A Framework on A Computer Assisted and Systematic Methodology for Detection of Chronic Lower Back Pain using Artificial Intelligence and Computer Graphics Technologies

Back pain is one of the major musculoskeletal pain problems that can affect many people and is considered as one of the main causes of disability all over the world. Lower back pain, which is the most common type of back pain, is estimated to affect at least 60% to 80% of the adult population in the United Kingdom at some time in their lives. Some of those patients develop a more serious condition namely Chronic Lower Back Pain in which physicians must carry out a more involved diagnostic procedure to determine its cause. In most cases, this procedure involves a long and laborious task by the physicians to visually identify abnormalities from the patient’s Magnetic Resonance Images. Limited technological advances have been made in the past decades to support this process. This paper presents a comprehensive literature review on these technological advances and presents a framework of a methodology for diagnosing and predicting Chronic Lower Back Pain. This framework will combine current state-of-the-art computing technologies including those in the area of artificial intelligence, physics modelling, and computer graphics, and is argued to be able to improve the diagnosis process

A Machine Learning and Computer Assisted Methodology for Diagnosing Chronic Lower Back Pain on Lumbar Spine Magnetic Resonance Images

Chronic Lower Back Pain (CLBP) is one of the major types of pain that affects many people around the world. It is estimated that 28.1% of US adults suffer from this illness and 2.5 million of the UK population experience this type of pain every day. Most CLBP cases do not happen overnight and it is usually developed from a less serious but acute variant of lower back pain. An acute type of lower back pain can develop into a chronic one if the underlying cause is serious and left untreated. The longer a person is disabled by back pain, the less chance he or she returns to work and the more health care cost he or she will require. It is therefore important to identify the cause of back pains as early as possible in order to improve the chance of patient rehabilitation. The speediness of early diagnosis can depend on many factors including referral time from a general practitioner to the hospital, waiting time for a specialist appointment, time for a Magnetic Resonance Imaging (MRI) scan and time for the analysis result to come out. Currently diagnosing the lower back pain is done by visual observation and analysis of the lumbar spine MRI images by radiologists and clinicians and this process could take up much of their time and effort. This, therefore, rationalizes the need for a new method to increase the efficiency and effectiveness of the imaging diagnostic process. This thesis details a novel methodology to automatically aid clinicians in performing diagnosis of CLBP on lumbar spine MRI images. The methodology is based on the current accepted medical practice of manual inspection of the MRI scans of the patient’s lumbar spine as advised by several practitioners in this field. The main methodology is divided into three sub-methods the first sub-method is disc herniation detection using disc segmentation and centroid distance function. While the second sub-method is lumbar spinal stenosis detection via segmentation of area between anterior and posterior (AAP) Elements. Whereas, the last sub-method is the use of deep learning to perform semantic segmentation to identify regions in the MRI images that are relevant to the diagnosis process. The method then performs boundary delineation between these regions, identifies key points along the boundaries and measures distances between these points that can be used as an indication to the health of the lumbar spine. Due to a limitation in the size and suitability of the currently existing open-access lumbar spine dataset necessary to train and test any good classification algorithms, a dataset consisting of 48,345 MRI slices from a complete clinical lumbar MRI study of 515 symptomatic back pain patients from several specialty hospitals around the world has been created. Each MRI study is annotated by expert radiologists with notes regarding the observed characteristics, condition of the lumbar spine, or presence of diseases. The ground-truth dataset containing manually labelled segmented images has also been developed. To complement this ground-truth dataset, a novel method of constructing and evaluating the suitability of ground truth data for lumbar spine MRI image segmentation has been developed. A subset of the dataset, which includes the data for 101 patients, is used in a set of experiments that have been conducted using a variety of algorithms to conclude with using SegNet as the image segmentation algorithm. The network consists of VGG16 layers pre-trained using a subset of non-medical images from the ImageNet database and fine-tuned using the training portion of the ground-truth dataset. The results of these experiments show the accurate delineation of important boundaries of regions in lumbar spine MRI. The experiments also show very close agreement between the expert radiologists’ notes on the condition of a lumbar spine and the conclusion of the system about the lumbar spine in the majority of cases

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

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' 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

CARACTERIZACIÓN CUANTITATIVA DE LA PATOLOGÍA DISCAL Y LUMBAR DEGENERATIVA MEDIANTE ANÁLISIS DE IMAGEN POR RESONANCIA MAGNÉTICA Y DETECCIÓN Y SEGMENTACIÓN DE LA COLUMNA VERTEBRAL EN PACIENTES ONCOLÓGICOS A PARTIR DEL ANÁLISIS DE IMAGEN EN TOMOGRAFÍA COMPUTARIZADA

[EN] Over the last 20 years health system has been revolutionized by imaging technology so diagnostic imaging has become the mainstay of the management of patients. Nowadays, degeneration of the intervertebral discs, herniation and spinal stenosis are very common entities that affect millions of people and cause back pain. The development of computer-aided diagnosis (CAD) methods for classifying and quantifying these pathologies has increased in the past decade as a way to assist radiologists in the diagnosis task. So, the main objective of the first part of this Doctoral Thesis is the development of a CAD software for the classification and quantification of spine disease by means of Magnetic Resonance image analysis. To this end, two different groups of patients have been used, one as training group (14 patients) and the other as testing group (53 patients). To classify disc degeneration according to the gold standard, Pfirrmann classification, a method mainly based on the measurement of disc signal intensity and structure has been developed. The method developed to detect disc herniations has been focused on disc segmentation and its approximation by an ellipse, in this way it is possible to extract disc shape features for detecting contour abnormalities. The method developed to detect spinal stenosis, based on signal intensity, has been developed to extract the spinal canal and, by applying different techniques, to detect spinal stenosis at every intervertebral disc level and quantify the severity of the pathology. The results have shown a segmentation inaccuracy below 1%. Regarding reproducibility, it has been obtained an almost perfect agreement (measured by the k and ICC statistics) for all the analysed pathologies. The results have shown that the developed methods can assist radiologists to perform their decision-making tasks, providing support for enhanced reproducibility of MRI reports and achieving greater objectivity. However, not only the intervertebral discs are susceptible to suffer several pathologies. The vertebral bodies are also subject to a wide variety of diseases because of different circumstances. So, prior to any diagnosis task, an accurate detection and segmentation of the vertebral bodies are the first crucial steps. Therefore, the main objective of the second part of this Doctoral Thesis is the development of an automatic method for the detection and segmentation of the spine in Computed Tomography imaging. Performing an automatic and robust segmentation is a very challenging task due to the difficulty discriminating between the ribs and the vertebral bodies. To overcome this problem, two different segmentation methods have been combined: the first method uses a Level-Set method to perform an initial segmentation; the second method uses a probabilistic atlas to refine the initial segmentation with a special focus on ribs suppression. So a 3D volume indicating the probability of each voxel of belonging to the spine has been developed, by means of a set of images, corresponding to 14 patients (training group), manually segmented by an expert. The generated probability map has been deformed and adapted to each testing case. To evaluate the segmentation results and the improvement obtained after applying the atlas to the initial segmentation, the Dice similarity coefficient (DSC) and the Hausdorff distance (HD) have been used. The results have shown up an average of 11 mm of improvement in segmentation accuracy in terms of HD, obtaining an overall final average of 14,98 ± 1,32 mm. A refinement of 1,3 % has been obtained in terms of DSC, with a global value of 91,75 ± 1,20 %. The study has demonstrated that the atlas is able to detect and appropriately eliminate the ribs while improving the segmentation accuracy.[ES] En los últimos 20 años el sistema sanitario se ha visto revolucionado por la tecnología de la imagen, por lo que el diagnóstico por imagen se ha convertido en un pilar fundamental en el manejo de los pacientes. Hoy en día la degeneración de los discos intervertebrales, la hernia discal y la estenosis del canal vertebral, son tres patologías que afectan a millones de personas y causan dolor de espalda. El desarrollo de sistemas CAD para clasificar y cuantificar estas patologías se ha incrementado en la última década como una forma de ayuda al radiólogo en el diagnóstico. Por tanto, la primera parte de esta Tesis Doctoral tiene como objetivo el desarrollo de un sistema CAD para la clasificación y cuantificación de la patología discal por medio del análisis de Imagen por Resonancia Magnética. Con este fin se han utilizado dos grupos de pacientes, uno como grupo de entrenamiento (14 pacientes) y el otro como grupo de prueba (53 pacientes). Para la clasificación de la degeneración discal se ha desarrollado un método basado en el cálculo de la estructura del disco y de su señal de intensidad. El método de detección de herniaciones se ha centrado en la segmentación del disco y su aproximación por una elipse, para extraer así información sobre la forma del disco. El método de detección de estenosis, basado en la señal de intensidad, ha sido desarrollado para extraer el canal vertebral y, con la aplicación de diferentes técnicas, detectar estrechamientos a la altura de los discos y cuantificar la gravedad de los mismos. Los resultados han demostrado una alta precisión en la segmentación, con un error inferior al 1 %. En cuanto a la reproducibilidad, se ha obtenido un acuerdo casi perfecto (medido con los coeficientes CCI y k) para todas las patologías analizadas. Los resultados obtenidos demuestran que los métodos desarrollados pueden servir de ayuda al radiólogo en el diagnóstico, mejorando la reproducibilidad y logrando una mayor objetividad. Sin embargo, no sólo los discos intervertebrales son susceptibles de sufrir alguna patología. Los cuerpos vertebrales también pueden sufrir lesiones por diversas circunstancias. No obstante, antes de realizar cualquier tarea de diagnóstico, llevar a cabo una detección y segmentación precisa de los cuerpos vertebrales es un primer paso crucial. Así pues, la segunda parte de esta Tesis Doctoral tiene como objetivo el desarrollo de un método automático para la detección y segmentación de la columna vertebral por medio del análisis de Tomografía Computarizada. Llevar a cabo una segmentación automática y precisa es una tarea complicada debido principalmente a la gran dificultad para distinguir entre los cuerpos vertebrales y las costillas. Para solucionar este problema se han combinado dos métodos de segmentación diferentes: el primero utiliza un método Level-Set para llevar a cabo una segmentación inicial; el segundo utiliza un atlas probabilístico, para refinar la segmentación inicial, con un enfoque especial en la supresión de las costillas. Por tanto, se ha obtenido un volumen 3D indicando la probabilidad de cada voxel de pertenecer o no a la columna vertebral, por medio de un conjunto de imágenes correspondientes a 14 pacientes segmentadas manualmente por un experto. El mapa de probabilidad generado ha sido deformado y adaptado a cada uno de los 7 pacientes del grupo de prueba. Para evaluar los resultados de la segmentación y la mejora obtenida después de aplicar el atlas a la segmentación inicial, se ha utilizado el coeficiente Dice (DSC) y la distancia Hausdorff (HD). Los resultados han demostrado una mejora en la precisión de la segmentación de 11 mm de media en términos de HD, con una media global de 14,98 ± 1,32 mm. En términos de DSC se ha obtenido una mejora de un 1,3 % , con una media global de 91,75 ± 1,20 %. El estudio ha demostrado que el atlas es capaz de detectar y eliminar apropiadamente las estructuras costales[CA] En els últims 20 anys el sistema sanitari s'ha vist revolucionat per la tecnologia de la imatge, per la qual cosa el diagnòstic per imatge s'ha convertit en un pilar fonamental en el maneig dels pacients. Hui en dia la degeneració dels discos intervertebrals, l'hèrnia discal i l'estenosi del canal vertebral, són tres patologies molt comunes que afecten milions de persones i causen dolor d'esquena. El desenvolupament de sistemes CAD per a classificar i quantificar estes patologies s'ha incrementat en l'última dècada com una forma d'ajuda al radiòleg en el diagnòstic. Per tant, la primera part d'aquesta Tesi Doctoral té com a objectiu el desenvolupament d'un sistema CAD per a la classificació i quantificació de la patologia discal per mitjà de l'anàlisi d'Imatge per Ressonància Magnètica. Amb aquest fi s'han utilitzat dos grups de pacients distints, un com a grup d'entrenament (14 pacients) i l'altre com a grup de prova (53 pacients). Per a la classificació de la degeneració discal, s'ha desenvolupat un mètode basat en el càlcul de l'estructura del disc i del seu senyal d'intensitat. El mètode de detecció d'herniacions s'ha centrat en la segmentació del disc i la seua aproximació per una el·lipse, per a extraure així informació sobre la forma del disc. El mètode de detecció d'estenosi, basat en el senyal d'intensitat, ha sigut desenvolupat per a extraure el canal vertebral i amb l'aplicació de diferents tècniques detectar estrenyiments a l'altura dels discos i quantificar la gravetat dels mateixos. Els resultats han demostrat una alta precisió en la segmentació, amb un error inferior a l'1 %. En quant a la reproduïbilitat, s'ha obtingut un acord quasi perfecte (mesurat amb els coeficients CCI i k) per a totes les patologies analitzades. Els resultats obtinguts demostren que els mètodes desenvolupats poden servir d'ajuda al radiòleg en el diagnòstic, millorant la reproduïbilitat i aconseguint una major objectivitat. No obstant això, no sols els discos intervertebrals són susceptibles de patir alguna patologia. Els cossos vertebrals també poden patir lesions per diverses circumstàncies. Per tant, abans de realitzar qualsevol tasca de diagnòstic, dur a terme una detecció i segmentació precisa dels cossos vertebrals és un primer pas crucial. Així, doncs, la segona part d'aquesta Tesi Doctoral té com a objectiu el desenvolupament d'un mètode automàtic per a la detecció i segmentació de la columna vertebral per mitjà de l'anàlisi de Tomografia Computada. Dur a terme una segmentació automàtica i precisa és una tasca complicada degut principalment a la gran dificultat per a distingir entre els cossos vertebrals i les costelles. Per a solucionar aquest problema s'han combinat dos mètodes de segmentació diferents: el primer utilitza un mètode Level-Set per a dur a terme una segmentació inicial; el segon utilitza un atles probabilístic, per a refinar la segmentació inicial amb un enfocament especial en la supressió de les costelles. Per tant, s'ha obtingut un volum 3D indicant la probabilitat de cada voxel de pertànyer o no a la columna vertebral, per mitjà d'un conjunt d'imatges corresponents a 14 pacients (grup d'entrenament) segmentades manualment per un expert. El mapa de probabilitat generat ha sigut deformat i adaptat a cadascun dels 7 pacients del grup de prova. Per a avaluar els resultats de la segmentació i la millora obtinguda després d'aplicar l'atles a la segmentació inicial, s'ha utilitzat el coeficient Dice (DSC) i la distància Hausdorff (HD). Els resultats han demostrat una millora en la precisió de la segmentació d'11 mm de mitja en termes de HD, amb una mitja global de 14,98 ± 1,32 mm. S'ha obtingut una millora d'un 1,3 % en termes de DSC, amb una mitja global de 91,75 ± 1,20 %. L'estudi ha demostrat que l'atles és capaç de detectar i eliminar apropiadament les estructures costals alhora que millora la precisió de la segmentació.Ruiz España, S. (2016). CARACTERIZACIÓN CUANTITATIVA DE LA PATOLOGÍA DISCAL Y LUMBAR DEGENERATIVA MEDIANTE ANÁLISIS DE IMAGEN POR RESONANCIA MAGNÉTICA Y DETECCIÓN Y SEGMENTACIÓN DE LA COLUMNA VERTEBRAL EN PACIENTES ONCOLÓGICOS A PARTIR DEL ANÁLISIS DE IMAGEN EN TOMOGRAFÍA COMPUTARIZADA [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/68485TESI