Further Details on Predicting IRT Difficulty

This supplementary material serves as technical appendix of the paper When AI Difficulty is Easy: The Explanatory Power of Predicting IRT Difficulty (Martínez-Plumed et al. 2022), published in The Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22). The following sections give detailed information about 1) data gathering for benchmarks; 2) IRT properties and methodology followed; 3) learning models configuration and hyperparameter setting; 4) differences between difficulty prediction and class prediction; 5) the deployment and results of alternative approaches for difficulty estimation; 6) specifics and results using a generic difficulty metric in different applications and 7) extended IRT applications.Martínez Plumed, F.; Castellano Falcón, D.; Monserrat Aranda, C.; Hernández Orallo, J. (2022). Further Details on Predicting IRT Difficulty. http://hdl.handle.net/10251/18133

Optimización del renderizado de volumen mediante la técnica shear-warping

El renderizado de volumen es una técnica para representar objetos tridimensionales en una imagen, a partir de las muestras obtenidas de dichos objetos. Debido a la gran cantidad de muestras necesarias para obtener una imagen nítida, el coste temporal de los diferentes algoritmos desarrollados en la actualidad no permite la representación de los objetos de forma eficiente, obligando a dichos algoritmos a recurrir a técnicas que requieren el uso de hardware específico y/o la disminución de la calidad de la imagen final. Este proyecto tiene como objetivo la implementación de un algoritmo que permita visualizar objetos tridimensionales en proyección paralela, de forma eficiente, esto es, con un coste temporal inferior a un segundo, a partir de un volumen de muestras de tamaño habitual (2563 voxels), en ordenadores de propósito general, sin comprometer la calidad de la imagen ni utilizar hardware específico. Para ello, se estudian las estrategias de los diferentes algoritmos presentes en la literatura, extrayendo sus ventajas e inconvenientes, así como diferentes técnicas de optimización del rendimiento de dichos algoritmos. A continuación, se diseñan las características de un algoritmo, además de las estructuras de datos apropiadas, para aplicar la técnica de descomposición matricial shear-warp, la cual permite combinar las ventajas de los algoritmos analizados junto con las técnicas de optimización de rendimiento. Por último, se implementa el algoritmo y se analizan los resultados obtenidos en cuanto a rendimiento y calidad de imagen, emitiendo las conclusiones pertinentes y proponiendo diversas alternativas a desarrollar en un futuro

Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning

[EN] A great challenge in osteoporosis clinical assessment is identifying patients at higher risk of hip fracture. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold-standard, but its classification accuracy is limited to 65%. DXA-based Finite Element (FE) models have been developed to predict the mechanical failure of the bone. Yet, their contribution has been modest. In this study, supervised machine learning (ML) is applied in conjunction with clinical and computationally driven mechanical attributes. Through this multi-technique approach, we aimed to obtain a predictive model that outperforms BMD and other clinical data alone, as well as to identify the best-learned ML classifier within a group of suitable algorithms. A total number of 137 postmenopausal women (81.4 +/- 6.95 years) were included in the study and separated into a fracture group (n = 89) and a control group (n = 48). A semi-automatic and patient-specific DXA-based FE model was used to generate mechanical attributes, describing the geometry, the impact force, bone structure and mechanical response of the bone after a sideways-fall. After preprocessing the whole dataset, 19 attributes were selected as predictors. Support Vector Machine (SVM) with radial basis function (RBF), Logistic Regression, Shallow Neural Networks and Random Forest were tested through a comprehensive validation procedure to compare their predictive performance. Clinical attributes were used alone in another experimental setup for the sake of comparison. SVM was confirmed to generate the best-learned algorithm for both experimental setups, including 19 attributes and only clinical attributes. The first, generated the best-learned model and outperformed BMD by 14pp. The results suggests that this approach could be easily integrated for effective prediction of hip fracture without interrupting the actual clinical workflow.This study was partially funded by two grants Catedra UPVFundacion Quaes, obtained by Eduardo Villamor Medina and Antonio Cutillas Pardines, and one FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor Medina.Villamor, E.; Monserrat Aranda, C.; Del Río, L.; Romero-Martín, J.; Rupérez Moreno, MJ. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine. 193:1-11. https://doi.org/10.1016/j.cmpb.2020.105484S111193Holt, G., Smith, R., Duncan, K., Hutchison, J. D., & Reid, D. (2009). Changes in population demographics and the future incidence of hip fracture. Injury, 40(7), 722-726. doi:10.1016/j.injury.2008.11.004Cooper, C., Campion, G., & Melton, L. J. (1992). Hip fractures in the elderly: A world-wide projection. Osteoporosis International, 2(6), 285-289. doi:10.1007/bf01623184Cooper, C., Atkinson, E. J., Jacobsen, S. J., O’Fallon, W. M., & Melton, L. J. (1993). Population-Based Study of Survival after Osteoporotic Fractures. American Journal of Epidemiology, 137(9), 1001-1005. doi:10.1093/oxfordjournals.aje.a116756Geusens, P., van Geel, T., & van den Bergh, J. (2010). Can hip fracture prediction in women be estimated beyond bone mineral density measurement alone? Therapeutic Advances in Musculoskeletal Disease, 2(2), 63-77. doi:10.1177/1759720x09359541El Maghraoui, A., & Roux, C. (2008). DXA scanning in clinical practice. QJM, 101(8), 605-617. doi:10.1093/qjmed/hcn022Chevalley, T., Rizzoli, R., Nydegger, V., Slosman, D., Tkatch, L., Rapin, C.-H., … Bonjour, J.-P. (1991). Preferential low bone mineral density of the femoral neck in patients with a recent fracture of the proximal femur. Osteoporosis International, 1(3), 147-154. doi:10.1007/bf01625444Li, N., Li, X., Xu, L., Sun, W., Cheng, X., & Tian, W. (2013). Comparison of QCT and DXA: Osteoporosis Detection Rates in Postmenopausal Women. International Journal of Endocrinology, 2013, 1-5. doi:10.1155/2013/895474Fountoulis, G., Kerenidi, T., Kokkinis, C., Georgoulias, P., Thriskos, P., Gourgoulianis, K., … Vlychou, M. (2016). Assessment of Bone Mineral Density in Male Patients with Chronic Obstructive Pulmonary Disease by DXA and Quantitative Computed Tomography. International Journal of Endocrinology, 2016, 1-6. doi:10.1155/2016/6169721Yang, L., Palermo, L., Black, D. M., & Eastell, R. (2014). Prediction of Incident Hip Fracture with the Estimated Femoral Strength by Finite Element Analysis of DXA Scans in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research, 29(12), 2594-2600. doi:10.1002/jbmr.2291Dall’Ara, E., Eastell, R., Viceconti, M., Pahr, D., & Yang, L. (2016). Experimental validation of DXA-based finite element models for prediction of femoral strength. Journal of the Mechanical Behavior of Biomedical Materials, 63, 17-25. doi:10.1016/j.jmbbm.2016.06.004Enns-Bray, W. S., Bahaloo, H., Fleps, I., Pauchard, Y., Taghizadeh, E., Sigurdsson, S., … Helgason, B. (2019). Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort. Bone, 120, 25-37. doi:10.1016/j.bone.2018.09.014Terzini, M., Aldieri, A., Rinaudo, L., Osella, G., Audenino, A. L., & Bignardi, C. (2019). Improving the Hip Fracture Risk Prediction Through 2D Finite Element Models From DXA Images: Validation Against 3D Models. Frontiers in Bioengineering and Biotechnology, 7. doi:10.3389/fbioe.2019.00220Nguyen, N. D., Frost, S. A., Center, J. R., Eisman, J. A., & Nguyen, T. V. (2008). Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporosis International, 19(10), 1431-1444. doi:10.1007/s00198-008-0588-0Kanis, J. A., Oden, A., Johansson, H., Borgström, F., Ström, O., & McCloskey, E. (2009). FRAX® and its applications to clinical practice. Bone, 44(5), 734-743. doi:10.1016/j.bone.2009.01.373Bolland, M. J., Siu, A. T., Mason, B. H., Horne, A. M., Ames, R. W., Grey, A. B., … Reid, I. R. (2011). Evaluation of the FRAX and Garvan fracture risk calculators in older women. Journal of Bone and Mineral Research, 26(2), 420-427. doi:10.1002/jbmr.215Kruse, C., Eiken, P., & Vestergaard, P. (2016). Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporosis International, 28(3), 819-832. doi:10.1007/s00198-016-3828-8Nishiyama, K. K., Ito, M., Harada, A., & Boyd, S. K. (2013). Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporosis International, 25(2), 619-626. doi:10.1007/s00198-013-2459-6Jiang, P., Missoum, S., & Chen, Z. (2015). Fusion of clinical and stochastic finite element data for hip fracture risk prediction. Journal of Biomechanics, 48(15), 4043-4052. doi:10.1016/j.jbiomech.2015.09.044Naylor, K. E., McCloskey, E. V., Eastell, R., & Yang, L. (2013). Use of DXA-based finite element analysis of the proximal femur in a longitudinal study of hip fracture. Journal of Bone and Mineral Research, 28(5), 1014-1021. doi:10.1002/jbmr.1856Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Rossman, T., Kushvaha, V., & Dragomir-Daescu, D. (2015). QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling. Computer Methods in Biomechanics and Biomedical Engineering, 19(2), 208-216. doi:10.1080/10255842.2015.1006209Si, H. (2015). TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator. ACM Transactions on Mathematical Software, 41(2), 1-36. doi:10.1145/2629697Yang, L., Peel, N., Clowes, J. A., McCloskey, E. V., & Eastell, R. (2009). Use of DXA-Based Structural Engineering Models of the Proximal Femur to Discriminate Hip Fracture. Journal of Bone and Mineral Research, 24(1), 33-42. doi:10.1359/jbmr.080906Schileo, E., Dall’Ara, E., Taddei, F., Malandrino, A., Schotkamp, T., Baleani, M., & Viceconti, M. (2008). An accurate estimation of bone density improves the accuracy of subject-specific finite element models. Journal of Biomechanics, 41(11), 2483-2491. doi:10.1016/j.jbiomech.2008.05.017Morgan, E. F., & Keaveny, T. M. (2001). Dependence of yield strain of human trabecular bone on anatomic site. Journal of Biomechanics, 34(5), 569-577. doi:10.1016/s0021-9290(01)00011-2Morgan, E. F., Bayraktar, H. H., & Keaveny, T. M. (2003). Trabecular bone modulus–density relationships depend on anatomic site. Journal of Biomechanics, 36(7), 897-904. doi:10.1016/s0021-9290(03)00071-xBayraktar, H. H., Morgan, E. F., Niebur, G. L., Morris, G. E., Wong, E. K., & Keaveny, T. M. (2004). Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. Journal of Biomechanics, 37(1), 27-35. doi:10.1016/s0021-9290(03)00257-4Ün, K., Bevill, G., & Keaveny, T. M. (2006). The effects of side-artifacts on the elastic modulus of trabecular bone. Journal of Biomechanics, 39(11), 1955-1963. doi:10.1016/j.jbiomech.2006.05.012Schileo, E., Balistreri, L., Grassi, L., Cristofolini, L., & Taddei, F. (2014). To what extent can linear finite element models of human femora predict failure under stance and fall loading configurations? Journal of Biomechanics, 47(14), 3531-3538. doi:10.1016/j.jbiomech.2014.08.024Wirtz, D. C., Schiffers, N., Pandorf, T., Radermacher, K., Weichert, D., & Forst, R. (2000). Critical evaluation of known bone material properties to realize anisotropic FE-simulation of the proximal femur. Journal of Biomechanics, 33(10), 1325-1330. doi:10.1016/s0021-9290(00)00069-5Eckstein, F., Wunderer, C., Boehm, H., Kuhn, V., Priemel, M., Link, T. M., & Lochmüller, E.-M. (2003). Reproducibility and Side Differences of Mechanical Tests for Determining the Structural Strength of the Proximal Femur. Journal of Bone and Mineral Research, 19(3), 379-385. doi:10.1359/jbmr.0301247Orwoll, E. S., Marshall, L. M., Nielson, C. M., Cummings, S. R., Lapidus, J., … Cauley, J. A. (2009). Finite Element Analysis of the Proximal Femur and Hip Fracture Risk in Older Men. Journal of Bone and Mineral Research, 24(3), 475-483. doi:10.1359/jbmr.081201Choi, W. J., Cripton, P. A., & Robinovitch, S. N. (2014). Effects of hip abductor muscle forces and knee boundary conditions on femoral neck stresses during simulated falls. Osteoporosis International, 26(1), 291-301. doi:10.1007/s00198-014-2812-4Van den Kroonenberg, A. J., Hayes, W. C., & McMahon, T. A. (1995). Dynamic Models for Sideways Falls From Standing Height. Journal of Biomechanical Engineering, 117(3), 309-318. doi:10.1115/1.2794186Robinovitch, S. N., Hayes, W. C., & McMahon, T. A. (1991). Prediction of Femoral Impact Forces in Falls on the Hip. Journal of Biomechanical Engineering, 113(4), 366-374. doi:10.1115/1.2895414Robinovitch, S. N., McMahon, T. A., & Hayes, W. C. (1995). Force attenuation in trochanteric soft tissues during impact from a fall. Journal of Orthopaedic Research, 13(6), 956-962. doi:10.1002/jor.1100130621Dufour, A. B., Roberts, B., Broe, K. E., Kiel, D. P., Bouxsein, M. L., & Hannan, M. T. (2011). The factor-of-risk biomechanical approach predicts hip fracture in men and women: the Framingham Study. Osteoporosis International, 23(2), 513-520. doi:10.1007/s00198-011-1569-2Schileo, E., Taddei, F., Cristofolini, L., & Viceconti, M. (2008). Subject-specific finite element models implementing a maximum principal strain criterion are able to estimate failure risk and fracture location on human femurs tested in vitro. Journal of Biomechanics, 41(2), 356-367. doi:10.1016/j.jbiomech.2007.09.009Mautalen, C. A., Vega, E. M., & Einhorn, T. A. (1996). Are the etiologies of cervical and trochanteric hip fractures different? Bone, 18(3), S133-S137. doi:10.1016/8756-3282(95)00490-4Yang, S., Leslie, W. D., Luo, Y., Goertzen, A. L., Ahmed, S., Ward, L. M., … Lix, L. M. (2017). Automated DXA-based finite element analysis for hip fracture risk stratification: a cross-sectional study. Osteoporosis International, 29(1), 191-200. doi:10.1007/s00198-017-4232-8Testi, D., Viceconti, M., Cappello, A., & Gnudi, S. (2002). Prediction of Hip Fracture Can Be Significantly Improved by a Single Biomedical Indicator. Annals of Biomedical Engineering, 30(6), 801-807. doi:10.1114/1.1495866Langton, C. M., Pisharody, S., & Keyak, J. H. (2008). Generation of a 3D proximal femur shape from a single projection 2D radiographic image. Osteoporosis International, 20(3), 455-461. doi:10.1007/s00198-008-0665-4Humbert, L., Martelli, Y., Fonolla, R., Steghofer, M., Di Gregorio, S., Malouf, J., … Barquero, L. M. D. R. (2017). 3D-DXA: Assessing the Femoral Shape, the Trabecular Macrostructure and the Cortex in 3D from DXA images. IEEE Transactions on Medical Imaging, 36(1), 27-39. doi:10.1109/tmi.2016.2593346Keyak, J. H., Sigurdsson, S., Karlsdottir, G., Oskarsdottir, D., Sigmarsdottir, A., Zhao, S., … Lang, T. F. (2011). Male–female differences in the association between incident hip fracture and proximal femoral strength: A finite element analysis study. Bone, 48(6), 1239-1245. doi:10.1016/j.bone.2011.03.682Lobo, E., Marcos, G., Santabárbara, J., Salvador-Rosés, H., Lobo-Escolar, L., De la Cámara, C., … Lobo-Escolar, A. (2017). Gender differences in the incidence of and risk factors for hip fracture: A 16-year longitudinal study in a southern European population. Maturitas, 97, 38-43. doi:10.1016/j.maturitas.2016.12.00

Risk Assessment of Hip Fracture Based on Machine Learning

[EN] Identifying patients with high risk of hip fracture is a great challenge in osteoporosis clinical assessment. Bone Mineral Density (BMD) measured by Dual-Energy X-Ray Absorptiometry (DXA) is the current gold standard in osteoporosis clinical assessment. However, its classification accuracy is only around 65%. In order to improve this accuracy, this paper proposes the use of Machine Learning (ML) models trained with data from a biomechanical model that simulates a sideways-fall. Machine Learning (ML) models are models able to learn and to make predictions from data. During a training process, ML models learn a function that maps inputs and outputs without previous knowledge of the problem. The main advantage of ML models is that once the mapping function is constructed, they can make predictions for complex biomechanical behaviours in real time. However, despite the increasing popularity of Machine Learning (ML) models and their wide application to many fields of medicine, their use as hip fracture predictors is still limited. This paper proposes the use of ML models to assess and predict hip fracture risk. Clinical, geometric, and biomechanical variables from the finite element simulation of a side fall are used as independent variables to train the models. Among the different tested models, Random Forest stands out, showing its capability to outperform BMD-DXA, achieving an accuracy over 87%, with specificity over 92% and sensitivity over 83%.This study was partially funded by the FPI grant (FPI-SP20170111) from the Universitat Politecnica de Valencia obtained by Eduardo Villamor.Galassi, A.; Martín-Guerrero, JD.; Villamor, E.; Monserrat Aranda, C.; Rupérez Moreno, MJ. (2020). Risk Assessment of Hip Fracture Based on Machine Learning. Applied bionics and biomechanics (Online). 2020:1-13. https://doi.org/10.1155/2020/8880786S1132020World Health OrganizationAssessment of fracture risk and its application to screening for postmenopausal osteoporosis. Report of a WHO Study Group1994http://www.who.int/iris/handle/10665/39142, http://apps.who.int//iris/handle/10665/39142Cooper, C., Campion, G., & Melton, L. J. (1992). Hip fractures in the elderly: A world-wide projection. Osteoporosis International, 2(6), 285-289. doi:10.1007/bf01623184El Maghraoui, A., & Roux, C. (2008). DXA scanning in clinical practice. QJM, 101(8), 605-617. doi:10.1093/qjmed/hcn022Testi, D., Viceconti, M., Cappello, A., & Gnudi, S. (2002). Prediction of Hip Fracture Can Be Significantly Improved by a Single Biomedical Indicator. Annals of Biomedical Engineering, 30(6), 801-807. doi:10.1114/1.1495866Nguyen, N. D., Frost, S. A., Center, J. R., Eisman, J. A., & Nguyen, T. V. (2008). Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporosis International, 19(10), 1431-1444. doi:10.1007/s00198-008-0588-0Bolland, M. J., Siu, A. T., Mason, B. H., Horne, A. M., Ames, R. W., Grey, A. B., … Reid, I. R. (2011). Evaluation of the FRAX and Garvan fracture risk calculators in older women. Journal of Bone and Mineral Research, 26(2), 420-427. doi:10.1002/jbmr.215Fountoulis, G., Kerenidi, T., Kokkinis, C., Georgoulias, P., Thriskos, P., Gourgoulianis, K., … Vlychou, M. (2016). Assessment of Bone Mineral Density in Male Patients with Chronic Obstructive Pulmonary Disease by DXA and Quantitative Computed Tomography. International Journal of Endocrinology, 2016, 1-6. doi:10.1155/2016/6169721Pellicer-Valero, O. J., Rupérez, M. J., Martínez-Sanchis, S., & Martín-Guerrero, J. D. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications, 143, 113083. doi:10.1016/j.eswa.2019.113083Martínez-Martínez, F., Rupérez-Moreno, M. J., Martínez-Sober, M., Solves-Llorens, J. A., Lorente, D., Serrano-López, A. J., … Martín-Guerrero, J. D. (2017). A finite element-based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real-time. Computers in Biology and Medicine, 90, 116-124. doi:10.1016/j.compbiomed.2017.09.019Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94-98. doi:10.7861/futurehosp.6-2-94Kruse, C., Eiken, P., & Vestergaard, P. (2016). Clinical fracture risk evaluated by hierarchical agglomerative clustering. Osteoporosis International, 28(3), 819-832. doi:10.1007/s00198-016-3828-8Ho-Le, T. P., Center, J. R., Eisman, J. A., Nguyen, T. V., & Nguyen, H. T. (2017). Prediction of hip fracture in post-menopausal women using artificial neural network approach. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2017.8037784Dall’Ara, E., Eastell, R., Viceconti, M., Pahr, D., & Yang, L. (2016). Experimental validation of DXA-based finite element models for prediction of femoral strength. Journal of the Mechanical Behavior of Biomedical Materials, 63, 17-25. doi:10.1016/j.jmbbm.2016.06.004Enns-Bray, W. S., Bahaloo, H., Fleps, I., Pauchard, Y., Taghizadeh, E., Sigurdsson, S., … Helgason, B. (2019). Biofidelic finite element models for accurately classifying hip fracture in a retrospective clinical study of elderly women from the AGES Reykjavik cohort. Bone, 120, 25-37. doi:10.1016/j.bone.2018.09.014Testi, D., Viceconti, M., Baruffaldi, F., & Cappello, A. (1999). Risk of fracture in elderly patients: a new predictive index based on bone mineral density and finite element analysis. Computer Methods and Programs in Biomedicine, 60(1), 23-33. doi:10.1016/s0169-2607(99)00007-3Yang, L., Palermo, L., Black, D. M., & Eastell, R. (2014). Prediction of Incident Hip Fracture with the Estimated Femoral Strength by Finite Element Analysis of DXA Scans in the Study of Osteoporotic Fractures. Journal of Bone and Mineral Research, 29(12), 2594-2600. doi:10.1002/jbmr.2291Luo, Y., Ahmed, S., & Leslie, W. D. (2018). Automation of a DXA-based finite element tool for clinical assessment of hip fracture risk. Computer Methods and Programs in Biomedicine, 155, 75-83. doi:10.1016/j.cmpb.2017.11.020Terzini, M., Aldieri, A., Rinaudo, L., Osella, G., Audenino, A. L., & Bignardi, C. (2019). Improving the Hip Fracture Risk Prediction Through 2D Finite Element Models From DXA Images: Validation Against 3D Models. Frontiers in Bioengineering and Biotechnology, 7. doi:10.3389/fbioe.2019.00220Nishiyama, K. K., Ito, M., Harada, A., & Boyd, S. K. (2013). Classification of women with and without hip fracture based on quantitative computed tomography and finite element analysis. Osteoporosis International, 25(2), 619-626. doi:10.1007/s00198-013-2459-6Jiang, P., Missoum, S., & Chen, Z. (2015). Fusion of clinical and stochastic finite element data for hip fracture risk prediction. Journal of Biomechanics, 48(15), 4043-4052. doi:10.1016/j.jbiomech.2015.09.044Ferizi, U., Besser, H., Hysi, P., Jacobs, J., Rajapakse, C. S., Chen, C., … Chang, G. (2018). Artificial Intelligence Applied to Osteoporosis: A Performance Comparison of Machine Learning Algorithms in Predicting Fragility Fractures From MRI Data. Journal of Magnetic Resonance Imaging, 49(4), 1029-1038. doi:10.1002/jmri.26280Villamor, E., Monserrat, C., Del Río, L., Romero-Martín, J. A., & Rupérez, M. J. (2020). Prediction of osteoporotic hip fracture in postmenopausal women through patient-specific FE analyses and machine learning. Computer Methods and Programs in Biomedicine, 193, 105484. doi:10.1016/j.cmpb.2020.105484Rossman, T., Kushvaha, V., & Dragomir-Daescu, D. (2015). QCT/FEA predictions of femoral stiffness are strongly affected by boundary condition modeling. Computer Methods in Biomechanics and Biomedical Engineering, 19(2), 208-216. doi:10.1080/10255842.2015.1006209Si, H. (2015). TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator. ACM Transactions on Mathematical Software, 41(2), 1-36. doi:10.1145/2629697Morgan, E. F., & Keaveny, T. M. (2001). Dependence of yield strain of human trabecular bone on anatomic site. Journal of Biomechanics, 34(5), 569-577. doi:10.1016/s0021-9290(01)00011-2Morgan, E. F., Bayraktar, H. H., & Keaveny, T. M. (2003). Trabecular bone modulus–density relationships depend on anatomic site. Journal of Biomechanics, 36(7), 897-904. doi:10.1016/s0021-9290(03)00071-xBayraktar, H. H., Morgan, E. F., Niebur, G. L., Morris, G. E., Wong, E. K., & Keaveny, T. M. (2004). Comparison of the elastic and yield properties of human femoral trabecular and cortical bone tissue. Journal of Biomechanics, 37(1), 27-35. doi:10.1016/s0021-9290(03)00257-4Wirtz, D. C., Schiffers, N., Pandorf, T., Radermacher, K., Weichert, D., & Forst, R. (2000). Critical evaluation of known bone material properties to realize anisotropic FE-simulation of the proximal femur. Journal of Biomechanics, 33(10), 1325-1330. doi:10.1016/s0021-9290(00)00069-5Eckstein, F., Wunderer, C., Boehm, H., Kuhn, V., Priemel, M., Link, T. M., & Lochmüller, E.-M. (2003). Reproducibility and Side Differences of Mechanical Tests for Determining the Structural Strength of the Proximal Femur. Journal of Bone and Mineral Research, 19(3), 379-385. doi:10.1359/jbmr.0301247Orwoll, E. S., Marshall, L. M., Nielson, C. M., Cummings, S. R., Lapidus, J., … Cauley, J. A. (2009). Finite Element Analysis of the Proximal Femur and Hip Fracture Risk in Older Men. 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Interactive evaluation of surgery skills in surgery simulators: A new method based on string matching algorithms

The final publication is available at Springer via http://dx.doi.org/10.1007/s11548-013-0881-zMonserrat Aranda, C.; Lucas, A.; Hernández-Orallo, J.; Rupérez Moreno, MJ.; Alcañiz Raya, ML. (2013). Interactive evaluation of surgery skills in surgery simulators: A new method based on string matching algorithms. International Journal of Computer Assisted Radiology and Surgery. 8(1 Supplement):373-374. doi:10.1007/s11548-013-0881-zS37337481 Supplemen

Deformable brain atlas validation of the location of subthalamic nucleus using T1-weighted MR images of patients operated on for Parkinson's

[EN] Parkinson¿s disease is a degenerative disease of the central nervous system. One of the most effective treatments is deep brain stimulation. This technique requires the localization of an objective structure: the subthalamic nucleus. Unfortunately this structure is difficult to locate. In this work the creation of a deformable brain atlas that enables the identification of the subthalamic nucleus in T1-weighted magnetic resonance imaging (MRI) in an automatic, precise and fast way is presented. The system has been validated using data from 10 patients (20 nucleus) operated on for Parkinson¿s. Our system offers better results using a Wendland function with an error of 1.8853 ± 0.9959 mm.Ortega Pérez, M.; Juan Lizandra, MC.; Alcañiz Raya, ML.; Gil Gómez, JA.; Monserrat Aranda, C. (2008). Deformable brain atlas validation of the location of subthalamic nucleus using T1-weighted MR images of patients operated on for Parkinson's. Computerized Medical Imaging and Graphics. 32(5):367-378. doi:10.1016/j.compmedimag.2008.02.003S36737832

A new methodology for the in vivo estimation of the elastic constants that characterize the patient-specific biomechanical behavior of the human cornea

This work presents a methodology for the in vivo characterization of the complete biomechanical behavior of the human cornea of each patient. Specifically, the elastic constants of a hyperelastic, second-order Ogden model were estimated for 24 corneas corresponding to 12 patients. The finite element method was applied to simulate the deformation of human corneas due to non-contact tonometry, and an iterative search controlled by a genetic heuristic was used to estimate the elastic parameters that most closely approximates the simulated deformation to the real one. The results from a synthetic experiment showed that these parameters can be estimated with an error of about 5%. The results of 24 in vivo corneas showed an overlap of about 90% between simulation and real deformed cornea and a modified Hausdorff distance of 25 mu m, which indicates the great accuracy of the proposed methodology. (C) 2014 Elsevier Ltd. All rights reserved.This project has been partially funded by MECD (reference AP2009-2414) and MINECO (INNPACTO, IPT-2012-0495-300000).Lago, MA.; Rupérez Moreno, MJ.; Martínez Martínez, F.; Monserrat Aranda, C.; Larra, E.; Gueell, JL.; Peris-Martinez, C. (2015). A new methodology for the in vivo estimation of the elastic constants that characterize the patient-specific biomechanical behavior of the human cornea. Journal of Biomechanics. 48(1):38-43. https://doi.org/10.1016/j.jbiomech.2014.11.009S384348

NaRALap: augmented reality system for navigation in laparoscopic surgery

The final publication is available at Springer via http://dx.doi.org/10.1007/s11548-011-0579-z.The AR system has a good resolution and currently is used for the placement of the trocars. Possible improvements will be performed to make the system independent of the camera position or to use natural marks. The biomechanical model and the AR algorithms will be combined with a tracker, for tracking the surgical instruments, in order to implement a valid system for liver biopsies. It will take into account the deformation due to the pneumoperitoneum and due to the breath of the patient. To develop the navigator that will guide the laparoscopic interventions, both AR system and biomechanical model will be combined with the laparoscopic camera in order to make an easier environment with only one vision in a 2D monitor.This work has been supported by the project MITYC (ref. TSI020100-2009-189). We would like to express our deep gratitude to the Hospital Clínica Benidorm for its participation in this project.López-Mir, F.; Martínez Martínez, F.; Fuertes Cebrián, JJ.; Lago, MA.; Rupérez Moreno, MJ.; Naranjo Ornedo, V.; Monserrat Aranda, C. (2011). NaRALap: augmented reality system for navigation in laparoscopic surgery. International Journal of Computer Assisted Radiology and Surgery. 6:98-99. https://doi.org/10.0.3.239/s11548-011-0579-zS9899

Evaluation based on the gradient method of the elastic properties of human tissues in vivo

En la actualidad la simulación numérica del comportamiento mecánico de tejidos humanos en el campo de la medicina es un ámbito de estudio que ha despertado gran interés en la comunidad científica. El estudio del comportamiento de dichos tejidos conlleva una gran dificultad, en parte, atribuida al hecho de que el comportamiento de dichos tejidos cambia de paciente a paciente y en numerosas ocasiones no es posible realizar experimentos directos sobre el tejido para determinar sus propiedades elásticas. Para tal fin, en el presente trabajo se propone un método para hallar dichas propiedades asumiendo un modelo constitutivo de Mooney-Rivlin. Dicho método se basa en la información propor- cionada por imágenes médicas en dos situaciones de deformación del órgano y, mediante un proceso de optimización basado en el gradiente se obtienen, con precisión, las propiedades elásticas del modelo constitutivo. Los experimentos numéricos realizados demuestran la validez del método para el ejemplo utilizado.At present, the numerical simulation of the mechanical behavior of human tissues in the field of medicine is a field of study that has aroused great interest in the scientific community. The study of the behavior of these tissues entails a great difficulty, partly attributed to the fact that the behavior of these tissues changes from patient to patient and in many occasions it is not possible to perform direct experiments on the tissue to determine its elastic properties. For this purpose, the present work proposes a method to find these properties assuming a constitutive model of Mooney-Rivlin. This method is based on the information provided by medical images in two situations of organ deformation and, through a process of optimization based on the gradient, the elastic properties of the constitutive model are obtained with precision. The numerical experiments performed demonstrate the validity of the method for the example used

Segmentation of the breast skin and its influence in the simulation of the breast compresion during an X-Ray mammography

A novel method of skin segmentation is presented aimed to obtain as many pixels belonging to the real skin as possible. This method is validated by experts in radiology. In addition, a biomechanical model of the breast, which considers the skin segmented in this way, is constructed to study the influence of considering real skin in the simulation of the breast compression during an X-ray mammography. The reaction forces of the plates are obtained and compared with the reaction forces obtained using classical methods that model the skin as a 2D membranes that cover all the breast. The results of this work show that, in most of the cases, the method of skin segmentation is accurate and that real skin should be considered in the simulation of the breast compression during the X-ray mammographies. Copyright © 2012 J. A. Solves Llorens et al.This project has been partially funded by the Regional Valencian Government through IMPIVA with FEDER funding (reference IMIDTF/2010/111), by CDTI (reference IDI-20101153), and by MICINN (reference TIN2010-20999-C04-01). The authors would like to express their gratitude to the personnel from the Hospitals HCB and La Fe.Solves Llorens, JA.; Rupérez Moreno, MJ.; Monserrat Aranda, C.; Feliu, E.; García, M.; Lloret, M. (2012). Segmentation of the breast skin and its influence in the simulation of the breast compresion during an X-Ray mammography. Scientific World Journal. 2012:1-8. https://doi.org/10.1100/2012/876489S182012Malur, S., Wurdinger, S., Moritz, A., Michels, W., & Schneider, A. (2000). Comparison of written reports of mammography, sonography and magnetic resonance mammography for preoperative evaluation of breast lesions, with special emphasis on magnetic resonance mammography. Breast Cancer Research, 3(1). doi:10.1186/bcr271Rajagopal, V., Nielsen, P. M. F., & Nash, M. P. (2010). Modeling breast biomechanics for multi‐modal image analysis—successes and challenges. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 2(3), 293-304. doi:10.1002/wsbm.58Rajagopal, V., Lee, A., Chung, J.-H., Warren, R., Highnam, R. P., Nash, M. P., & Nielsen, P. M. F. (2008). Creating Individual-specific Biomechanical Models of the Breast for Medical Image Analysis. Academic Radiology, 15(11), 1425-1436. doi:10.1016/j.acra.2008.07.017Ruiter, N. V., Stotzka, R., Muller, T.-O., Gemmeke, H., Reichenbach, J. R., & Kaiser, W. A. (2006). Model-based registration of X-ray mammograms and MR images of the female breast. IEEE Transactions on Nuclear Science, 53(1), 204-211. doi:10.1109/tns.2005.862983Kellner, A. L., Nelson, T. R., Cervino, L. I., & Boone, J. M. (2007). Simulation of Mechanical Compression of Breast Tissue. IEEE Transactions on Biomedical Engineering, 54(10), 1885-1891. doi:10.1109/tbme.2007.893493Del Palomar, A. P., Calvo, B., Herrero, J., López, J., & Doblaré, M. (2008). A finite element model to accurately predict real deformations of the breast. Medical Engineering & Physics, 30(9), 1089-1097. doi:10.1016/j.medengphy.2008.01.005Willson, S. A., Adam, E. J., & Tucker, A. K. (1982). Patterns of breast skin thickness in normal mammograms. Clinical Radiology, 33(6), 691-693. doi:10.1016/s0009-9260(82)80407-8Huang, S.-Y., Boone, J. M., Yang, K., Kwan, A. L. C., & Packard, N. J. (2008). The effect of skin thickness determined using breast CT on mammographic dosimetry. Medical Physics, 35(4), 1199-1206. doi:10.1118/1.2841938Van Engeland, S., Snoeren, P. R., Huisman, H., Boetes, C., & Karssemeijer, N. (2006). Volumetric breast density estimation from full-field digital mammograms. IEEE Transactions on Medical Imaging, 25(3), 273-282. doi:10.1109/tmi.2005.862741Khazen, M., Warren, R. M. L., Boggis, C. R. M., Bryant, E. C., Reed, S., … Warsi, I. (2008). A Pilot Study of Compositional Analysis of the Breast and Estimation of Breast Mammographic Density Using Three-Dimensional T1-Weighted Magnetic Resonance Imaging. Cancer Epidemiology Biomarkers & Prevention, 17(9), 2268-2274. doi:10.1158/1055-9965.epi-07-2547Nie, K., Chen, J.-H., Chan, S., Chau, M.-K. I., Yu, H. J., Bahri, S., … Su, M.-Y. (2008). Development of a quantitative method for analysis of breast density based on three-dimensional breast MRI. Medical Physics, 35(12), 5253-5262. doi:10.1118/1.3002306Nie, K., Chang, D., Chen, J.-H., Shih, T.-C., Hsu, C.-C., Nalcioglu, O., & Su, M.-Y. (2009). Impact of skin removal on quantitative measurement of breast density using MRI. Medical Physics, 37(1), 227-233. doi:10.1118/1.3271353Gil, D., & Radeva, P. (2004). A Regularized Curvature Flow Designed for a Selective Shape Restoration. IEEE Transactions on Image Processing, 13(11), 1444-1458. doi:10.1109/tip.2004.836181Osher, S., & Tsai, R. (2003). Level Set Methods and Their Applications in Image Science. Communications in Mathematical Sciences, 1(4), 1-20. doi:10.4310/cms.2003.v1.n4.a1Tanner, C., Schnabel, J. A., Hill, D. L. G., Hawkes, D. J., Leach, M. O., & Hose, D. R. (2006). Factors influencing the accuracy of biomechanical breast models. Medical Physics, 33(6Part1), 1758-1769. doi:10.1118/1.2198315Hendriks, F. M., Brokken, D., van Eemeren, J. T. W. M., Oomens, C. W. J., Baaijens, F. P. T., & Horsten, J. B. A. M. (2003). A numerical-experimental method to characterize the non-linear mechanical behaviour of human skin. Skin Research and Technology, 9(3), 274-283. doi:10.1034/j.1600-0846.2003.00019.