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tests to generate the XML records for the index for the EDITH demo on digital twins, integrating output from the FoS classifier

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Alessia Bardi 2023-04-21 16:46:30 +02:00
parent 24c41806ac
commit 382f46a8e4
4 changed files with 1125 additions and 0 deletions
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17
dhp-workflows/dhp-graph-provision/src/test/java/eu/dnetlib/dhp/oa/provision/IndexRecordTransformerTest.java
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@ -128,6 +128,23 @@ public class IndexRecordTransformerTest {
testRecordTransformation(record);
}
@Test
public void testForEdithDemo() throws IOException, TransformerException {
final String record = IOUtils.toString(getClass().getResourceAsStream("edith-demo/10.1098-rsta.2020.0257.xml"));
testRecordTransformation(record);
}
@Test
public void testForEdithDemoCovid() throws IOException, TransformerException {
final String record = IOUtils.toString(getClass().getResourceAsStream("edith-demo/10.3390-pr9111967-covid.xml"));
testRecordTransformation(record);
}
@Test
public void testForEdithDemoEthics() throws IOException, TransformerException {
final String record = IOUtils.toString(getClass().getResourceAsStream("edith-demo/10.2196-33081-ethics.xml"));
testRecordTransformation(record);
}
@Test
void testDoiUrlNormalization() throws MalformedURLException {

596
dhp-workflows/dhp-graph-provision/src/test/resources/eu/dnetlib/dhp/oa/provision/edith-demo/10.1098-rsta.2020.0257.xml Normal file
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@ -0,0 +1,596 @@
<record>
<result xmlns:dri="http://www.driver-repository.eu/namespace/dri"
xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<header>
<dri:objIdentifier>doi_dedup___::e225555a08a082ad8f53f179bc59c5d0</dri:objIdentifier>
<dri:dateOfCollection>2023-01-27T05:32:10Z</dri:dateOfCollection>
</header>
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human heart from 2D cine magnetic resonance slices.
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schemename="dnet:dataCite_title">A completely automated pipeline for 3D reconstruction of
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<description>Cardiac magnetic resonance (CMR) imaging is a valuable modality in the diagnosis and
characterization of cardiovascular diseases, since it can identify abnormalities in structure
and function of the myocardium non-invasively and without the need for ionizing radiation.
However, in clinical practice, it is commonly acquired as a collection of separated and
independent 2D image planes, which limits its accuracy in 3D analysis. This paper presents a
completely automated pipeline for generating patient-specific 3D biventricular heart models from
cine magnetic resonance (MR) slices. Our pipeline automatically selects the relevant cine MR
images, segments them using a deep learning-based method to extract the heart contours, and
aligns the contours in 3D space correcting possible misalignments due to breathing or subject
motion first using the intensity and contours information from the cine data and next with the
help of a statistical shape model. Finally, the sparse 3D representation of the contours is used
to generate a smooth 3D biventricular mesh. The computational pipeline is applied and evaluated
in a CMR dataset of 20 healthy subjects. Our results show an average reduction of misalignment
artefacts from 1.82 ± 1.60 mm to 0.72 ± 0.73 mm over 20 subjects, in terms of distance from the
final reconstructed mesh. The high-resolution 3D biventricular meshes obtained with our
computational pipeline are used for simulations of electrical activation patterns, showing
agreement with non-invasive electrocardiographic imaging. The automatic methodologies presented
here for patient-specific MR imaging-based 3D biventricular representations contribute to the
efficient realization of precision medicine, enabling the enhanced interpretability of clinical
data, the digital twin vision through patient-specific image-based modelling and simulation, and
augmented reality applications.
This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new
challenges and opportunities’.
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schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Cine mri
</subject>
<subject classid="MAG" classname="Microsoft Academic Graph classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Structure and function
</subject>
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schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Cardiac magnetic resonance
</subject>
<subject classid="MAG" classname="Microsoft Academic Graph classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Magnetic resonance imaging
</subject>
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provenanceaction="sysimport:actionset" trust="0.5338496">medicine.diagnostic_test
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schemename="dnet:subject_classification_typologies" inferred="false"
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</subject>
<subject classid="MAG" classname="Microsoft Academic Graph classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Human heart
</subject>
<subject classid="MAG" classname="Microsoft Academic Graph classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Modality (human–computer interaction)
</subject>
<subject classid="MAG" classname="Microsoft Academic Graph classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">3D reconstruction
</subject>
<subject classid="MAG" classname="Microsoft Academic Graph classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Computer science
</subject>
<subject classid="MAG" classname="Microsoft Academic Graph classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies">Nuclear magnetic resonance
</subject>
<subject classid="SDG" classname="Sustainable Development Goals"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:sdg">3. Good health
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">03 medical and health sciences
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">0302 clinical medicine
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">030218 Nuclear Medicine &amp;
Medical Imaging
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">03021801 Radiology/Image
segmentation
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">medical ---- image ----
learning ---- deep ---- medical image ---- deep learning ---- learn ---- datum ---- imaging ----
ai
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">deep learning
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">datum
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<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">030204 Cardiovascular System
&amp; Hematology
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">03020401 Aging-associated
diseases/Heart diseases
</subject>
<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">030217 Neurology &amp;
Neurosurgery
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<subject classid="FOS" classname="Fields of Science and Technology classification"
schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="true"
inferenceprovenance="update" provenanceaction="subject:fos">03021701 Brain/Neural circuits
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provenanceaction="sysimport:crosswalk:repository" trust="0.9">Articles
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provenanceaction="sysimport:crosswalk:repository" trust="0.9">Research Articles
</subject>
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schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">cardiac mesh reconstruction
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<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">cine MRI
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schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">misalignment correction
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provenanceaction="sysimport:crosswalk:repository" trust="0.9">electrophysiological
simulation
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schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:crosswalk:repository" trust="0.9">ECGI
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<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9">Heart
</subject>
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9">Humans
</subject>
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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provenanceaction="sysimport:actionset" trust="0.9">Imaging, Three-Dimensional
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<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Imaging
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<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
schemename="dnet:subject_classification_typologies" inferred="false"
provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Imaging, Cine
</subject>
<subject classid="keyword" classname="keyword" schemeid="dnet:subject_classification_typologies"
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provenanceaction="sysimport:actionset" trust="0.9">Magnetic Resonance Spectroscopy
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<publisher>The Royal Society</publisher>
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<description>Background: The concept of digital twins has great potential for transforming the existing health care system by making it more personalized. As a convergence of health care, artificial intelligence, and information and communication technologies, personalized health care services that are developed under the concept of digital twins raise a myriad of ethical issues. Although some of the ethical issues are known to researchers working on digital health and personalized medicine, currently, there is no comprehensive review that maps the major ethical risks of digital twins for personalized health care services. Objective This study aims to fill the research gap by identifying the major ethical risks of digital twins for personalized health care services. We first propose a working definition for digital twins for personalized health care services to facilitate future discussions on the ethical issues related to these emerging digital health services. We then develop a process-oriented ethical map to identify the major ethical risks in each of the different data processing phases. MethodsWe resorted to the literature on eHealth, personalized medicine, precision medicine, and information engineering to identify potential issues and developed a process-oriented ethical map to structure the inquiry in a more systematic way. The ethical map allows us to see how each of the major ethical concerns emerges during the process of transforming raw data into valuable information. Developers of a digital twin for personalized health care service may use this map to identify ethical risks during the development stage in a more systematic way and can proactively address them. ResultsThis paper provides a working definition of digital twins for personalized health care services by identifying 3 features that distinguish the new application from other eHealth services. On the basis of the working definition, this paper further layouts 10 major operational problems and the corresponding ethical risks. ConclusionsIt is challenging to address all the major ethical risks that a digital twin for a personalized health care service might encounter proactively without a conceptual map at hand. The process-oriented ethical map we propose here can assist the developers of digital twins for personalized health care services in analyzing ethical risks in a more systematic manner. </description>
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inferenceprovenance="update" provenanceaction="subject:fos">ai ---- moral ---- technology ---- human ---- artificial ---- responsibility ---- value ---- intelligence ---- ethical ---- artificial intelligence
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quantitative validation workflow. Statistically significant parameters are identified as part of
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with a deviation of less than 1%. The advantages of continuous production are pointed out
compared to batchwise in vitro transcription by optimization of the space–time yield.
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