A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning

Progress in biomechanical modelling of human soft tissue is the basis for the development of new clinical applications capable of improving the diagnosis and treatment of some diseases (e.g. cancer), as well as the surgical planning and guidance of some interventions. The finite element method (FEM) is one of the most popular techniques used to predict the deformation of the human soft tissue due to its high accuracy. However, FEM has an associated high computational cost, which makes it difficult its integration in real-time computer-aided surgery systems. An alternative for simulating the mechanical behaviour of human organs in real time comes from the use of machine learning (ML) techniques, which are much faster than FEM. This paper assesses the feasibility of ML methods for modelling the biomechanical behaviour of the human liver during the breathing process, which is crucial for guiding surgeons during interventions where it is critical to track this deformation (e.g. some specific kind of biopsies) or for the accurate application of radiotherapy dose to liver tumours. For this purpose, different ML regression models were investigated, including three tree-based methods (decision trees, random forests and extremely randomised trees) and other two simpler regression techniques (dummy model and linear regression). In order to build and validate the ML models, a labelled data set was constructed from modelling the deformation of eight ex-vivo human livers using FEM. The best prediction performance was obtained using extremely randomised trees, with a mean error of 0.07 mm and all the samples with an error under 1 mm. The achieved results lay the foundation for the future development of some real-time software capable of simulating the human liver deformation during the breathing process during clinical interventions.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R and DPI2013-40859-R, both also supported by European FEDER funds. The authors acknowledge the kind collaboration of the personnel from the hospital involved in the research.Lorente, D.; Martínez-Martínez, F.; Rupérez Moreno, MJ.; Lago, MA.; Martínez-Sober, M.; Escandell-Montero, P.; Martínez-Martínez, JM.... (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications. 71:342-357. doi:10.1016/j.eswa.2016.11.037S3423577

A European research agenda for somatic symptom disorders, bodily distress disorders, and functional disorders: Results of an estimate-talk-estimate delphi expert study

Background: Somatic Symptom Disorders (SSD), Bodily Distress Disorders (BDD) and functional disorders (FD) are associated with high medical and societal costs and pose a substantial challenge to the population and health policy of Europe. To meet this challenge, a specific research agenda is needed as one of the cornerstones of sustainable mental health research and health policy for SSD, BDD, and FD in Europe. Aim: To identify the main challenges and research priorities concerning SSD, BDD, and FD from a European perspective. Methods: Delphi study conducted from July 2016 until October 2017 in 3 rounds with 3 workshop meetings and 3 online surveys, involving 75 experts and 21 European countries. EURONET-SOMA and the European Association of Psychosomatic Medicine (EAPM) hosted the meetings. Results: Eight research priorities were identified: (1) Assessment of diagnostic profiles relevant to course and treatment outcome. (2) Development and evaluation of new, effective interventions. (3) Validation studies on questionnaires or semi-structured interviews that assess chronic medical conditions in this context. (4) Research into patients preferences for diagnosis and treatment. (5) Development of new methodologic designs to identify and explore mediators and moderators of clinical course and treatment outcomes (6). Translational research exploring how psychological and somatic symptoms develop from somatic conditions and biological and behavioral pathogenic factors. (7) Development of new, effective interventions to personalize treatment. (8) Implementation studies of treatment interventions in different settings, such as primary care, occupational care, general hospital and specialty mental health settings. The general public and policymakers will benefit from the development of new, effective, personalized interventions for SSD, BDD, and FD, that will be enhanced by translational research, as well as from the outcomes of research into patient involvement, GP-patient communication, consultation-liaison models and implementation. Conclusion: Funding for this research agenda, targeting these challenges in coordinated research networks such as EURONET-SOMA and EAPM, and systematically allocating resources by policymakers to this critical area in mental and physical well-being is urgently needed to improve efficacy and impact for diagnosis and treatment of SSD, BDD, and FD across Europe

Three-dimensional seismic velocity tomography of Montserrat from the SEA-CALIPSO offshore/onshore experiment

The SEA-CALIPSO experiment in December 2007 incorporated a sea-based airgun source, and seismic recorders both on Montserrat and on the adjacent sea floor. A high quality subset of the data was used for a first arrival P-wave velocity tomographic study. A total of more than 115,000 traveltime data from 4413 airgun shots, and 58 recording stations, were used in this highresolution tomographic inversion. The experiment geometry limited the depth of well resolved structures to about 5 km. The most striking features of the tomography are three relatively high velocity zones below each of the main volcanic centers on Montserrat, and three low velocity zones flanking Centre Hills. We suggest that the high velocity zones represent the solid andesitic cores of the volcano complexes, characterized by wave speeds faster than adjacent volcaniclastic material. The low velocity zones may reflect porous volcaniclastic material and/or alteration by formerly active hydrothermal systems. Copyright © 2010 by the American Geophysical Union

Fatigue strength evaluation of a 13%Cr supermartensitic stainless steel by the thermographic method

The fatigue limits of a 13Cr super-martensitic stainless steel from a hot rolled seamless tube were determined for three heat treatment conditions, including the as received quenched and tempered (QT) condition, quenched (Q), and quenched and double tempered (Q-DT). These heat treatments were applied to produce different microstructures and different tensile mechanical properties. The microstructures were characterized by microscopy and magnetic methods. The fatigue strength was accessed through the utilization of the thermographic technique. The results show that fatigue limits measured were between 38% and 44% of the ultimate strength. The as quenched condition gave the higher fatigue limit (444 MPa). However, the material has the lower dutility at this condition. The results aid to decide the best heat treatment condition and give support to design in applications where dynamic loadings are expecte