Clinical workflow of sonographers performing fetal anomaly ultrasound scans: deep-learning-based analysis.

anatomy artificial intelligence automation big data clinical workflow computer vision data science deep learning image analysis machine learning neural network obstetrics pregnancy screening sonography ultrasound

Journal

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
ISSN: 1469-0705
Titre abrégé: Ultrasound Obstet Gynecol
Pays: England
ID NLM: 9108340

Informations de publication

Date de publication:
12 2022
Historique:
revised: 04 06 2022
received: 15 03 2022
accepted: 10 06 2022
pubmed: 22 6 2022
medline: 6 12 2022
entrez: 21 6 2022
Statut: ppublish

Résumé

Despite decades of obstetric scanning, the field of sonographer workflow remains largely unexplored. In the second trimester, sonographers use scan guidelines to guide their acquisition of standard planes and structures; however, the scan-acquisition order is not prescribed. Using deep-learning-based video analysis, the aim of this study was to develop a deeper understanding of the clinical workflow undertaken by sonographers during second-trimester anomaly scans. We collected prospectively full-length video recordings of routine second-trimester anomaly scans. Important scan events in the videos were identified by detecting automatically image freeze and image/clip save. The video immediately preceding and following the important event was extracted and labeled as one of 11 commonly acquired anatomical structures. We developed and used a purposely trained and tested deep-learning annotation model to label automatically the large number of scan events. Thus, anomaly scans were partitioned as a sequence of anatomical planes or fetal structures obtained over time. A total of 496 anomaly scans performed by 14 sonographers were available for analysis. UK guidelines specify that an image or videoclip of five different anatomical regions must be stored and these were detected in the majority of scans: head/brain was detected in 97.2% of scans, coronal face view (nose/lips) in 86.1%, abdomen in 93.1%, spine in 95.0% and femur in 92.3%. Analyzing the clinical workflow, we observed that sonographers were most likely to begin their scan by capturing the head/brain (in 24.4% of scans), spine (in 23.2%) or thorax/heart (in 22.8%). The most commonly identified two-structure transitions were: placenta/amniotic fluid to maternal anatomy, occurring in 44.5% of scans; head/brain to coronal face (nose/lips) in 42.7%; abdomen to thorax/heart in 26.1%; and three-dimensional/four-dimensional face to sagittal face (profile) in 23.7%. Transitions between three or more consecutive structures in sequence were uncommon (up to 13% of scans). None of the captured anomaly scans shared an entirely identical sequence. We present a novel evaluation of the anomaly scan acquisition process using a deep-learning-based analysis of ultrasound video. We note wide variation in the number and sequence of structures obtained during routine second-trimester anomaly scans. Overall, each anomaly scan was found to be unique in its scanning sequence, suggesting that sonographers take advantage of the fetal position and acquire the standard planes according to their visibility rather than following a strict acquisition order. © 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Identifiants

pubmed: 35726505
doi: 10.1002/uog.24975
pmc: PMC10107110
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

759-765

Subventions

Organisme : European Research Council
ID : ERC-ADG-2015 694581
Pays : International
Organisme : Department of Health
Pays : United Kingdom

Informations de copyright

© 2022 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Références

Med Image Anal. 2021 Apr;69:101973
pubmed: 33550004
Am J Obstet Gynecol. 2021 Aug;225(2):200-202
pubmed: 33905743
Pediatr Cardiol. 2018 Apr;39(4):726-730
pubmed: 29350246
Am J Obstet Gynecol. 2018 Jan;218(1):19-28
pubmed: 28688814
J Matern Fetal Neonatal Med. 2019 Feb;32(4):666-670
pubmed: 29041834
J Med Imaging Radiat Oncol. 2017 Jun;61(3):304-310
pubmed: 27753281
Ultrasound Obstet Gynecol. 2016 May;47(5):565-72
pubmed: 26582756
Am J Obstet Gynecol. 2020 Jun;222(6):615.e1-615.e9
pubmed: 31930994
Ultrasound Obstet Gynecol. 2020 Oct;56(4):498-505
pubmed: 32530098
Ultrasound Obstet Gynecol. 2022 Jun;59(6):840-856
pubmed: 35592929
J Ultrasound Med. 2018 Nov;37(11):E13-E24
pubmed: 30308091
AJR Am J Roentgenol. 2016 Apr;206(4):792-6
pubmed: 26866956
Prenat Diagn. 2008 Sep;28(9):822-7
pubmed: 18646244
Ultrasound Obstet Gynecol. 2017 Oct;50(4):429-441
pubmed: 27546497
Ultrasound Obstet Gynecol. 2020 Oct;56(4):579-587
pubmed: 31909548
Ultrasound Obstet Gynecol. 2022 Mar;59(3):304-316
pubmed: 34940999
Sci Rep. 2020 Jun 23;10(1):10200
pubmed: 32576905
Front Public Health. 2019 Sep 04;7:244
pubmed: 31552212
J Ultrasound Med. 2013 May;32(5):847-50
pubmed: 23620327
Ultraschall Med. 2020 Apr;41(2):138-145
pubmed: 32107757
IEEE Trans Cybern. 2017 Jun;47(6):1576-1586
pubmed: 28371793
IEEE Trans Med Imaging. 2017 Nov;36(11):2204-2215
pubmed: 28708546
BJOG. 2018 Nov;125(12):1568
pubmed: 30302930
Sci Rep. 2020 Aug 6;10(1):13223
pubmed: 32764673
Obstet Gynecol. 2010 Jun;115(6):1233-1238
pubmed: 20502295
J Ultrasound Med. 2020 Jan;39(1):E5-E16
pubmed: 31846540
Sci Rep. 2021 Jul 8;11(1):14109
pubmed: 34238950
Ultrasound Obstet Gynecol. 2020 Mar;55(3):375-382
pubmed: 31763735
New Bioeth. 2023 Jan 21;:1-10
pubmed: 36680493

Auteurs

L Drukker (L)

Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK.
Women's Ultrasound, Department of Obstetrics and Gynecology, Beilinson Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

H Sharma (H)

Institute of Biomedical Engineering, University of Oxford, Oxford, UK.

J N Karim (JN)

Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK.

R Droste (R)

Institute of Biomedical Engineering, University of Oxford, Oxford, UK.

J A Noble (JA)

Institute of Biomedical Engineering, University of Oxford, Oxford, UK.

A T Papageorghiou (AT)

Nuffield Department of Women's and Reproductive Health, John Radcliffe Hospital, University of Oxford, Oxford, UK.

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Classifications MeSH