Format

Send to

Choose Destination
J Biomed Semantics. 2017 Jan 31;8(1):4. doi: 10.1186/s13326-017-0117-1.

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

Author information

1
UPMC Medical Faculty (Paris 6), Department of Fetal Medicine in Armand Trousseau Hospital (APHP), INSERM U1142 (LIMICS), 26 Avenue du Dr Arnold Netter, 75012, Paris, UE, France. ferdinand.dhombres@aphp.fr.
2
UPMC Medical Faculty (Paris 6), Department of Fetal Medicine in Armand Trousseau Hospital (APHP), INSERM U1142 (LIMICS), 26 Avenue du Dr Arnold Netter, 75012, Paris, UE, France.
3
INSERM U1153 (Obstetrical, Perinatal and Pediatric Epidemiology Research Team, Center for Biostatistics and Epidemiology), Maternité Port Royal, 53 Avenue de l'Observatoire, 75014, Paris, UE, France.
4
APHP DSI, INSERM U1142 (LIMICS), 15, rue de l'École de Médecine, 75006, Paris, UE, France.
5
Pyramides Medical Imaging Center, 13 av. de l'Opéra, 75001, Paris, UE, France.
6
University College Hospital (UCLH) Department of Obstetrics and Gynaecology, Academic Department of Obstetrics and Gynaecology, University College London (UCL) Institute for Women's Health, 86-96 Chenies Mews, London, WC1E 6HX, UE, UK.
7
Department of Obstetrics and Gynaecology, Gynaecology Diagnostic and Outpatient Treatment Unit, University College Hospital (UCLH), 235 Euston Road, London, NW1 2BU, UE, UK.

Abstract

BACKGROUND:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

Application ontology; Ectopic pregnancy; Knowledge base

PMID:
28137311
PMCID:
PMC5282861
DOI:
10.1186/s13326-017-0117-1
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for BioMed Central Icon for PubMed Central
Loading ...
Support Center