A diagnostic imaging technique and therapeutic strategy for early osteoarthritis

Thesis (Ph.D.)--Boston UniversityOsteoarthritis (OA) is a chronic, progressive disease of diarthrodial joints arising from the breakdown of articular cartilage. As one of the leading causes of disability and lifestyle limitations in the United States, osteoarthritis is estimated to affect 27 million people in the U.S. and cost the economy $128 billion annually. Current diagnostic techniques detect OA only in its later stages, when irreversible cartilage damage has already occurred. A reliable, non-invasive method for diagnosing OA in its early stages would provide an opportunity to intervene and potentially to stay disease progression. Likewise, the field of OA research would benefit from a technique that allows tissue engineering and small molecule therapies to be evaluated longitudinally. Contrast-enhanced computed tomography (CECT) of cartilage is a developing medical imaging technique for evaluating cartilage biochemical and biomechanical properties. CECT has been shown to accurately quantify measures of cartilage integrity such as glycosaminoglycan (GAG) content, equilibrium compressive modulus, and coefficients of friction. In the studies presented herein, cationic iodinated contrast agents are developed for quantitative cartilage CECT, a technique predicated on the diffusion and partitioning of a charged contrast agent into the cartilage. The experiments show that cationic contrast agents lack specific interactions with anionic GAGs and are highly taken up in cartilage due, instead, to their electrostatic attraction. At diffusion equilibrium, both anionic and cationic agents indicate GAG content and biomechanical properties as measured by microcomputed tomography, though cationic contrast agents were found to diffuse through cartilage more slowly than anionic ones. Translating CECT to intact joints with clinically available helical CT scanners bears promising results, but concerns remain regarding in vivo applicability. Anionic contrast agents enable GAG content quantification following brief contrast agent exposure, whereas cationic agents require full equilibration within the tissue. To explore treatment modalities for early OA, a novel interpenetrating hydrogel method was developed to reconstitute the mechanical properties of cartilage models for early OA. Preliminary results show that the interpenetrating network strengthened cartilage with respect to compressive loading suggesting that the treatment could potentially serve as a functional replacement for GAG lost in the early stages of OA

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Advancing Precision Medicine: Unveiling Disease Trajectories, Decoding Biomarkers, and Tailoring Individual Treatments

Chronic diseases are not only prevalent but also exert a considerable strain on the healthcare system, individuals, and communities. Nearly half of all Americans suffer from at least one chronic disease, which is still growing. The development of machine learning has brought new directions to chronic disease analysis. Many data scientists have devoted themselves to understanding how a disease progresses over time, which can lead to better patient management, identification of disease stages, and targeted interventions. However, due to the slow progression of chronic disease, symptoms are barely noticed until the disease is advanced, challenging early detection. Meanwhile, chronic diseases often have diverse underlying causes and can manifest differently among patients. Besides the external factors, the development of chronic disease is also influenced by internal signals. The DNA sequence-level differences have been proven responsible for constant predisposition to chronic diseases. Given these challenges, data must be analyzed at various scales, ranging from single nucleotide polymorphisms (SNPs) to individuals and populations, to better understand disease mechanisms and provide precision medicine. Therefore, this research aimed to develop an automated pipeline from building predictive models and estimating individual treatment effects based on the structured data of general electronic health records (EHRs) to identifying genetic variations (e.g., SNPs) associated with diseases to unravel the genetic underpinnings of chronic diseases. First, we used structured EHRs to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. In this step, we employed causal inference methods (constraint-based and functional causal models) for feature selection and utilized Markov chains, attention long short-term memory (LSTM), and Gaussian process (GP). SHapley Additive exPlanations (SHAPs) and local interpretable model-agnostic explanations (LIMEs) further extended the work to identify important clinical features. Next, I developed a novel counterfactual-based method to predict individual treatment effects (ITE) from observational data. To discern a “balanced” representation so that treated and control distributions look similar, we disentangled the doctor’s preference from the covariance and rebuilt the representation of the treated and control groups. We use integral probability metrics to measure distances between distributions. The expected ITE estimation error of a representation was the sum of the standard generalization error of that representation and the distance between the distributions induced. Finally, we performed genome-wide association studies (GWAS) based on the stage information we extracted from our unsupervised disease progression model to identify the biomarkers and explore the genetic correction between the disease and its phenotypes

Computational Tools and Experimental Methods for the Development of Passive Prosthetic Feet

Modern prosthetic foot designs are incredibly diverse in comparison to what was o↵ered to amputees at the turn of the millennium. Powered ankles can supply natural levels of joint torque, whilst passive feet continue to optimise for kinematic goals. However, most passive feet still do not solve the issue of unhealthy loads, and an argument can be made that optimisation methods have neglected the less active and elderly amputee. This thesis creates a framework for a novel approach to prosthetic foot optimisation by focusing on the transitionary motor tasks of gait initiation and termination.An advanced FEA model has been created in ANSYS® using boundary con-ditions derived from an ISO testing standard that replicates stance phase loading. This model can output standard results found in the literature and goes beyond by parameterising the roll-over shape within the software using custom APDL code. Extensive contact exploration and an experimental study have ensured the robustness of the model. Subject force and kinematic data can be used for specific boundary conditions, which would allow for easy adaptation to the transitionary motor tasks.This FEA model has been used in the development of prosthetic experiment tool, which can exchange helical springs to assess e↵ects of small changes in sti↵-ness on gait metrics. A rigorous design methodology was employed for all compo-nents, including parametric design studies, response surface optimisation, and ISO level calculations. The design has been manufactured into a working prototype and is ready for clinical trials to determine its efficacy.The conclusion of this framework is in the development of an experimental method to collect subject data for use in the models. A pilot study uncovered reliable protocols, which were then verified with ANOVA statistics. Proportional ratios were defined as additions to metric peak analyses already found in the liter-ature. These tools are ready for deployment in full clinical trials with amputees, so that a new prosthetic optimisation pathway can be discovered for the benefit of less active or elderly amputees

On the biomechanics of ligaments and muscles throughout the range of hip motion

At the limits of the range of hip motion, impingement, subluxation and edge loading can cause osteoarthritis in natural hips or early failure hip replacements. The aim of this PhD was to investigate the role of hip joint soft tissues throughout the range of hip motion to better understand their role in preventing (or perhaps even causing) these problematic load cases. A musculoskeletal model was used to investigate the muscular contribution to edge loading and found that in the mid-range of hip motion, the lines of action of hip muscles pointed inward from the acetabular rim and thus would stabilise the hip. However, in deep hip flexion with adduction, nearly half the muscles had unfavourable lines of action which could encourage edge loading. Conversely, in-vitro tests on nine cadaveric hips found that the hip capsular ligaments were slack in the mid-range of hip motion but tightened to restrain excessive hip rotation in positions close to the limits of hip motion. This passive restraint prevented the hip from moving into positions where the muscle lines of action were found to be unfavourable and thus could help protect the hip from edge loading. The ligaments were also found to protect the hip against impingement and dislocation. Out of the labrum, the ligamentum teres and the three capsular ligaments, it was found that the iliofemoral and ischiofemoral ligaments were primary restraints to hip rotation. These two capsular ligaments should be prioritised for protection/repair during hip surgery to maintain normal hip passive restraint. Whilst this can be technically demanding, failing to preserve/restore their function may increase the risk of osteoarthritic degeneration or hip replacement failure.Open Acces

Stratification of patellofemoral pain using clinical, biomechanical and imaging features

Patellofemoral pain (PFP) is a common musculoskeletal complaint and the efficacy of current therapies aimed at PFP is limited. The aetiology of PFP is widely considered to be multifactorial and as a result the clinical presentation is often heterogeneous. In an attempt to address this issue, an international PFP consensus statement, published in 2013, highlighted the need to sub-group patients with PFP to enable more stratified interventions. A multi-methodological approach was used in this thesis. A systematic review of the existing imaging literature in PFP demonstrated that PFP is associated with a number of imaging features in particular MRI bisect offset and CT congruence angle and that some of these features should be modifiable with conservative treatment. A retrospective analysis investigating the overall 3D shape and 3D equivalents of commonly used PFJ imaging features demonstrated no differences between a group with and without PFP, challenging the current perceptions on the structural associations to PFP. A cross-sectional cluster analysis using modifiable clinical, biomechanical and imaging features identified four subgroups that are present in PFP cohort with a Weak group showing the worst prognosis at 12 months. Lastly, a pragmatic randomised controlled feasibility study comparing matched treatment to usual care management showed that matching treatment to a specific subgroup is feasible in terms of adherence, retention and conversion to consent. In summary, the findings of this thesis improves our understanding of the structural associations to PFP; the subgroups that exist within the PFP population; the natural prognosis of these PFP subgroups; and the feasibility of targeting treatment at PFP subgroups within a clinical trial

Advancing clinical evaluation and diagnostics with artificial intelligence technologies

Machine Learning (ML) is extensively used in diverse healthcare applications to aid physicians in diagnosing and identifying associations, sometimes hidden, between dif- ferent biomedical parameters. This PhD thesis investigates the interplay of medical images and biosignals to study the mechanisms of aging, knee cartilage degeneration, and Motion Sickness (MS). The first study shows the predictive power of soft tissue radiodensitometric parameters from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hy- pertension, and diabetes. The results show the role of fat, muscle, and connective tissue in the evaluation of healthy aging. Moreover, we classify patients experiencing gait symptoms, neurological deficits, and a history of stroke in a Korean population, reveal- ing the significant impact of cognitive dual-gait analysis when coupled with single-gait. The second study establishes new paradigms for knee cartilage assessment, correlating 2D and 3D medical image features obtained from CT and MRI scans. In the frame of the EU-project RESTORE we were able to classify degenerative, traumatic, and healthy cartilages based on their bone and cartilage features, as well as we determine the basis for the development of a patient-specific cartilage profile. Finally, in the MS study, based on a virtual reality simulation synchronized with a moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features and analyzed their importance in predicting MS symptoms, concussion in female ath- letes, and lifestyle influence. The MS features are extracted from the brain, muscle, heart, and from the movement of the center of pressure during the experiment and demonstrate their potential value to advance quantitative evaluation of postural con- trol response. This work demonstrates, through various studies, the importance of ML technologies in improving clinical evaluation and diagnosis contributing to advance our understanding of the mechanisms associated with pathological conditions.Tölvulærdómur (Machine Learning eða ML) er algjörlega viðurkennt og nýtt í ýmsum heilbrigðisþjónustuviðskiptum til að hjálpa læknunum við að greina og finna tengsl milli mismunandi líffærafræðilegra gilda, stundum dulinna. Þessi doktorsritgerð fjallar um samspil læknisfræðilegra mynda og lífsmerkja til að skoða eðli aldrunar, niðurbrot hnéhringjar og hreyfikerfissjúkdóms (Motion Sickness eða MS). Fyrsta rannsóknin sýnir spárkraft midjubeins-CT-skanna í því að fullyrða staðfest- ar meðalþyngdarlíkön, þar sem gögn úr AGES-Reykjavik-rannsókninni eru tengd við hjarta- og æðafræðilega sjúkdóma, blóðþrýstingsveikindi og sykursýki hjá 3.000 þátt- takendum. Niðurstöðurnar sýna hlutverk fitu, vöðva og tengikjarna í mati á heilbrigð- um öldrun. Þar að auki flokkum við sjúklinga sem upplifa gangvandamál, taugaein- kenni og sögu af heilablóðfalli í kóreanskri þjóð, þar sem einstök gangtaksskoðun er tengd saman við tvískoðun. Önnur rannsóknin setur upp ný tölfræðisfræðileg umhverfisviðmið til matar á hnéhringju með samhengi 2D og 3D mynda sem aflað er úr CT og MRI-skömmtum. Í rauninni höfum við getuð flokkað niðurbrots-, slys- og heilbrigðar hnéhringjur á grundvelli bein- og brjóskmerkja með raun að sækja niðurstöður í umfjöllun um sjúklingar eftir réttu einkasniði. Að lokum, í MS-rannsókninni, notum við myndræn tilraun samþættaða með hreyfan- legan grundvöll og EEG, hjartslátt, EMG þar sem yfir 3.000 aðgerðir eru útfránn og greindir til að átta sig á áhrifum MS, höfuðárás hjá konum sem eru íþróttamenn, lífs- stíl og fleira. Einkenni MS eru aflöguð úr heilanum, vöðvum, hjarta og frá hreyfingum þyngdupunktsins á meðan tilraunin stendur og sýna mög

Clinical and Imaging Assessment of Metal on Metal Hip Patients

A high failure rate of metal-on-metal (MoM) hip implants prompted regulatory authorities to issue worldwide product recalls. The cause for their failure and decisions surrounding the need for revision is complex due to poor understanding of the toxic effects of metal debris. In addition to local soft tissue destruction, circulating cobalt can cause rare but fatal cardiotoxicity. This thesis describes the detection of metal cobalt-chromium within the liver of a patient with highly elevated blood cobalt (587ppb) using novel MRI imaging techniques, validated by liver biopsy and micro x-ray fluorescence. The prevalence of tissue metal deposition and potential cardiotoxic effects were assessed through a prospective case controlled cohort study. Ninety patients were recruited into three age and gender-matched groups according to blood metal levels. All underwent detailed cardiovascular and liver phenotyping using MRI (for myocardial volumes and function, T2*, T1 and Extra-Cellular Volume mapping), echocardiography, and blood biomarker sampling. T2* is a novel MRI biomarker of tissue metal deposition. Blood cobalt levels among the cohort ranged 0.1 to 118ppb, which is still seen in patients presenting for clinical follow-up. No significant between-group differences were found for cardiac volume or function, nor was there any difference in tissue characterization using T1, T2* and ECV. Higher blood cobalt levels did not translate to increased metal deposition within the heart or liver.The application of these results were analsyed through a multi-disciplianary team setting designed to aid complex decisions of who, when and how to treat MoM patients surgically. By analysis of MDT recommendations compared to the treatment undertaken it was demonstrated that an MDT approach is an acceptable evidence-based aid to decision-making.This thesis concludes that cobalt tissue deposition can be detected using non-invasive MRI techniques, however metal deposition is not commonly seen with blood cobalt levels upto 118ppb with reassuringly little cardiotoxic effects. These results help reassure clinicians managing MoM patients through an MDT approach

Design and test of a Displacement Workspace Mapping Station for articular joints

In 2003, 267,000 Americans received total knee replacements prohibiting high impact athletics for the remainder of a patient’s life. A better understanding of the movement and constraint of the knee is necessary to provide more realistic motion of or possibly eliminate the need for joint prosthetics. Fixed Orientation Displacement Workspaces (FODW) can be applied to study the relationship of the passive constraint system and six (6) degree of freedom (DOF) movement of the human knee. A FODW consists of the volume of possible positions the tibia/fibula can occupy relative to a fixed femur without changing the relative orientation of the bones. Theoretical models of the FODW provided a promising snapshot of knee kinematics. A Displacement Workspace Test Station (DWTS) for mapping FODWs was built. An in vitro articular joint completes the loop between a strain gauge-based six (6) axis load cell and a 6 DOF manipulandum mounted to a fixed reference frame. The joint is hand manipulated while a C++ program, Armtalk, operates applications that sample and filter both manipulandum position/orientation and load cell output signals at over 500Hz. Armtalk automatically stores raw data points at 2 Hz or upon a user foot-pedal signal. Forces and moments acting at the joint and its angular orientation are added to each raw data point by algorithms in a spreadsheet. The algorithms select points that represent a particular FODW according to a user specified range of acceptable joint forces and moments and bone orientations. The Cartesian coordinates of individual FODW data points are input into a NURBS-based CAD program for visualization. The DWTS has a 0.2286 mm positional accuracy, a 200 N capacity, and a 0.075 mm/kN compliance. A 2 DOF test checked the Armtalk application and calculated the DWTS angular accuracy to be 0.008°. To calibrate the load cell, moment and force scaling factors of 0.00922 in lb/unit and 0.00554 lb/unit were calculated, respectively. The spreadsheet algorithms successfully reduced data in a 6 DOF test. The CAD program modeled workspaces from 2 and 6 DOF tests with a 1.3 % volumetric accuracy. The apparatus is ready to map FODW of articular joints