Plantar fascia ultrasound images characterization and classification using support vector machine

The examination of plantar fascia (PF) ultrasound (US) images is subjective and based on the visual perceptions and manual biometric measurements carried out by medical experts. US images feature extraction, characterization and classification have been widely introduced for improving the accuracy of medical assessment, reducing its subjective nature and the time required by medical experts for PF pathology diagnosis. In this paper, we develop an automated supervised classification approach using the Support Vector Machine (Linear and Kernel) to distinguishes between symptomatic and asymptomatic PF cases. Such an approach will facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Six feature sets were extracted from the segmented PF region. Additionally, features normalization, features ranking and selection analysis using an unsupervised infinity selection method were introduced for the characterization and the classification of symptomatic and asymptomatic PF subjects. The performance of the classifiers was assessed using confusion matrix attributes and some derived performance measures including recall, specificity, balanced accuracy, precision, F-score and Matthew’s correlation coefficient. Using the best selected features sets, Linear SVM and Kernel SVM achieved an F-Score of 97.06 and 98.05 respectively

Thickness estimation, automated classification and novelty detection in ultrasound images of the plantar fascia tissues

The plantar fascia (PF) tissue plays an important role in the movement and the stability of the foot during walking and running. Thus it is possible for the overuse and the associated medical problems to cause injuries and some severe common diseases. Ultrasound (US) imaging offers significant potential in diagnosis of PF injuries and monitoring treatments. Despite the advantages of US, the generated PF images are difficult to interpret during medical assessment. This is partly due to the size and position of the PF in relation to the adjacent tissues. This limits the use of US in clinical practice and therefore impacts on patient services for what is a common problem and a major cause of foot pain and discomfort. It is therefore a requirement to devise an automated system that allows better and easier interpretation of PF US images during diagnosis. This study is concerned with developing a computer-based system using a combination of medical image processing techniques whereby different PF US images can be visually improved, segmented, analysed and classified as normal or abnormal, so as to provide more information to the doctors and the clinical treatment department for early diagnosis and the detection of the PF associated medical problems. More specifically, this study is required to investigate the possibility of a proposed model for localizing and estimating the PF thickness a cross three different sections (rearfoot, midfoot and forefoot) using a supervised ANN segmentation technique. The segmentation method uses RBF artificial neural network module in order to classify small overlapping patches into PF and non-PF tissue. Feature selection technique was performed as a post-processing step for feature extraction to reduce the number of the extracted features. Then the trained RBF-ANN is used to segment the desired PF region. The PF thickness was calculated using two different methods: distance transformation and a proposed area-length calculation algorithm. Additionally, different machine learning approaches were investigated and applied to the segmented PF region in order to distinguish between symptomatic and asymptomatic PF subjects using the best normalized and selected feature set. This aims to facilitate the characterization and the classification of the PF area for the identification of patients with inferior heel pain at risk of plantar fasciitis. Finally, a novelty detection framework for detecting the symptomatic PF samples (with plantar fasciitis disorder) using only asymptomatic samples is proposed. This model implies the following: feature analysis, building a normality model by training the one-class SVDD classifier using only asymptomatic PF training datasets, and computing novelty scores using the trained SVDD classifier, training and testing asymptomatic datasets, and testing symptomatic datasets of the PF dataset. The performance evaluation results showed that the proposed approaches used in this study obtained favourable results compared to other methods reported in the literature

Ultrasound assessment of deep fascia sliding mobility in vivo: a scoping review

Introdução: A falha do deslizamento fascial pode ocorrer em casos de uso excessivo ou inadequado, trauma ou cirurgia, resultando em inflamação local, dor, sensibilização e potencial disfunção. As propriedades mecânicas dos tecidos fasciais, incluindo a sua mobilidade, têm sido avaliadas in vivo através de ecografia. No entanto, este parece ser um método que ainda não está devidamente padronizado nem validado. Objetivos: Identificar, sintetizar e comparar os princípios metodológicos da investigação científica que utilizou a avaliação ecográfica do deslizamento da fáscia profunda em humanos in vivo, e avaliar a sua fiabilidade. Métodos: Realizou-se uma pesquisa sistemática da literatura nas bases de dados ScienceDirect, PubMed (Medline), Web of Science e B-On, de acordo com as diretrizes PRISMA Extension for Scoping Reviews (PRISMA-ScR). A revisão seguiu três etapas principais: (1) identificação da questão e da literatura relevante; (2) seleção da literatura; e (3) agrupamento, mapeamento e resumo dos dados. Critérios de elegibilidade: Foram incluídos os artigos que usaram a ecografia para avaliar o deslizamento da fáscia profunda em seres humanos in vivo, usando o termo “sliding” ou outro com significado semelhante. Foram excluídos os estudos: não disponíveis em publicações revistas por partes, não disponíveis em inglês, português ou espanhol ou cujo texto completo não se encontrava acessível. Resultados: De um total de 104 artigos completos avaliados para elegibilidade, foram incluídos 18 artigos que avaliaram as fáscias profundas das regiões toracolombar (n=4), abdominal (n=7), femoral (n=4) e crural (n=3). Estes estudos abordaram questões de diagnóstico (n=11) e benefícios terapêuticos (n=7) e apresentaram níveis de evidência entre II e IV. Foram usados vários termos para descrever as medidas de resultados correspondentes ao deslizamento fascial. Foram usados diversos posicionamentos dos participantes, procedimentos para induzir o deslizamento fascial e características dos dispositivos de ecografia. Os métodos de análise do deslizamento fascial incluíram a comparação de imagens ecográficas inicial (estado de repouso) e final (estado alvo) e o uso de técnicas de software de correlação-cruzada através de algoritmos de rastreamento automatizado. Estes métodos mostraram-se fiáveis para medir o deslizamento entre a fáscia toracolombar, as junções músculo-fasciais do transverso abdominal, a fáscia lata e a fáscia crural e as fáscias epimisiais adjacentes. No entanto, os artigos incluídos apresentaram terminologias, questões de investigação, populações participantes e metodologias heterogéneas. É escassa a investigação de alta qualidade para determinar a fiabilidade dos métodos atuais para analisar outras fáscias e avaliar a influência da idade, de características relacionadas com o género, composição corporal ou condições clínicas específicas nas medidas de deslizamento fascial. Conclusão: Os métodos ecográficos de medição do deslizamento fascial incluem a comparação entre frames inicial e final de uma gravação de vídeo de ultrassom e a análise de relação cruzada através de algoritmos de rastreamento automatizado. Estes métodos parecem ser fiáveis para medir o deslizamento de algumas fáscias, mas é necessária literatura para confirmar a sua fiabilidade para outras. Além disso, são necessários protocolos de avaliação específicos e padronizados para cada região anatómica, de modo que a avaliação ecográfica do deslizamento fascial in vivo possa ser usada adequadamente na investigação e na prática clínica

Current understanding of the diagnosis and management of the tendinopathy: An update from the lab to the clinical practice

[Abstract] Tendinopathy is labeled by many authors as a troublesome, common pathology, present in up to 30% medical care con- sultations involving musculoskeletal conditions. Despite the lasting interest for addressing tendon pathology, current re- searchers agree that even the exact definition of the term tendinopathy is unclear. Tendinopathy is currently diagnosed as a clinical hypothesis based on the patient symptoms and physical context. One of the main goals of current clinical management is to personalize treatment approaches to adapt them to the many different needs of the population. Tendons are complex structures that unite muscles and bones with two main objectives: to transmit forces and stor- age and release energy. Regarding the tensile properties of the tendons, several authors argued that tendons have higher tensile strength compared with muscles, however, are con- sidered less flexible. Tendinopathy is an accepted term which is used to indicated a variety of tissue conditions that appear in injured tendons and describes a non-rupture damage in the tendon or para- tendon, which is intensified with mechanical loading Even when the pathoetiology of tendinopathy is unclear, there is a wide array of treatments available to treat and manage tendinopathy. Although tendinitis usually debuts with an in- flammatory response, the majority of chronic tendinopathies do not present inflammation and so the choosing of treat- ment should vary depending on severity, compliance, pain and duration of symptoms. The purpose of this article is to review and provide an overview about the currently research of the tendon diagno- sis, management and etiology

THE EFFECTS AND MECHANISMS OF KINESIOLOGICAL TAPING IN PEOPLE WITH LOWER BACK PAIN

PhDKinesioSTaping (KT) is increasingly used to treat low back pain (LBP). LBP is a common disorder with high lifetime incidence and recurrence which is complicated by variable treatment effects and unclear mechanisms. The overarching aim of this thesis was to determine whether biomechanical tissue responses could be identified and then used to determine subgroups of responders or nonSresponders to KT. Changes in thoracolumbar fascial thickness, structure and shear strain are associated with LBP. Methodological development was required and delivered a reliable, valid, inSvivo measurement technique to enable quantification of lumbar soft tissue biomechanics. ThreeSdimensional ultrasound videos with known orientation and position were recorded from the thoracolumbar tissues while participants performed range of movement tasks. Surface electromyography and kinematic data were collected. An automated algorithm using crossScorrelation to track contiguous tissue layers across sequential frames was developed and applied to process videos. A rapid systematic review was conducted and confirmed the lack of KT efficacy, in contrast with observed popularity. The first observation study indicated that normal subjects had some tissue layer specific changes in movement with KT application. Subsequent studies of participants with LBP showed reduced superficial tissue movement compared to controls, but MANOVA showed that KT did not change either group’s overall soft tissue biomechanics. Interestingly, overall soft tissue biomechanics responded differently among the small subgroup of participants with LBP who reported immediate, albeit minor, pain relief. This thesis shows that there are some effects of a common KT procedure on the lumbar soft tissues which are not yet robustly proven enough to be clinically applicable. Future study is warranted on those whose condition immediately benefits from receiving KT application to reveal if this mechanism can be developed and used to improve the immediate treatment response for those with LBP. Further, the dynamic tissue measurement method developed in this project should be considered as a transferable tool which has the potential to be applied to study effects and mechanisms of the other therapeutic modalities

The Use of Shear Wave Elastography to Estimate the Mechanical Properties of the Soft Tissues Surrounding the Proximal Femur

Introduction: Falls are the leading cause of injury, morbidity, and mortality amongst older adults nationally and worldwide, with 95% resulting in hip fractures. Previous studies have found that the soft tissues (subcutaneous adipose and muscle) surrounding the proximal femur influence the magnitude and distribution of the impact force transmitted to the underlying bone during an impact, and thus hip fracture risk. However, there is a gap in the literature since studies have not explored the role of the soft tissues’ mechanical properties, particularly Young’s modulus, on the impact force magnitudes and distribution during both resting and muscle contracted states. This gap can be addressed using shear wave elastography (SWE), an ultrasound imaging technique that quantifies soft tissues' shear wave velocity (SWV). SWV can be used to compute the Young’s moduli of the tissues and gain a better understanding of the mechanical properties of the tissues. Investigating the SWV of the soft tissues would help improve the biofidelity of physical and computational hip models, which would allow us to better understand lateral fall impact dynamics and help to improve the design of hip fracture prevention technologies. Objectives and Hypotheses: There were two main objectives of this thesis. The first objective was to evaluate the intra-rater reliability of the SWE protocol used to characterize the SWV of the soft tissues (subcutaneous adipose and muscle) in the lateral-hip region, and the potential influence of muscle contraction on intra-rater reliability. The second objective was to assess the influence of muscle contraction on the soft tissues’ SWV. Based on the previous literature, it was hypothesized that: 1) the intra-rater reliability of the subcutaneous adipose and muscle SWV measurements taken with this protocol would range from good to excellent (ICC > 0.75), and would not be influenced by contraction state, and 2) there would be an interaction effect of muscle contraction on the SWV magnitudes of the soft tissues. It was expected that: a) the muscle SWV magnitude would be greater during the muscle contracted state compared to the resting state, while b) the subcutaneous adipose SWV magnitude would not change during the resting and muscle contracted states. Methods: Twenty healthy, young adults between the ages of 18-35 years with a BMI ≤ 24.9 kg/m2 were recruited. The elastography setting on the GE LOGIQ E10 commercial ultrasound machine and a linear probe were used to take 3 measurements of the muscle and subcutaneous adipose tissues from a marked location on the participants’ left lateral hip region. Three measurements from each tissue were taken during the muscle relaxed and contracted states, resulting in 12 measurements being collected from each participant. A two-way mixed effects absolute agreement intraclass correlation (ICC) model was used to assess the intra-rater reliability of the measurements and the influence that muscle contraction had on it. A two-way repeated measures analysis of variance (ANOVA) was used to assess the potential interaction effect of muscle contraction on the SWV magnitudes of the soft tissues. Results: As hypothesized the intra-rater reliability of the subcutaneous adipose and muscle tissues ranged from good to excellent (0.81 < ICC < 0.95) and was not influenced by contraction state. This aligned with previous literature and supported that the protocol was appropriate for examining the influence of muscle contraction on SWV (Objective 2). Two-way repeated measures ANOVA revealed a significant interaction effect of muscle contraction on the SWV magnitudes of the soft tissues (p < 0.05), which was particularly driven by the significant increase in the muscle’s SWV from resting to contracted state (p < 0.001). In contrast, the subcutaneous adipose’s SWV was not different during the two conditions (p = 0.825). These results aligned with the hypothesis and indicated that the moduli of the soft tissues in this region respond differently between relaxed and contracted states. Discussion and Conclusion: The protocol used in this thesis had good to excellent intra-rater reliability and can be used to characterize the moduli of the soft tissues in additional locations in the hip region. Additionally, the influence of BMI and aging on the stiffness of the soft tissues in this region can also be investigated. Muscle contraction also influenced the SWV of the muscle, while the SWV of the subcutaneous adipose did not change. This finding indicates that the soft tissues in the hip have unique properties and respond differently to muscle contraction. Accordingly, it is worth exploring whether they should be modelled separately in biomechanical models of the hip and pelvis. Implementing this finding in physical and computational models that currently bulk together the properties of the soft tissues in the hip, should improve their biofidelity. Additionally, the protocol used in this thesis, along with the differences observed in the soft tissues’ SWV, can potentially be used to assess muscle quality. Since alternative modes on the ultrasound, such as echo intensity, are currently used to assess changes in muscle quality, SWE may be an alternative technique that can be used

Program: Graduate Research Achievement Day 2019

Full program for 2019 Graduate School Achievement Day.https://digitalcommons.odu.edu/graduate_school/1001/thumbnail.jp