Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis

Hip fracture risk assessment: Artificial neural network outperforms conditional logistic regression in an age- and sex-matched case control study

Copyright @ 2013 Tseng et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Background - Osteoporotic hip fractures with a significant morbidity and excess mortality among the elderly have imposed huge health and economic burdens on societies worldwide. In this age- and sex-matched case control study, we examined the risk factors of hip fractures and assessed the fracture risk by conditional logistic regression (CLR) and ensemble artificial neural network (ANN). The performances of these two classifiers were compared. Methods - The study population consisted of 217 pairs (149 women and 68 men) of fractures and controls with an age older than 60 years. All the participants were interviewed with the same standardized questionnaire including questions on 66 risk factors in 12 categories. Univariate CLR analysis was initially conducted to examine the unadjusted odds ratio of all potential risk factors. The significant risk factors were then tested by multivariate analyses. For fracture risk assessment, the participants were randomly divided into modeling and testing datasets for 10-fold cross validation analyses. The predicting models built by CLR and ANN in modeling datasets were applied to testing datasets for generalization study. The performances, including discrimination and calibration, were compared with non-parametric Wilcoxon tests. Results - In univariate CLR analyses, 16 variables achieved significant level, and six of them remained significant in multivariate analyses, including low T score, low BMI, low MMSE score, milk intake, walking difficulty, and significant fall at home. For discrimination, ANN outperformed CLR in both 16- and 6-variable analyses in modeling and testing datasets (p?<?0.005). For calibration, ANN outperformed CLR only in 16-variable analyses in modeling and testing datasets (p?=?0.013 and 0.047, respectively). Conclusions - The risk factors of hip fracture are more personal than environmental. With adequate model construction, ANN may outperform CLR in both discrimination and calibration. ANN seems to have not been developed to its full potential and efforts should be made to improve its performance.National Health Research Institutes in Taiwa

A multi-center milestone study of clinical vertebral CT segmentation

A multiple center milestone study of clinical vertebra segmentation is presented in this paper. Vertebra segmentation is a fundamental step for spinal image analysis and intervention. The first half of the study was conducted in the spine segmentation challenge in 2014 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) Workshop on Computational Spine Imaging (CSI 2014). The objective was to evaluate the performance of several state-of-the-art vertebra segmentation algorithms on computed tomography (CT) scans using ten training and five testing dataset, all healthy cases; the second half of the study was conducted after the challenge, where additional 5 abnormal cases are used for testing to evaluate the performance under abnormal cases. Dice coefficients and absolute surface distances were used as evaluation metrics. Segmentation of each vertebra as a single geometric unit, as well as separate segmentation of vertebra substructures, was evaluated. Five teams participated in the comparative study. The top performers in the study achieved Dice coefficient of 0.93 in the upper thoracic, 0.95 in the lower thoracic and 0.96 in the lumbar spine for healthy cases, and 0.88 in the upper thoracic, 0.89 in the lower thoracic and 0.92 in the lumbar spine for osteoporotic and fractured cases. The strengths and weaknesses of each method as well as future suggestion for improvement are discussed. This is the first multi-center comparative study for vertebra segmentation methods, which will provide an up-to-date performance milestone for the fast growing spinal image analysis and intervention

Computational Intelligence Methods for Medical Image Understanding, Visualization, and Interaction

Ph.DDOCTOR OF PHILOSOPH

Non-communicable Diseases, Big Data and Artificial Intelligence

This reprint includes 15 articles in the field of non-communicable Diseases, big data, and artificial intelligence, overviewing the most recent advances in the field of AI and their application potential in 3P medicine

Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

BACKGROUND: It is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0. METHODOLOGY: We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively. CONCLUSIONS: ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF

Identification of osteoporosis using ensemble deep learning model with panoramic radiographs and clinical covariates

Osteoporosis is becoming a global health issue due to increased life expectancy. However, it is difficult to detect in its early stages owing to a lack of discernible symptoms. Hence, screening for osteoporosis with widely used dental panoramic radiographs would be very cost-effective and useful. In this study, we investigate the use of deep learning to classify osteoporosis from dental panoramic radiographs. In addition, the effect of adding clinical covariate data to the radiographic images on the identification performance was assessed. For objective labeling, a dataset containing 778 images was collected from patients who underwent both skeletal-bone-mineral density measurement and dental panoramic radiography at a single general hospital between 2014 and 2020. Osteoporosis was assessed from the dental panoramic radiographs using convolutional neural network (CNN) models, including EfficientNet-b0, -b3, and -b7 and ResNet-18, -50, and -152. An ensemble model was also constructed with clinical covariates added to each CNN. The ensemble model exhibited improved performance on all metrics for all CNNs, especially accuracy and AUC. The results show that deep learning using CNN can accurately classify osteoporosis from dental panoramic radiographs. Furthermore, it was shown that the accuracy can be improved using an ensemble model with patient covariates

Mechanical and morphometric characterization of cancellous bone

[EN] Bone fracture is a social health problem of increasing magnitude because of its prevalence in aged population due to osteoporosis. Bone quality is often characterized by bone mineral density (BMD) measured at cancellous bone regions using dual-energy X-ray absorptiometry (DXA). However, BMD alone cannot predict several cases because not only density is important, but also microstructure plays an important role in cancellous bone strength. The mechanical properties can be used as indicators of bone integrity as a function of age, disease or treatment. Therefore, cancellous bone fracture characterization and its relationship to microstructure has not been completely solved in the literature and is relevant to improve fracture prediction. In this thesis, we aim at characterizing cancellous bone morphometry and mechanical behavior. Morphometry is estimated through the analysis of micro-computed tomography (micro-CT) images of vertebral cancellous bone specimens. With regards to the mechanical behavior, we calculate elastic, yield and failure properties at the apparent and tissue levels. To determine them, we followed different approaches: compression tests, finite element models and micro-CT phantoms. We have developed finite element models that reproduce the elastic and failure response of cancellous bone under compression conditions. We modeled failure as a combination of continuum damage mechanics and the element deletion technique. The numerical models permitted to estimate elastic and failure properties. Failure properties were consistent with results reported in the literature. Specifically, our results revealed that yield strain is relatively constant (0.7 %) over a range of apparent densities, while failure strain presents a wider range of variation. A single strain parameter (equivalent strain) was found as an accurate descriptor of cancellous bone compression failure. Image-based numerical models usually need for the action of a technician to segment the images. Therefore, we studied the sensitivity to variations of the segmentation threshold on the morphometry and the elastic properties of vertebral cancellous bone specimens of different bone volume fractions. The apparent modulus is highly sensitive to the segmentation threshold. We report variations between 45 and 120 % for a ± 15 % threshold variation. Other parameters, such as BS/BV, BS/TV, Tb.Sp, Tb.N, Conn.D and fractal dimension were influenced significantly. Digital image correlation (DIC) was applied to images taken during compression testing to analyze displacement fields at failure and characterize them. Some variables were explored to describe failure and a study is done about how DIC parameters influence the strain field obtained. Facet and step sizes have a relevant effect on the failure strain estimation, and an increment of both parameters reduces the strain estimation up to 40 %. Besides, several parameters combination led to correct failure pattern detection, so values reported in the literature should be referred to the parameters used. Furthermore, we explored if cancellous bone microstructure acts (non-speckle/texture approach) as a proper pattern to calculate displacements using DIC technique. As regards relationships between microstructure and mechanics, single and multiple parameter analysis were performed to assess the morphometric variables that control the explanation of mechanical properties variation. Bone volume fraction (BV/TV), bone surface to volume ratio (BS/BV), mean trabecular thickness (Tb.Th) and fractal dimension (D) presented the best linear correlations to the elastic properties, while both the yield and failure strains did not show correlation to any morphometric parameter. The regressions obtained permit to estimate those mechanical properties that describe the state of a specimen.[ES] Las fracturas óseas constituyen un problema social de salud con magnitud creciente por su prevalencia en la población de edad avanzada debido a la osteoporosis. La calidad del hueso suele caracterizarse mediante la estimación de la densidad mineral ósea (DMO) en regiones de hueso trabecular, utilizando absorciometría de rayos X de energía dual (DXA). No obstante, la DMO por si sola no es capaz de predecir numerosos casos de fractura porque no solo importa la pérdida de densidad, sino que la microestructura también tiene un papel principal en la resistencia del hueso. Las propiedades mecánicas del hueso pueden usarse como indicadores de su integridad en función de la edad, enfermedad o tratamiento. Por lo tanto, la caracterización de la fractura de hueso trabecular y su relación con la microestructura no se ha resuelto de forma completa en la literatura y es relevante para mejorar las predicciones de fractura. En esta tesis, nuestro principal objetivo es caracterizar la morfometría y el comportamiento mecánico del hueso trabecular. Estimamos la morfometría a través del análisis de imágenes obtenidas por micro tomografía computerizada (micro-CT) de muestras de hueso trabecular vertebral de cerdo. Respecto al comportamiento mecánico, calculamos propiedades elásticas, de plasticidad y fractura a escala aparente y de tejido. Para determinar esas propiedades, hemos seguido diferentes procedimientos: ensayos a compresión, modelos de elementos finitos y fantomas de calibración micro-CT. Los modelos de elementos finitos desarrollados reproducen la respuesta elástica y de fallo bajo condiciones de compresión en hueso trabecular, modelando el fallo como combinación de mecánica del daño contínuo y la técnica de eliminación de elementos. Los modelos numéricos desarrollados han permitido estimar propiedades elásticas y de fallo. En concreto, las deformaciones de inicio de fallo estimadas son relativamente constantes para las muestras analizadas (0.7 %), mientras que las deformaciones últimas de fallo presentan un rango de variación mayor. Por otro lado, encontramos que la deformación equivalente es el descriptor más preciso del fallo a compresión del hueso trabecular. Normalmente, los modelos numéricos basados en imágenes suelen necesitar la acción de un técnico para segmentar las imágenes. En este sentido, estudiamos la sensibilidad de la morfometría y la estimación de propiedades elásticas ante variaciones en el umbral de segmentación en muestras con distinta fracción en volumen. Hemos obtenido que la rigidez aparente es muy sensible a cambios en el umbral de segmentación, con variaciones entre 45 y 120 % para una variación de ± 15 % del umbral de segmentación. Otros parámetros, como BS/BV, BS/TV, Tb.Sp, Tb.N, Conn.D y la dimensión fractal se ven afectados significativamente. Por otro lado, hemos aplicado la técnica correlación digital por imagen (DIC) para caracterizar campos de desplazamientos en el fallo a compresión del hueso trabecular, a partir del análisis de imágenes tomadas durante el ensayo de las muestras. Además, estudiamos la influencia de algunos parámetros de la técnica DIC en el campo de deformaciones obtenido. También, hemos explorado la aplicación DIC sin el uso de moteado, utilizando como patrón de reconocimiento la propia microestructura trabecular. En relación al estudio de la influencia de la microestructura en la respuesta mecánica, hemos calculado correlaciones de uno y varios parámetros para analizar qué variables morfométricas explican la variación de las propiedades mecánicas. La fracción en volumen de hueso (BV/TV), la relación entre el área y el volumen de hueso (BS/BV), el espesor trabecular medio (Tb.Th) y la dimensión fractal (D) presentan las mejores correlaciones lineales respecto a las propiedades elásticas, mientras que las deformaciones de inicio de plasticidad y fractura no mostraron correlación con ningún parámetro morfométrico.[CA] Les fractures òssies constitueixen un problema social de salut amb magnitud creixent per la seua prevalença en la població d'edat avançada a causa de l'osteoporosi. La qualitat de l'os sol caracteritzar-se mitjançant l'estimació de la densitat mineral òssia (DMO) en regions d'os trabecular, utilitzant absorciometria de raigs X d'energia dual (DXA). No obstant això, la DMO per si sola no és capaç de predir nombrosos casos de fractura perquè no sols importa la pèrdua de densitat, sinó que la microestructura també té un paper principal en la resistència de l'os. Les propietats mecàniques de l'os poden usar-se com a indicadors de la seua integritat en funció de l'edat, malaltia o tractament. Per tant, la caracterització de la fractura d'os trabecular i la seua relació amb la microestructura no s'ha resolt de manera completa en la literatura i és rellevant per a millorar les prediccions de fractura. En aquesta tesi, el nostre principal objectiu és caracteritzar la morfometria i el comportament mecànic de l'os trabecular. Estimem la morfometria a través de l'anàlisi d'imatges obtingudes per micro tomografia automatitzada (micro-CT) de mostres d'os trabecular vertebral de porc. Respecte al comportament mecànic, calculem propietats elàstiques, de plasticitat i fractura a escala aparent i de teixit. Per a determinar aqueixes propietats, hem seguit diferents procediments: assajos a compressió, models d'elements finits i fantomas de calibratge micro-CT. Hem desenvolupat models d'elements finits que reprodueixen la resposta elàstica i de fallada sota condicions de compressió en os trabecular, modelant la fallada com a combinació de mecànica del dany continu i la tècnica d'eliminació d'elements. Els models numèrics desenvolupats han permés estimar propietats elàstiques i de fallada. Les nostres estimacions respecte a propietats de fallada són consistents amb valors reportats en la literatura. En concret, les deformacions d'inici de fallada estimades són relativament constants per a les mostres analitzades (0.7 %), mentre que les deformacions últimes de fallada presenten un rang de variació major. D'altra banda, trobem que la deformació equivalent és el descriptor més precís de la fallada a compressió de l'os trabecular. Els models numèrics basats en imatges solen necessitar l'acció d'un tècnic per a segmentar les imatges. En aquest sentit, estudiem la sensibilitat de la morfometria i l'estimació de propietats elàstiques davant variacions en el llindar de segmentació en mostres amb diferent fracció en volum. Hem obtingut que la rigidesa aparent és molt sensible a canvis en el llindar de segmentació, amb variacions entre 45 i 120 % per a una variació de ± 15 % del llindar de segmentació. Altres paràmetres, com BS/BV, BS/TV, Tb.Sp, Tb.N, Conn.D i la dimensió fractal es veuen afectats significativament. D'altra banda, hem aplicat la tècnica correlació digital per imatge (DIC) per a caracteritzar camps de desplaçaments en la fallada a compressió de l'os trabecular, a partir de l'anàlisi d'imatges preses durant l'assaig de les mostres. A més, estudiem la influència d'alguns paràmetres de la tècnica DIC en el camp de deformacions obtingut. També, hem explorat l'aplicació DIC sense l'ús de clapejat, utilitzant com a patró de reconeixement la pròpia microestructura trabecular. En relació a l'estudi de la influència de la microestructura en la resposta mecànica, hem calculat correlacions d'un i diversos paràmetres per a analitzar quines variables morfomètriques expliquen la variació de les propietats mecàniques. La fracció en volum d'os (BV/TV), la relació entre l'àrea i el volum d'os (BS/BV), la espessor trabecular mitjà (Tb.th) i la dimensió fractal (D) presenten les millors correlacions lineals respecte a les propietats elàstiques, mentre que les deformacions d'inici de plasticitat i fractura no van mostrar correlació amb cap paràmetre morfomètric.Belda González, R. (2020). Mechanical and morphometric characterization of cancellous bone [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/149376TESI