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Developing a knowledge base to support the annotation of ultrasound images of ectopic pregnancy

Overview of attention for article published in Journal of Biomedical Semantics, January 2017
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Title
Developing a knowledge base to support the annotation of ultrasound images of ectopic pregnancy
Published in
Journal of Biomedical Semantics, January 2017
DOI 10.1186/s13326-017-0117-1
Pubmed ID
Authors

Ferdinand Dhombres, Paul Maurice, Stéphanie Friszer, Lucie Guilbaud, Nathalie Lelong, Babak Khoshnood, Jean Charlet, Nicolas Perrot, Eric Jauniaux, Davor Jurkovic, Jean-Marie Jouannic

Abstract

Ectopic pregnancy is a frequent early complication of pregnancy associated with significant rates of morbidly and mortality. The positive diagnosis of this condition is established through transvaginal ultrasound scanning. The timing of diagnosis depends on the operator expertise in identifying the signs of ectopic pregnancy, which varies dramatically among medical staff with heterogeneous training. Developing decision support systems in this context is expected to improve the identification of these signs and subsequently improve the quality of care. In this article, we present a new knowledge base for ectopic pregnancy, and we demonstrate its use on the annotation of clinical images. The knowledge base is supported by an application ontology, which provides the taxonomy, the vocabulary and definitions for 24 types and 81 signs of ectopic pregnancy, 484 anatomical structures and 32 technical elements for image acquisition. The knowledge base provides a sign-centric model of the domain, with the relations of signs to ectopic pregnancy types, anatomical structures and the technical elements. The evaluation of the ontology and knowledge base demonstrated a positive feedback from a panel of 17 medical users. Leveraging these semantic resources, we developed an application for the annotation of ultrasound images. Using this application, 6 operators achieved a precision of 0.83 for the identification of signs in 208 ultrasound images corresponding to 35 clinical cases of ectopic pregnancy. We developed a new ectopic pregnancy knowledge base for the annotation of ultrasound images. The use of this knowledge base for the annotation of ultrasound images of ectopic pregnancy showed promising results from the perspective of clinical decision support system development. Other gynecological disorders and fetal anomalies may benefit from our approach.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 47 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 21%
Student > Ph. D. Student 9 19%
Student > Bachelor 5 11%
Researcher 4 9%
Student > Doctoral Student 3 6%
Other 5 11%
Unknown 11 23%
Readers by discipline Count As %
Medicine and Dentistry 16 34%
Computer Science 5 11%
Engineering 4 9%
Nursing and Health Professions 2 4%
Mathematics 1 2%
Other 4 9%
Unknown 15 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 24 February 2017.
All research outputs
#17,876,644
of 22,953,506 outputs
Outputs from Journal of Biomedical Semantics
#288
of 364 outputs
Outputs of similar age
#293,513
of 420,234 outputs
Outputs of similar age from Journal of Biomedical Semantics
#11
of 13 outputs
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So far Altmetric has tracked 364 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 18th percentile – i.e., 18% of its peers scored the same or lower than it.
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We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.