Applied AI/ML for automatic customisation of medical implants

Most knee replacement surgeries are performed using ‘off-the-shelf’ implants, supplied with a set number of standardised sizes. X-rays are taken during pre-operative assessment and used by clinicians to estimate the best options for patients. Manual templating and implant size selection have, however, been shown to be inaccurate, and frequently the generically shaped products do not adequately fit patients’ unique anatomies. Furthermore, off-the-shelf implants are typically made from solid metal and do not exhibit mechanical properties like the native bone. Consequently, the combination of these factors often leads to poor outcomes for patients. Various solutions have been outlined in the literature for customising the size, shape, and stiffness of implants for the specific needs of individuals. Such designs can be fabricated via additive manufacturing which enables bespoke and intricate geometries to be produced in biocompatible materials. Despite this, all customisation solutions identified required some level of manual input to segment image files, identify anatomical features, and/or drive design software. These tasks are time consuming, expensive, and require trained resource. Almost all currently available solutions also require CT imaging, which adds further expense, incurs high levels of potentially harmful radiation, and is not as commonly accessible as X-ray imaging. This thesis explores how various levels of knee replacement customisation can be completed automatically by applying artificial intelligence, machine learning and statistical methods. The principal output is a software application, believed to be the first true ‘mass-customisation’ solution. The software is compatible with both 2D X-ray and 3D CT data and enables fully automatic and accurate implant size prediction, shape customisation and stiffness matching. It is therefore seen to address the key limitations associated with current implant customisation solutions and will hopefully enable the benefits of customisation to be more widely accessible.Open Acces

RELATIONSHIPS OF LONG-TERM BISPHOSPHONATE TREATMENT WITH MEASURES OF BONE MICROARCHITECTURE AND MECHANICAL COMPETENCE

Oral bisphosphonate drug therapy is a common and effective treatment for osteoporosis. Little is known about the long-term effects of bisphosphonates on bone quality. This study examined the structural and mechanical properties of trabecular bone following 0-16 years of bisphosphonate treatment. Fifty-three iliac crest bone samples of Caucasian women diagnosed with low turnover osteoporosis were identified from the Kentucky Bone Registry. Forty-five were treated with oral bisphosphonates for 1 to 16 years while eight were treatment naive. A section of trabecular bone was chosen from a micro-computed tomography (Scanco µCT 40) scan of each sample for a uniaxial linearly elastic compression simulation using finite element analysis (ANSYS 14.0). Morphometric parameters (BV/TV, SMI, Tb.Sp., etc.) were computed using µCT. Apparent modulus, effective modulus and estimated failure stress were calculated. Biomechanical and morphometric parameters improved with treatment duration, peaked around 7 years, and then declined independently of age. The findings suggest a limit to the benefits associated with bisphosphonate treatment and that extended continuous bisphosphonate treatment does not continue to improve bone quality. Bone quality, and subsequently bone strength, may eventually regress to a state poorer than at the onset of treatment. Treatment duration limited to less than 7 years is recommended

Anatomical shape reconstruction and manufacturing: solving topological changes of lumen vessel trough geometric approach

Over the last years there has been an increasing growth of interest in Rapid Prototyping (RP) techniques applied to various fields of medicine. RP makes it possible, in vascular surgery, to produce accurate anatomic replicas of patient vessels. These replicas can help the customization of surgical invasive interventions such as in situ stent-graft insertion in carotid region. The main goal of this work is to obtain high quality in lumen reconstruction and manufacturing replicas by RP technique. This goal is achieved through the complete control of each phase of the generating process. We present a semi-automatic method for carotid lumen reconstruction based on Boundary Representation (BRep). All parameters influencing the quality of the shape reconstruction are presented and discussed: shape acquisition, shape reconstruction and shape manufacturing. The shape acquisition starts by extracting the points belonging to the boundary of the lumen vessel, from Computer Tomography (CT) images. These points, parameterised in a vector, are the input data of the shape reconstruction algorithm based on B-Spline interpolation. The B-Spline type for representing curves and surfaces were chosen because of their properties of continuity and local control. In the shape reconstruction stage we had to face problems due to the topological change on the vessel structure. For vessel regions where there are not changes of topology, we use the closed B-Spline curves (belonging to adjacent acquisition planes) as generating curves to build a B-Spline surface. For vessel regions with at least a change of topology (ex. bifurcation region) our algorithm split automatically the involved curves to obtain three rectangular B-Spline patches. Such patches are joined together to obtain the bifurcation vessel lumen. The set of lumen surfaces is then inserted in a Boundary Representation in order to get a valid solid. To analyse the quality of the reconstructed shapes, the final object is compared with the acquisition image. This solid is correctly tessellated in triangles to produce the data format used by the RP devices (STL)

Digital process management for the integrated bending of thermoplastic CFRP tapes

A bending based approach for the preforming of carbon fiber reinforced thermoplastic (CFRTP) tapes has been implemented at wbk Institute of Production Science. In early experimentation, it became evident that the process requires regular adaption of process parameters in order to meet the desired quality level. To limit the manual effort, the parameter variation and process characterization should be automated. In this paper, an approach for the automatic parameter identification and tuning through systematic variation combined with optical process measurement is presented. The functionality of the measurement system as well as the parameter identification and error compensation are described

In-Situ Defect Detection in Laser Powder Bed Fusion by Using Thermography and Optical Tomography—Comparison to Computed Tomography

Among additive manufacturing (AM) technologies, the laser powder bed fusion (L-PBF) is one of the most important technologies to produce metallic components. The layer-wise build-up of components and the complex process conditions increase the probability of the occurrence of defects. However, due to the iterative nature of its manufacturing process and in contrast to conventional manufacturing technologies such as casting, L-PBF offers unique opportunities for in-situ monitoring. In this study, two cameras were successfully tested simultaneously as a machine manufacturer independent process monitoring setup: a high-frequency infrared camera and a camera for long time exposure, working in the visible and infrared spectrum and equipped with a near infrared filter. An AISI 316L stainless steel specimen with integrated artificial defects has been monitored during the build. The acquired camera data was compared to data obtained by computed tomography. A promising and easy to use examination method for data analysis was developed and correlations between measured signals and defects were identified. Moreover, sources of possible data misinterpretation were specified. Lastly, attempts for automatic data analysis by data integration are presented

Vision-based techniques for gait recognition

Global security concerns have raised a proliferation of video surveillance devices. Intelligent surveillance systems seek to discover possible threats automatically and raise alerts. Being able to identify the surveyed object can help determine its threat level. The current generation of devices provide digital video data to be analysed for time varying features to assist in the identification process. Commonly, people queue up to access a facility and approach a video camera in full frontal view. In this environment, a variety of biometrics are available - for example, gait which includes temporal features like stride period. Gait can be measured unobtrusively at a distance. The video data will also include face features, which are short-range biometrics. In this way, one can combine biometrics naturally using one set of data. In this paper we survey current techniques of gait recognition and modelling with the environment in which the research was conducted. We also discuss in detail the issues arising from deriving gait data, such as perspective and occlusion effects, together with the associated computer vision challenges of reliable tracking of human movement. Then, after highlighting these issues and challenges related to gait processing, we proceed to discuss the frameworks combining gait with other biometrics. We then provide motivations for a novel paradigm in biometrics-based human recognition, i.e. the use of the fronto-normal view of gait as a far-range biometrics combined with biometrics operating at a near distance

Analysis, Segmentation and Prediction of Knee Cartilage using Statistical Shape Models

Osteoarthritis (OA) of the knee is one of the leading causes of chronic disability (along with the hip). Due to rising healthcare costs associated with OA, it is important to fully understand the disease and how it progresses in the knee. One symptom of knee OA is the degeneration of cartilage in the articulating knee. The cartilage pad plays a major role in painting the biomechanical picture of the knee. This work attempts to quantify the cartilage thickness of healthy male and female knees using statistical shape models (SSMs) for a deep knee bend activity. Additionally, novel cartilage segmentation from magnetic resonance imaging (MRI) and estimation algorithms from computer tomography (CT) or x-rays are proposed to facilitate the efficient development and accurate analysis of future treatments related to the knee. Cartilage morphology results suggest distinct patterns of wear in varus, valgus, and neutral degenerative knees, and examination of contact regions during the deep knee bend activity further emphasizes these patterns. Segmentation results were achieved that were comparable if not of higher quality than existing state-of-the-art techniques for both femoral and tibial cartilage. Likewise, using the point correspondence properties of SSMs, estimation of articulating cartilage was effective in healthy and degenerative knees. In conclusion, this work provides novel, clinically relevant morphological data to compute segmentation and estimate new data in such a way to potentially contribute to improving results and efficiency in evaluation of the femorotibial cartilage layer

Three-dimensional anatomical atlas of the human body

A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsAnatomical atlases allow mapping the anatomical structures of the human body. Early versions of these systems consisted of analogic representations with informative text and labelled images of the human body. With the advent of computer systems, digital versions emerged and the third dimension was introduced. Consequently, these systems increased their efficiency, allowing more realistic visualizations with improved interactivity. The development of anatomical atlases in geographic information systems (GIS) environments allows the development of platforms with a high degree of interactivity and with tools to explore and analyze the human body. In this thesis, a prototype for the human body representation is developed. The system includes a 3D GIS topological model, a graphical user interface and functions to explore and analyze the interior and the surface of the anatomical structures of the human body. The GIS approach relies essentially on the topological characteristics of the model and on the kind of available functions, which include measurement, identification, selection and analysis. With the incorporation of these functions, the final system has the ability to replicate the kind of information provided by the conventional anatomical atlases and also provides a higher level of functionality, since some of the atlases limitations are precisely features offered by GIS, namely, interactive capabilities, multilayer management, measurement tools, edition mode, allowing the expansion of the information contained in the system, and spatial analyzes

Fast, scalable, Bayesian spike identification for multi-electrode arrays

We present an algorithm to identify individual neural spikes observed on high-density multi-electrode arrays (MEAs). Our method can distinguish large numbers of distinct neural units, even when spikes overlap, and accounts for intrinsic variability of spikes from each unit. As MEAs grow larger, it is important to find spike-identification methods that are scalable, that is, the computational cost of spike fitting should scale well with the number of units observed. Our algorithm accomplishes this goal, and is fast, because it exploits the spatial locality of each unit and the basic biophysics of extracellular signal propagation. Human intervention is minimized and streamlined via a graphical interface. We illustrate our method on data from a mammalian retina preparation and document its performance on simulated data consisting of spikes added to experimentally measured background noise. The algorithm is highly accurate