Contribution to the clinical validation of a generic method for the classification of osteoarthritic and non-pathological knee function

The Cardiff Dempster-Shafer (DS) classifier is a generic automated technique for analysing motion analysis (MA) data. It can accurately discriminate between level gait characteristics of non-pathological (NP) and osteoarthritic (OA) knee function. It can also quantify and visualise the functional outcome of a total knee replacement (TKR). A number of studies were undertaken to explore and enhance this method. The training set for the classifier was increased by 48% by collecting additional knee function data for level gait. Knee function for nine new patients was classified pre and post-TKR surgery. At 12 months post-TKR, two patients exhibited non-dominant NP knee function. The remaining patients did not recover NP gait. This finding is similar to previous classifications of level gait. To improve the distinction between varying degrees of knee function, stair gait was introduced into the trial. A staircase was designed and validated. Adduction and flexion moments acting about the knee joint and medial component of the ground reaction force were found to be important in the classification of OA and NP knee function from stair gait. Using a combination of these variables the DS classifier was able to characterise OA and NP function for 15 subjects correctly with 100% accuracy, determined using a leave-one-out method of cross validation. The variables were tested to assess the outcome of TKR surgery. The patient assessed recovered NP stair gait post surgery. An image based study was undertaken to investigate the quality of the MA data used in the DS classifier. A step up/down activity for 5 NP and 5 TKR subjects was recorded using non-simultaneous MA and dynamic fluoroscopy. Accurate knee kinematics were computed from the fluoroscopy images using KneeTrack image registration software. MA measured significantly larger knee joint translations and non-sagittal plane rotations. The largest errors in MA derived kinematics were 9.53 for adduction-abduction range of motion (ROM) measured from the NP cohort and 2.63cm compression-distraction ROM of the tibio-femoral joint, measured from the TKR cohort. The generic nature of the DS classifier was tested by its application to distinguish hip function following a lateral (LA) and posterior (PA) approach to total hip arthroplasty. The use of different variables was investigated with the classifier. The best classifier was able to distinguish between NP and LA function with 96.7% accuracy, LA and NP with 86.2% accuracy and between LA and PA with 81.5% accuracy. The PA approach was found to lead to more characteristic NP hip function than LA. These studies show that variables from stair gait should be included in addition to level gait in the classifier. Due to errors when measuring non-sagittal plane rotations using MA, these should be interpreted with caution. The generic nature of the classifier has been proven by its application to another joint, thus answering another orthopaedic question.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Contribution to the clinical validation of a generic method for the classification of osteoarthritic and non-pathological knee function

The Cardiff Dempster-Shafer (DS) classifier is a generic automated technique for analysing motion analysis (MA) data. It can accurately discriminate between level gait characteristics of non-pathological (NP) and osteoarthritic (OA) knee function. It can also quantify and visualise the functional outcome of a total knee replacement (TKR). A number of studies were undertaken to explore and enhance this method. The training set for the classifier was increased by 48% by collecting additional knee function data for level gait. Knee function for nine new patients was classified pre and post-TKR surgery. At 12 months post-TKR, two patients exhibited non-dominant NP knee function. The remaining patients did not recover NP gait. This finding is similar to previous classifications of level gait. To improve the distinction between varying degrees of knee function, stair gait was introduced into the trial. A staircase was designed and validated. Adduction and flexion moments acting about the knee joint and medial component of the ground reaction force were found to be important in the classification of OA and NP knee function from stair gait. Using a combination of these variables the DS classifier was able to characterise OA and NP function for 15 subjects correctly with 100% accuracy, determined using a leave-one-out method of cross validation. The variables were tested to assess the outcome of TKR surgery. The patient assessed recovered NP stair gait post surgery. An image based study was undertaken to investigate the quality of the MA data used in the DS classifier. A step up/down activity for 5 NP and 5 TKR subjects was recorded using non-simultaneous MA and dynamic fluoroscopy. Accurate knee kinematics were computed from the fluoroscopy images using KneeTrack image registration software. MA measured significantly larger knee joint translations and non-sagittal plane rotations. The largest errors in MA derived kinematics were 9.53 for adduction-abduction range of motion (ROM) measured from the NP cohort and 2.63cm compression-distraction ROM of the tibio-femoral joint, measured from the TKR cohort. The generic nature of the DS classifier was tested by its application to distinguish hip function following a lateral (LA) and posterior (PA) approach to total hip arthroplasty. The use of different variables was investigated with the classifier. The best classifier was able to distinguish between NP and LA function with 96.7% accuracy, LA and NP with 86.2% accuracy and between LA and PA with 81.5% accuracy. The PA approach was found to lead to more characteristic NP hip function than LA. These studies show that variables from stair gait should be included in addition to level gait in the classifier. Due to errors when measuring non-sagittal plane rotations using MA, these should be interpreted with caution. The generic nature of the classifier has been proven by its application to another joint, thus answering another orthopaedic question

Medical Robotics

The first generation of surgical robots are already being installed in a number of operating rooms around the world. Robotics is being introduced to medicine because it allows for unprecedented control and precision of surgical instruments in minimally invasive procedures. So far, robots have been used to position an endoscope, perform gallbladder surgery and correct gastroesophogeal reflux and heartburn. The ultimate goal of the robotic surgery field is to design a robot that can be used to perform closed-chest, beating-heart surgery. The use of robotics in surgery will expand over the next decades without any doubt. Minimally Invasive Surgery (MIS) is a revolutionary approach in surgery. In MIS, the operation is performed with instruments and viewing equipment inserted into the body through small incisions created by the surgeon, in contrast to open surgery with large incisions. This minimizes surgical trauma and damage to healthy tissue, resulting in shorter patient recovery time. The aim of this book is to provide an overview of the state-of-art, to present new ideas, original results and practical experiences in this expanding area. Nevertheless, many chapters in the book concern advanced research on this growing area. The book provides critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies. This book is certainly a small sample of the research activity on Medical Robotics going on around the globe as you read it, but it surely covers a good deal of what has been done in the field recently, and as such it works as a valuable source for researchers interested in the involved subjects, whether they are currently “medical roboticists” or not

Precise pose recovery of distal locking holes from single calibrated fluoroscopic image via a novel variable decomposition approach

This paper presents a novel variable decomposition approach for pose recovery of the distal locking holes using single calibrated fluoroscopic image. The problem is formulated as a model-based optimal fitting process, where the control variables are decomposed into two sets: (a) the angle between the nail axis and its projection on the imaging plane, and (b) the translation and rotation of the geometrical model of the distal locking hole around the nail axis. By using an iterative algorithm to find the optimal values of the latter set of variables for any given value of the former variable, we reduce the multiple-dimensional model-based optimal fitting problem to a one-dimensional search along a finite interval. We report the results of our in vitro experiments, which demonstrate that the accuracy of our approach is adequate for successful distal locking of intramedullary nails

Development of an In-Vitro Passive and Active Motion Simulator for the Investigation of Shoulder Function and Kinematics

Injuries and degenerative diseases of the shoulder are common and may relate to the joint’s complex biomechanics, which rely primarily on soft tissues to achieve stability. Despite the prevalence of these disorders, there is little information about their effects on the biomechanics of the shoulder, and a lack of evidence with which to guide clinical practice. Insight into these disorders and their treatments can be gained through in-vitro biomechanical experiments where the achieved physiologic accuracy and repeatability directly influence their efficacy and impact. This work’s rationale was that developing a simulator with greater physiologic accuracy and testing capabilities would improve the quantification of biomechanical parameters. This dissertation describes the development and validation of a simulator capable of performing passive assessments, which use experimenter manipulation, and active assessments – produced through muscle loading. Respectively, these allow the assessment of functional parameters such as stability, and kinematic/kinetic parameters including joint loading. The passive functionality enables specimen motion to be precisely controlled through independent manipulation of each rotational degree of freedom (DOF). Compared to unassisted manipulation, the system improved accuracy and repeatability of positioning the specimen (by 205% & 163%, respectively), decreased variation in DOF that are to remain constant (by 6.8°), and improved achievement of predefined endpoints (by 21%). Additionally, implementing a scapular rotation mechanism improved the physiologic accuracy of simulation. This enabled the clarification of the effect of secondary musculature on shoulder function, and the comparison of two competing clinical reconstructive procedures for shoulder instability. This was the first shoulder system to use real time kinematic feedback and PID control to produce active motion, which achieved unmatched accuracy ( These developments can be a powerful tool for increasing our understanding of the shoulder and also to provide information which can assist surgeons and improve patient outcomes

Probabilistic Feature-Based Registration for Interventional Medicine

The need to compute accurate spatial alignment between multiple representations of patient anatomy is a problem that is fundamental to many applications in computer-integrated interventional medicine. One class of methods for computing such alignments is feature-based registration, which aligns geometric information of the shapes being registered, such as salient landmarks or models of shape surfaces. A popular algorithm for surface-based registration is the Iterative Closest Point (ICP) algorithm, which treats one shape as a cloud of points that is registered to a second shape by iterating between point-correspondence and point-registration phases until convergence. In this dissertation, a class of "most likely point" variants on the ICP algorithm is developed that offers several advantages over ICP, such as high registration accuracy and the ability to confidently assess the quality of a registration outcome. The proposed algorithms are based on a probabilistic interpretation of the registration problem, wherein the point-correspondence and point-registration phases optimize the probability of shape alignment based on feature uncertainty models rather than minimizing the Euclidean distance between the shapes as in ICP. This probabilistic framework is used to model anisotropic errors in the shape measurements and to provide a natural context for incorporating oriented-point data into the registration problem, such as shape surface normals. The proposed algorithms are evaluated through a range of simulation-, phantom-, and clinical-based studies, which demonstrate significant improvement in registration outcomes relative to ICP and state-of-the-art methods

The 1991 Marshall Space Flight Center research and technology

A compilation of 194 articles addressing research and technology activities at the Marshall Space Flight Center (MSFC) is given. Activities are divided into three major areas: advanced studies addressing transportation systems, space systems, and space science activities conducted primarily in the Program Development Directorate; research tasks carried out in the Space Science Laboratory; and technology programs hosted by a wide array of organizations at the Center. The theme for this year's report is 'Building for the Future'