Browsing by Author "Espinoza, Ignacio"

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    Claim Behavior over Time in Twitter
    (SPRINGER INTERNATIONAL PUBLISHING AG, 2019) Weiss, Fernanda; Espinoza, Ignacio; Hurtado Gonzalez Julio Andres; Mendoza Rocha Marcelo Gabriel; Meiselwitz, G
    Social media is the primary source of information for many people around the world, not only to know about their families and friends but also to read about news and trends in different areas of interest. Fake News or rumors can generate big problems of misinformation, being able to change the mindset of a large group of people concerning a specific topic. Many companies and researchers have put their efforts into detecting these rumors with machine learning algorithms creating reports of the influence of these "news" in social media (https://www.knightfoundation.org/reports/disinformation-fake-news-and-influence-campaigns-on-twitter). Only a few studies have been made in detecting rumors in real-time, considering the first hours of propagation. In this work, we study the spread of a claim, analyzing different characteristics and how propagation patterns behave in time. Experiments show that rumors have different behaviours that can be used to classify them within the first hours of propagation.
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    IS2aR, a computational tool to transform voxelized reference phantoms into patient-specific whole-body virtual CTs for peripheral dose estimation
    (2023) Munoz-Hernandez, Isidora Sofia; Espinoza, Ignacio; Lopez-Martinez, Ignacio N.; Sanchez-Nieto, Beatriz
    Background: The risk of radiogenic cancer induction due to radiotherapy depends on the dose received by the patient's organs. Knowing the position of all organs is needed to assess this dose in a personalized way. However, radiotherapy planning computed tomography (pCT) scans contain truncated patient anatomy, limiting personalized dose evaluation. Purpose: To develop a simple and freely available computational tool that adapts an ICRP reference computational phantom to generate a patient-specific whole-body CT for peripheral dose computations. Methods: Various bone-segmentation methods were explored onto fifteen pCTs, and the one with the highest Sorensen-Dice coefficient was implemented. The reference phantom is registered to the pCT, obtaining a registration transform matrix, which is then applied to create the whole-body virtual CT. Additional validation involved a comparison of absorbed doses to organs delineated on both the pCT and the virtual CT. Results: A dedicated graphical user interface was designed and implemented to house the developed functions for i) selecting a registration region on which automatic bone segmentation and rigid registration will occur, ii) displaying the results of these processes under selectable views, and iii) exporting the final patient-specific whole-body CT. This software was termed IS2aR. The tested whole-body virtual CT generated by IS2aR fulfilled our requirements. Conclusions: IS2aR is a user-friendly computational software to create a personalized whole-body CT containing the original structures in the reference phantom. The personalized dose deposited in peripheral organs can be estimated further to assess second cancer induction risk in epidemiological studies.