadequate treatment can lead to lasting irreversible nerve damage and neuropathic pain in patients with severe nerve root compression. The therapy proposed in this thesis, is an attempt as an approach to a less invasive treatment than surgery, but faster in terms of recovery in comparison to the conservative treatment, using the natural properties of the immune cells from the human body to accelerate the resorption of the herniated IVD. In order for this therapy to emerge as a possible and feasible solution, firstly we evaluated the different characterizations of the macrophages, the main actors of the hernia resorption, and evaluated the impact of different polarizations in the phagocytic power of these cells. In order to choose the ideal phenotypes to take a step further in the development of this therapy, we also compared the different polarizations in indirect cocultures in order to better understand the behaviour of the different macrophage populations within the hernia tissue microenvironment. In the end, the phenotypes chosen, were: “M1sLPS”, “M2a” and “M2c”, with the purpose of further understanding and characterizing the populations chosen, and evaluate further in contact with the hernia samples, possibly in direct contact

Machine learning in orthopedics: a literature review

In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles\u2019 content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance

The safety and efficacy of mesenchymal stem cells for prevention or regeneration of intervertebral disc degeneration: a systematic review

General Posters: abstract no. GP86INTRODUCTION: Mesenchymal stem cells (MSCs) have been used to halt the progression or regenerate the disc with hopes to prevent or treat discogenic back pain. However, the safety and efficacy of the use of MSCs for such treatment in animal and human models at short and long term assessment (i.e. greater than 48 weeks) have not been systematically addressed. This study addressed a systematic review of comparative controlled studies addressing the use of MSCs to that of no treatment/saline for the treatment of disc degeneration. METHODS: Online databases were extensively searched. Controlled trials in animal models and humans were eligible for inclusion. Trial design, MSC characteristics, injection method, disc assessment, outcome intervals, and complication events were assessed. Validity of each study was assessed addressing trial design. Two individuals independently addressed the aforementioned. RESULTS: Twenty-two animal studies were included. No human comparative controlled trials were reported. All three types of MSCs (i.e. derived from bone marrow, synovial and adipose tissue) showed successful inhibition of disc degeneration progression. From three included studies, bone marrow derived MSC showed superior quality of disc repair when compared to other treatments, including TGF-β1, NP bilaminar co-culture and axial distraction regimen. However, osteophyte development was reported in two studies as potential complication of MSC transplantation. CONCLUSIONS: Based on animal models, the current evidence suggests that in the short-term MSC transplantation is safe and effective in halting disc degeneration; however, additional and larger studies are needed to assess the long-term regenerative effects and potential complications. Inconsistency in methodological design and outcome parameters prevent any robust conclusions. In addition, randomized controlled trials in humans are needed to assess the safety and efficacy of such therapy.published_or_final_versio