Evidence map›Paper›PMID 32530098›Full record

ArticleUltrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology2020

Introduction to artificial intelligence in ultrasound imaging in obstetrics and gynecology.

L Drukker, J A Noble, A T Papageorghiou

2 registry-linked trialsOpen access · hybridAbstract read
In one paragraph

Article in Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 95 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
95citing papers in PubMed, 3 pooled it
6.9field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05790473 unknown statusnot on this mapstarted 2023, after this paper: background citation

Artificial Intelligence Algorithm for the Screening of Abnormal Fetal Brain Findings at First Trimester Ultrasound Scan

TypeobservationalSponsorFondazione Policlinico Universitario Agostino Gemelli IRCCSRan2023 to 2025Enrolled10,000ConditionsFetal Anomaly, Brain MalformationArmsArtificial Intelligence
NCT06765512 active not recruitingnot on this mapstarted 2024, after this paper: background citation

Artificial Intelligence-Based Machine Learning to Classify Adenomyosis from Ultrasound Scans: a Multicentre Model Development Study

TypeobservationalSponsorCARE Fertility UKRan2024 to 2026Enrolled10,000ConditionsAdenomyosis of UterusArmsuse of deep learning and automated machine learning to diagnose and classify adenomyosis
3 · Its place in the literature

Who cites it

95 citing papers in PubMed, 3 syntheses or guidelines pooled it, 212 citations in OpenAlex.

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  10. Emerging applications of artificial intelligence for obstetric ultrasound: A scoping review.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
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  12. Review
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  20. When technology advances faster than competence: the risk of deskilling in obstetrics and gynecology.Revista brasileira de ginecologia e obstetricia : revista da Federacao Brasileira das Sociedades de Ginecologia e Obstetricia · 2026
    Article

35 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 2 institutions in 1 country.

L DrukkerNuffield Department of Women's & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, UK.ORCID 0000-0002-5588-1410
J A NobleInstitute of Biomedical Engineering, University of Oxford, Oxford, UK.
A T PapageorghiouNuffield Department of Women's & Reproductive Health, University of Oxford, John Radcliffe Hospital, Oxford, UK.
John Radcliffe Hospital · GBUniversity of Oxford · GB

Funding

Bill and Melinda Gates Foundation 49038Engineering and Physical Sciences Research Council EP/M013774/1Engineering and Physical Sciences Research Council EP/R013853/1H2020 European Research Council ERC-ADG-2015 694581National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC)UK Medical Research Council MR/P027938/1
6 · The paper itself

Abstract

Artificial intelligence (AI) uses data and algorithms to aim to draw conclusions that are as good as, or even better than, those drawn by humans. AI is already part of our daily life; it is behind face recognition technology, speech recognition in virtual assistants (such as Amazon Alexa, Apple's Siri, Google Assistant and Microsoft Cortana) and self-driving cars. AI software has been able to beat world champions in chess, Go and recently even Poker. Relevant to our community, it is a prominent source of innovation in healthcare, already helping to develop new drugs, support clinical decisions and provide quality assurance in radiology. The list of medical image-analysis AI applications with USA Food and Drug Administration or European Union (soon to fall under European Union Medical Device Regulation) approval is growing rapidly and covers diverse clinical needs, such as detection of arrhythmia using a smartwatch or automatic triage of critical imaging studies to the top of the radiologist's worklist. Deep learning, a leading tool of AI, performs particularly well in image pattern recognition and, therefore, can be of great benefit to doctors who rely heavily on images, such as sonologists, radiographers and pathologists. Although obstetric and gynecological ultrasound are two of the most commonly performed imaging studies, AI has had little impact on this field so far. Nevertheless, there is huge potential for AI to assist in repetitive ultrasound tasks, such as automatically identifying good-quality acquisitions and providing instant quality assurance. For this potential to thrive, interdisciplinary communication between AI developers and ultrasound professionals is necessary. In this article, we explore the fundamentals of medical imaging AI, from theory to applicability, and introduce some key terms to medical professionals in the field of ultrasound. We believe that wider knowledge of AI will help accelerate its integration into healthcare. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of the International Society of Ultrasound in Obstetrics and Gynecology.

Indexed as

Artificial IntelligenceFemaleGynecologyHumansObstetricsPregnancyUltrasonography

Identifiers

PMID32530098
PMCPMC7702141
OpenAlexW3034855613

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.