MULTIDISCIPLINARY TECHNIQUES FOR THE SIMULATION OF THE CONTACT BETWEEN THE FOOT AND THE SHOE UPPER IN GAIT: VIRTUAL REALITY, COMPUTATIONAL BIOMECHANICS, AND ARTIFICIAL NEURAL NETWORKS

Esta Tesis propone el uso de técnicas multidisciplinares como una alternativa viable a los procedimientos actuales de evaluación del calzado los cuales, normalmente, consumen muchos recursos humanos y técnicos. Estas técnicas son Realidad Virtual, Biomecánica Computacional y Redes Neuronales Artificiales. El marco de esta tesis es el análisis virtual del confort mecánico en el calzado, es decir, el análisis de las presiones de confort en el calzado y su principal objetivo es predecir las presiones ejercidas por el zapato sobre la superficie del pie al caminar mediante la simulación del contacto en esta interfaz. En particular, en esta tesis se ha desarrollado una aplicación software que usa el Método de los Elementos Finitos para simular la deformación del calzado. Se ha desarrollado un modelo preliminar que describe el comportamiento del corte del calzado, se ha implementado un proceso automático para el ajuste pie-zapato y se ha presentado una metodología para obtener una animación genérica del paso de cada individuo. Además, y con el fin de mejorar la aplicación desarrollada, se han propuesto nuevos modelos para simular el comportamiento del corte del calzado al caminar. Por otro lado, las Redes Neuronales Artificiales han sido aplicadas en esta tesis a la predicción de la fuerza ejercida por una esfera, que simulando un hueso, empuja a una muestra de material. Además, también han sido utilizadas para predecir las presiones ejercidas por el corte del calzado sobre la superficie del pie (presiones dorsales) en un paso completo. Las principales contribuciones de esta tesis son: el desarrollo de un innovador simulador que permitirá a los fabricantes de calzado realizar evaluaciones virtuales de las características de sus diseños sin tener que construir el prototipo real, y el desarrollo de una también innovadora herramienta que les permitirá predecir las presiones dorsales ejercidas por el calzado sobre la superficie del pie al caminar.Rupérez Moreno, MJ. (2011). MULTIDISCIPLINARY TECHNIQUES FOR THE SIMULATION OF THE CONTACT BETWEEN THE FOOT AND THE SHOE UPPER IN GAIT: VIRTUAL REALITY, COMPUTATIONAL BIOMECHANICS, AND ARTIFICIAL NEURAL NETWORKS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11235Palanci

Automatic supervision of gestures to guide novice surgeons during training

The final publication is available at Springer via http://dx.doi.org/10.1007/s00464-013-3285-9Background Virtual surgery simulators enable surgeons to learn by themselves, shortening their learning curves. Virtual simulators offer an objective evaluation of the surgeon’s skills at the end of each training session. The considered evaluation parameters are based on the analysis of the surgeon’s gestures performed throughout the training session. Currently, this information is usually known by surgeons only at the end of the training session, but very limited during the training performance. In this paper, we present a novel method for automatic and interactive evaluation of the surgeon’s skills that is able to supervise inexperienced surgeons during their training session with surgical simulators. Methods The method is based on the assumption that the sequence of gestures carried out by an expert surgeon in the simulator can be translated into a sequence (a character string) that should be reproduced by a novice surgeon during a training session. In this work, a string-matching algorithm has been modified to calculate the alignment and distance between the sequences of both expert and novice during the training performance. Results The results have shown that it is possible to distinguish between different skill levels at all times during the surgical training session. Conclusions The main contribution of this paper is a method where the difference between an expert’s sequence of gestures and a novice’s ongoing sequence is used to guide inexperienced surgeons. This is possible by indicating to novices the gesture corrections to be applied during surgical training as continuous expert supervision would do.Monserrat, C.; Lucas, A.; Hernández Orallo, J.; Rupérez Moreno, MJ. (2014). Automatic supervision of gestures to guide novice surgeons during training. Surgical Endoscopy. 28(4):1360-1370. doi:10.1007/s00464-013-3285-9S13601370284Ericsson KA (ed) (2009) Development of professional expertise: toward measurement of expert performance and design of optimal learning environments. Cambridge University Press, New YorkMcGaghie WC (2008) Research opportunities in simulation-based medical education using deliberate practice. Acad Emerg Med 15:995–1001Ericsson KA (2008) Deliberate practice and acquisition of expert performance: a general overview. Acad Emerg Med 15:988–994Issenberg SB, McGaghie WC, Petrusa ER et al (2005) Features and uses of high-fidelity medical simulations that lead to effective learning: a BEME systematic review. Med Teach 27:10–28Porte MC, Xeoulis G, Reznick RK, Dubrowski A (2007) Verbal feedback from an expert is more effective than self-accessed feedback about motion efficiency in learning new surgical skills. Am J Surg 193:105–110. doi: 10.1016/j.amjsurg.2006.03.016Hall PAV, Dowling GR (1980) Approximate string matching. ACM computing surveys (CSUR) 18(2):381–402. doi: 10.1145/356827.356830Stylopoulos N, Cotin S, Maithel SK et al (2004) Computer-enhanced laparoscopic training system (CELTS): bridging the gap. Surg Endosc 18(5):782–789. doi: 10.3233/978-1-60750-938-7-336Solis J, Oshima N, Ishii H, Matsuoka N et al (2009) Quantitative assessment of the surgical training methods with the suture/ligature training system WKS-2RII. In: IEEE international conference on robotics and automation, 2009 (ICRA ‘09), Kobe, pp 4219–4224. doi: 10.1109/ROBOT.2009.5152314Lin Z et al (2010) Objective evaluation of laparoscopic surgical skills using Waseda bioinstrumentation system WB-3. In: IEEE international conference on robotics and biomimetics (ROBIO), Tianjin, pp 247–252. doi: 10.1109/ROBIO.2010.5723335Chmarra MK, Klein S, Winter JCF, Jansen FW, Dankelman J (2010) Objective classification of residents based on their psychomotor laparoscopic skills. Surg Endosc 24(5):1031–1039. doi: 10.1007/s00464-009-0721-yLin HC, Shafran I, Yuh D, Hager GD (2006) Towards automatic skill evaluation: detection and segmentation of robot-assisted surgical motions. Comput Aided Surg 11(5):220–230. doi: 10.3109/10929080600989189Rosen J, Brown JD, Chang L, Sinanan MN, Hannaford B (2006) Generalized approach for modeling minimally invasive surgery as a stochastic process using a discrete Markov model. IEEE Trans Biomed Eng 53(3):399–413. doi: 10.1109/TBME.2005.869771Lahanas V, Loukas C, Nikiteas N, Dimitroulis D, Georgiou E (2011) Psychomotor skills assessment in laparoscopic surgery using augmented reality scenarios. 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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. Journal of Bone and Mineral Research, 24(3), 475-483. doi:10.1359/jbmr.081201Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Choi, 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., 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/s00198-011-1569-2BowyerK. W.ChawlaN. V.HallL. O.KegelmeyerW. P.SMOTE: synthetic minority over-sampling techniqueCoRRhttps://arxiv.org/abs/1106.181

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

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). 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Assessment of the use of technical software by the students in the context of mechanical engineering

[EN] In the framework of the European Higher Education area, university teaching has focused in recent years on adapting Master's and Bachelor's degrees to the demands of the professional sector. To do this, the training and development of the generic and specific skills recommended for the incorporation of students into the job market have been priority objectives in the approach to study plans. However, there is no consensus on the methodologies for evaluating these skills, especially regarding how to separate the acquisition and / or improvement of the skills from the specific knowledge and skills of the subjects. Due to the lack of time, teaching staff seek methodologies that do not involve additional tests for the evaluation of competences, which would increase the number of tests to a non-realistic number with the corresponding assessment duties for the professors. In order to make a contribution in this regard, this work presents an approach for evaluating the ability to handle specific software applied to problems in the area of mechanical engineering. This work proposes a methodology for acquiring the required skills and an evaluation system to grade the degree of expertise in the manipulation of the software. In our University, this skill is called the Specific Instrumental Skill, which measures the ability of the students for using the tools in engineering, like, in this case, the use of software to run structural numerical simulations as ANSYS®. The methodology proposed is based on an a priori training. This training is based on 2 hours weekly sessions where the students should solve, in groups of 2 or 3 students, a set of labs with the help of the professor. The students do not need to deliver any report to the professor since the objective of the sessions is the training of the students. Therefore, the pressure over the student is low and the professor avoid to mark a high number of student¿s reports, allowing him to focus only on the learning process of the students and not on the evaluation during the training sessions. These labs increase the difficulty along a number of sessions. The last session consists in an exam in which the students must solve a lab similar to those already solved during the training sessions. This time, each student will work individually without the help of the professor and with a control of the time. Finally, the performance of the methodology is checked by a cross-test for the same students who are part of the group of students of another subject (control subject) where the same tool (ANSYS®) is used. The collected data showed that the students following this methodology acquire the sufficient expertise for handling the software and their skills outperform those of the students of the control subject who did not follow the proposed methodology. As a conclusion, the methodology proposed in this work guarantees a good level of expertise for the students, as shown by the results. Since the results in the final lab exam and the results of the cross-test coincides, the use of the final test exam could be interpreted as a good indicator of the degree of expertise in the use of the software. Additionally, the proposed methodology reduces the work load for the professor as it only requires assessing 1 report per student (instead of several reports for each group of 2 or 3 students in each of the session) while ensuring the authorship of the report.Authors gratefully acknowledge the financial support of the Vicerrectorado de Estudios, Calidad y Acreditación and the Vicerrectorado de Recursos Digitales y Documentación of the Universitat Politècnica de València (project PIME B/19-20/165) and the Instituto de Ciencias de la Educación of the Universitat Politècnica de València (EICE INTEGRAL).Nadal, E.; Rupérez Moreno, MJ.; Giner Navarro, J.; Rovira, A.; Ródenas, JJ.; Martínez Casas, J.; Pedrosa, AM. (2020). Assessment of the use of technical software by the students in the context of mechanical engineering. IATED Academy. 3344-3348. https://doi.org/10.21125/iceri.2020.0756S3344334

Use of ict tools in hybrid environments of presential and virtual learning. Experience in two mechanical engineering subjects of master's degree

[Otros] In March 2020, the World Health Organization (WHO) raised to an international pandemic level the outbreak of the coronavirus (COVID-19) disease caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2). Consequently, the Spanish government declared the State of Alarm on March 14th, 2020. From that moment on, all non-essential activities were tightened lockdown. This measure affected all levels of education, and face-to-face learning was suspended until the end of the academic year. While the last year all the educational community was forced to suddenly move to online platforms, this year has been rather marked by the need of flexibility in the learning model. The main reasons are the constant threat of global lockdowns, the changing local mobility restriction measures and the forced isolation or quarantine in case of infections or close contact with someone with a positive Polymerase Chain Reaction (PCR) test. This situation has drastically affected the face-to-face teaching model. At the same time, this uncertain scenario has undoubtedly been an opportunity to implement Information and Communications Technology (ICT)activities in our teaching. This paper presents some actions implemented in two subjects of mechanical design of two different master¿s degrees with 36 and 19 students, respectively. The developed resources concern to the learning materials provided to the students, both for theoretical and practical lessons. The audio-visual materials generated over the classes, both videos and annotations on the virtual whiteboard, has also been managed. Finally, the delivery and defence of the students' works have been adapted. As it was foreseeable all, some of the students or the teacher, have followed the classes online over the term. In all cases, the developed materials and the methodology followed have been valid for use in face-to-face teaching, online and blended learning, thus providing the flexibility required. The level of success has been measured, mainly through a survey answered by the students. In addition, it has been analysed objective data, such as the marks and the number of visits or downloads of the documents uploaded to the UPV e-learning platform. Finally, it is concluded which actions taken would be valid for the post-COVID era.Authors gratefully acknowledge the financial support of the Vicerrectorado de Estudios, Calidad y Acreditación and the Vicerrectorado de Recursos Digitales y Documentación of the Universitat Politècnica de València (project PIME B/19-20/165 and project PIME C/20-21/201) and the Instituto de Ciencias de la Educación of the Universitat Politècnica de València (EICE INTEGRAL).Pedrosa, AM.; Vila Tortosa, MP.; Llopis-Albert, C.; Rupérez Moreno, MJ. (2021). Use of ict tools in hybrid environments of presential and virtual learning. Experience in two mechanical engineering subjects of master's degree. IATED Academy. 5077-5085. https://doi.org/10.21125/edulearn.2021.1048S5077508

Experiences in the development of didactic videos in remote and hybrid teaching: the case of Materials Science laboratory practices

[EN] The COVID-19 pandemics greatly affected teaching methods and tools. In the last two years the teaching method switched from face-to-face to online or hybrid and later on, to face-to-face again. This paper presents the results of the experience carried out in the lab-sessions of Materials Science subject within the Degree in Engineering in Industrial Design and Product Development, taught at the Higher Technical School of Design Engineering of the Universitat politècnica de València. The aim of this work is to develop an audio-visual teaching tool to be used during the course, to aid students understand the experimental procedures and the use of laboratory equipment in order to increase their academic performance. For this purpose, the obligatory didactic videos employed to undertake the laboratory session during the pandemics, when the students participated remotely, were processed and employed in a shorter version as an optional TIC tool during the last academic course. The student satisfaction towards the utility of didactic videos was measured by using an individual questionnaire. A comparison between the academic performance during pre- and post-pandemics was performed, as well.[ES] La pandemia de COVID-19 afectó en gran medida a los métodos y herramientas de aprendizaje. En los dos últimos años se pasó de la modalidad de enseñanza presencial a online o híbrida y, posteriormente, nuevamente a la presencial. Este artículo presenta los resultados de la experiencia realizada en las prácticas de la asignatura Ciencia de los Materiales del Grado en Ingeniería en Diseño Industrial y Desarrollo de Productos, impartida en la Escuela Técnica Superior de Ingeniería del Diseño de la Universitat Politècnica de València. El objetivo de este trabajo es desarrollar una herramienta didáctica audiovisual a utilizar durante el curso, para ayudar a los estudiantes a comprender los procedimientos experimentales y el uso de equipos de laboratorio con el objetivo de incrementar su rendimiento académico. Para ello se procesaron los videos didácticos desarrollados para realizar la sesión de laboratorio durante la pandemia, cuando los estudiantes participaban de forma no presencial, y se emplearon en una versión más corta como herramienta TIC opcional durante el último curso académico. La satisfacción de los estudiantes con la utilidad de los videos didácticos se midió mediante un cuestionario individual. También se realizó una comparación entre el rendimiento académico durante la pre-pandemia y post-pandemia.Pruna, AI.; Klyatskina Rusinovich, E.; Vicente Escuder, A.; Rupérez Moreno, MJ. (2022). Experiencias en el desarrollo de videos didácticos en las enseñanzas remota e híbrida: el caso de las prácticas de laboratorio de Ciencia de los Materiales. Editorial Universitat Politècnica de València. 1136-1143. https://doi.org/10.4995/INRED2022.2022.159061136114

Estimating the Relative Stiffness between a Hepatic Lesion and the Liver Parenchyma through Biomechanical Simulations of the Breathing Process

[EN] In this paper, a method to in vivo estimate the relative stifness between a hepatic lesion and the liver parenchyma is presented. Tis method is based on the fnite element simulation of the deformation that the liver undergoes during the breathing process. Boundary conditions are obtained through a registration algorithm known as Coherent Point Drif (CPD), which compares the liver form in two phases of the breathing process. Finally, the relative stifness of the tumour with respect to the liver parenchyma is calculated by means of a Genetic Algorithm, which does a blind search of this parameter. Te relative stifness together with the clinical information of the patient can be used to establish the type of hepatic lesion. Te developed methodology was frst applied to a test case, i.e., to a control case where the parameters were known, in order to verify its validity. Afer that, the method was applied to two real cases and low errors were obtained.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects DPI2013-40859-R and TIN2014-52033-R, both also supported by European FEDER funds.Martinez-Sanchis, S.; Rupérez Moreno, MJ.; Nadal, E.; Pareja, E.; Brugger, S.; Borzacchiello, D.; López, R.... (2018). Estimating the Relative Stiffness between a Hepatic Lesion and the Liver Parenchyma through Biomechanical Simulations of the Breathing Process. Mathematical Problems in Engineering. 1-10. https://doi.org/10.1155/2018/5317324S110Kmieć, Z. (2001). Introduction — Morphology of the Liver Lobule. Advances in Anatomy Embryology and Cell Biology, 1-6. doi:10.1007/978-3-642-56553-3_1Cequera, A., & García de León Méndez, M. C. (2014). Biomarkers for liver fibrosis: Advances, advantages and disadvantages. 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Tensional homeostasis and the malignant phenotype. Cancer Cell, 8(3), 241-254. doi:10.1016/j.ccr.2005.08.010Kuo, Y.-H., Lu, S.-N., Hung, C.-H., Kee, K.-M., Chen, C.-H., Hu, T.-H., … Wang, J.-H. (2010). Liver stiffness measurement in the risk assessment of hepatocellular carcinoma for patients with chronic hepatitis. Hepatology International, 4(4), 700-706. doi:10.1007/s12072-010-9223-1Heide, R., Strobel, D., Bernatik, T., & Goertz, R. (2010). Characterization of Focal Liver Lesions (FLL) with Acoustic Radiation Force Impulse (ARFI) Elastometry. Ultraschall in der Medizin - European Journal of Ultrasound, 31(04), 405-409. doi:10.1055/s-0029-1245565Frulio, N., Laumonier, H., Carteret, T., Laurent, C., Maire, F., Balabaud, C., … Trillaud, H. (2013). Evaluation of Liver Tumors Using Acoustic Radiation Force Impulse Elastography and Correlation With Histologic Data. Journal of Ultrasound in Medicine, 32(1), 121-130. doi:10.7863/jum.2013.32.1.121Ma, X., Zhan, W., Zhang, B., Wei, B., Wu, X., Zhou, M., … Li, P. (2014). Elastography for the differentiation of benign and malignant liver lesions: a meta-analysis. Tumor Biology, 35(5), 4489-4497. doi:10.1007/s13277-013-1591-4Guo, L.-H., Wang, S.-J., Xu, H.-X., Sun, L.-P., Zhang, Y.-F., Xu, J.-M., … Xu, X.-H. (2015). Differentiation of benign and malignant focal liver lesions: value of virtual touch tissue quantification of acoustic radiation force impulse elastography. Medical Oncology, 32(3). doi:10.1007/s12032-015-0543-9Dietrich, C., Bamber, J., Berzigotti, A., Bota, S., Cantisani, V., Castera, L., … Thiele, M. (2017). EFSUMB Guidelines and Recommendations on the Clinical Use of Liver Ultrasound Elastography, Update 2017 (Long Version). Ultraschall in der Medizin - European Journal of Ultrasound, 38(04), e16-e47. doi:10.1055/s-0043-103952Ferraioli, G., Filice, C., Castera, L., Choi, B. I., Sporea, I., Wilson, S. R., … Kudo, M. (2015). WFUMB Guidelines and Recommendations for Clinical Use of Ultrasound Elastography: Part 3: Liver. Ultrasound in Medicine & Biology, 41(5), 1161-1179. doi:10.1016/j.ultrasmedbio.2015.03.007Sigrist, R. M. S., Liau, J., Kaffas, A. E., Chammas, M. C., & Willmann, J. K. (2017). Ultrasound Elastography: Review of Techniques and Clinical Applications. Theranostics, 7(5), 1303-1329. doi:10.7150/thno.18650Cosgrove, D., Piscaglia, F., Bamber, J., Bojunga, J., Correas, J.-M., Gilja, O., … Dietrich, C. (2013). EFSUMB Guidelines and Recommendations on the Clinical Use of Ultrasound Elastography.Part 2: Clinical Applications. Ultraschall in der Medizin - European Journal of Ultrasound, 34(03), 238-253. doi:10.1055/s-0033-1335375Palmeri, M. L., & Nightingale, K. R. (2011). What challenges must be overcome before ultrasound elasticity imaging is ready for the clinic? Imaging in Medicine, 3(4), 433-444. doi:10.2217/iim.11.41Samir, A. E., Dhyani, M., Vij, A., Bhan, A. K., Halpern, E. F., Méndez-Navarro, J., … Chung, R. T. (2015). Shear-Wave Elastography for the Estimation of Liver Fibrosis in Chronic Liver Disease: Determining Accuracy and Ideal Site for Measurement. Radiology, 274(3), 888-896. doi:10.1148/radiol.14140839Toshima, T., Shirabe, K., Takeishi, K., Motomura, T., Mano, Y., Uchiyama, H., … Maehara, Y. (2011). New method for assessing liver fibrosis based on acoustic radiation force impulse: a special reference to the difference between right and left liver. Journal of Gastroenterology, 46(5), 705-711. doi:10.1007/s00535-010-0365-7Barr, R. G., Ferraioli, G., Palmeri, M. L., Goodman, Z. D., Garcia-Tsao, G., Rubin, J., … Levine, D. (2015). Elastography Assessment of Liver Fibrosis: Society of Radiologists in Ultrasound Consensus Conference Statement. Radiology, 276(3), 845-861. doi:10.1148/radiol.2015150619Venkatesh, S. K., Yin, M., & Ehman, R. L. (2013). 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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