Cited by 33Artificial Intelligence in Healthcare and EducationBiomedical Text Mining and OntologiesArtificial Intelligence in Healthcare

Towards ontology-based decision support systems for complex ultrasound diagnosis in obstetrics and gynecology

Maurice, P., Dhombres, F., Blondiaux, E., Friszer, S., Guilbaud, L., Lelong, N., Khoshnood, B., Charlet, J. +4 more·2017·Journal of Gynecology Obstetrics and Human Reproduction

Abstract

Introduction We have developed a new knowledge base intelligent system for obstetrics and gynecology ultrasound imaging, based on an ontology and a reference image collection. This study evaluates the new system to support accurate annotations of ultrasound images. We have used the early ultrasound diagnosis of ectopic pregnancies as a model clinical issue.Material and methods The ectopic pregnancy ontology was derived from medical texts (4260 ultrasound reports of ectopic pregnancy from a specialist center in the UK and 2795 Pubmed abstracts indexed with the MeSH term "Pregnancy, Ectopic") and the reference image collection was built on a selection from 106 publications. We conducted a retrospective analysis of the signs in 35 scans of ectopic pregnancy by six observers using the new system.Results The resulting ectopic pregnancy ontology consisted of 1395 terms, and 80 images were collected for the reference collection. The observers used the knowledge base intelligent system to provide a total of 1486 sign annotations. The precision, recall and F-measure for the annotations were 0.83, 0.62 and 0.71, respectively. The global proportion of agreement was 40.35% 95% CI [38.64-42.05].Discussion The ontology-based intelligent system provides accurate annotations of ultrasound images and suggests that it may benefit non-expert operators. The precision rate is appropriate for accurate input of a computer-based clinical decision support and could be used to support medical imaging diagnosis of complex conditions in obstetrics and gynecology.

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Crossref · 10.1016/j.jogoh.2017.03.004

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Authors
Maurice, P., Dhombres, F., Blondiaux, E., Friszer, S., Guilbaud, L., Lelong, N., Khoshnood, B., Charlet, J., Perrot, N., Jauniaux, E., Jurkovic, D., & Jouannic, J.-M.
Affiliations
  • Inserm (FR)
  • University College Hospital (GB)
Corresponding author
Dhombres, F.
Venue
Journal of Gynecology Obstetrics and Human Reproduction
Publisher
Elsevier BV
Published
2017-05
Volume
46
Issue
5
Pages
423-429
ISSN
2468-7847 (ISSN-L)
OpenAlex
W2601604902
Cited by
33
References
32
Citations by year
2026: 2 · 2025: 2 · 2024: 4 · 2023: 4 · 2022: 4 · 2021: 3 · 2020: 4 · 2019: 9 · 2018: 1
FWCI
2.24
Type
journal article
Language
en
Open access
closed
Locations
  • Journal of Gynecology Obstetrics and Human Reproduction (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
ectopic pregnancy, obstetric and gynecologic ultrasound, computer-aided diagnosis
MeSH terms
Diagnosis, Differential, Expert Systems, Female, Gynecology, Humans, Image Interpretation, Computer-Assisted, Obstetrics, Pregnancy, Pregnancy, Ectopic, Ultrasonography, Ultrasonography, Prenatal, Decision Support Systems, Clinical, Internet, Knowledge Bases, Biological Ontologies

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