Format

Send to

Choose Destination
J Gynecol Obstet Hum Reprod. 2017 May;46(5):423-429. doi: 10.1016/j.jogoh.2017.03.004. Epub 2017 Mar 31.

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

Author information

1
Inserm U1142 (Limics), UPMC medical faculty (Paris 6), department of fetal medicine, service de médecine fœtale, hôpital Armand-Trousseau, AP-HP, 26, avenue A.-Netter, 75012 Paris, France.
2
Inserm U1142 (Limics), UPMC medical faculty (Paris 6), department of fetal medicine, service de médecine fœtale, hôpital Armand-Trousseau, AP-HP, 26, avenue A.-Netter, 75012 Paris, France. Electronic address: ferdinand.dhombres@trs.aphp.fr.
3
Inserm U1153, obstetrical, perinatal and pediatric epidemiology research team, center for biostatistics and epidemiology, 75014 Paris, France.
4
Inserm U1142 (Limics), AP-HP DSI, 75006 Paris, France.
5
Pyramids medical imaging center, 75001 Paris, France.
6
Academic department of obstetrics and gynaecology, gynaecology diagnostic and outpatient treatment unit, university college hospital (UCLH), university college London (UCL), institute for women's health, London, UK.

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.

KEYWORDS:

Decision support system; Ectopic pregnancy; Image annotations; Knowledge base; Ontology; Ultrasound imaging

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center