The Use of Artificial Intelligence and Machine Learning Methods in Early Pregnancy Pre-eclampsia Screening: A Systematic Review Protocol

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2023 Apr 20

PMID 37079505

Authors

Paula L Hedley

Christian M Hagen

Casper Wilstrup

Michael Christiansen

Affiliations

Soon will be listed here.

Abstract

Introduction: Pre-eclampsia (PE) is a leading cause of perinatal morbidity and mortality worldwide. Low-dose aspirin can prevent PE in high risk pregnancies if started early. However, despite intense research into the area, early pregnancy screening for PE risk is still not a routine part of pregnancy care. Several studies have described the application of artificial intelligence (AI) and machine learning (ML) in risk prediction of PE and its subtypes. A systematic review of available literature is necessary to catalogue the current applications of AI/ML methods in early pregnancy screening for PE, in order to better inform the development of clinically relevant risk prediction algorithms which will enable timely intervention and the development of new treatment strategies. The aim of this systematic review is to identify and assess studies regarding the application of AI/ML methods in early pregnancy screening for PE.

Methods: A systematic review of peer-reviewed as well as the pre-published cohort, case-control, or cross-sectional studies will be conducted. Relevant information will be accessed from the following databases; PubMed, Google Scholar, Scopus, Embase, Web of Science, Cochrane Library, Arxiv, BioRxiv, and MedRxiv. The studies will be evaluated by two reviewers in a parallel, blind assessment of the literature, a third reviewer will assess any studies in which the first two reviewers did not agree. The free online tool Rayyan, will be used in this literature assessment stage. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist will be used to guide the review process and the methods of the studies will be assessed using the Newcastle-Ottawa scale. Narrative synthesis will be conducted for all included studies. Meta-analysis will also be conducted where data quality and availability allow.

Ethics And Dissemination: The review will not require ethical approval and the findings will be published in a peer-reviewed journal using the PRISMA guidelines.

Trial Registration: Trial registration: The protocol for this systematic review has been registered in PROSPERO [CRD42022345786]. https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022345786.

References

Mol B, Roberts C, Thangaratinam S, Magee L, de Groot C, Hofmeyr G . Pre-eclampsia. Lancet. 2015; 387(10022):999-1011. DOI: 10.1016/S0140-6736(15)00070-7. View

Page M, McKenzie J, Bossuyt P, Boutron I, Hoffmann T, Mulrow C . The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021; 372:n71. PMC: 8005924. DOI: 10.1136/bmj.n71. View

Hodgins S, Tielsch J, Rankin K, Robinson A, Kearns A, Caglia J . A New Look at Care in Pregnancy: Simple, Effective Interventions for Neglected Populations. PLoS One. 2016; 11(8):e0160562. PMC: 4990268. DOI: 10.1371/journal.pone.0160562. View

Christiansen M, Hedley P, Placing S, Wojdemann K, Carlsen A, Jorgensen J . Maternal Serum Resistin Is Reduced in First Trimester Preeclampsia Pregnancies and Is a Marker of Clinical Severity. Hypertens Pregnancy. 2015; 34(4):422-433. DOI: 10.3109/10641955.2014.913615. View

Zhang J, Meikle S, Trumble A . Severe maternal morbidity associated with hypertensive disorders in pregnancy in the United States. Hypertens Pregnancy. 2003; 22(2):203-12. DOI: 10.1081/PRG-120021066. View

Bertini A, Salas R, Chabert S, Sobrevia L, Pardo F . Using Machine Learning to Predict Complications in Pregnancy: A Systematic Review. Front Bioeng Biotechnol. 2022; 9:780389. PMC: 8807522. DOI: 10.3389/fbioe.2021.780389. View

Barragan-Montero A, Bibal A, Huet Dastarac M, Draguet C, Valdes G, Nguyen D . Towards a safe and efficient clinical implementation of machine learning in radiation oncology by exploring model interpretability, explainability and data-model dependency. Phys Med Biol. 2022; 67(11). PMC: 9870296. DOI: 10.1088/1361-6560/ac678a. View

Topol E . High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019; 25(1):44-56. DOI: 10.1038/s41591-018-0300-7. View

. ACOG Committee Opinion No. 743: Low-Dose Aspirin Use During Pregnancy. Obstet Gynecol. 2018; 132(1):e44-e52. DOI: 10.1097/AOG.0000000000002708. View

10.

Modesti P, Reboldi G, Cappuccio F, Agyemang C, Remuzzi G, Rapi S . Panethnic Differences in Blood Pressure in Europe: A Systematic Review and Meta-Analysis. PLoS One. 2016; 11(1):e0147601. PMC: 4725677. DOI: 10.1371/journal.pone.0147601. View

11.

. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020; 396(10258):1204-1222. PMC: 7567026. DOI: 10.1016/S0140-6736(20)30925-9. View

12.

Laigaard J, Sorensen T, Placing S, Holck P, Frohlich C, Wojdemann K . Reduction of the disintegrin and metalloprotease ADAM12 in preeclampsia. Obstet Gynecol. 2005; 106(1):144-9. DOI: 10.1097/01.AOG.0000165829.65319.65. View

13.

OGorman N, Wright D, Poon L, Rolnik D, Syngelaki A, De Alvarado M . Multicenter screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks' gestation: comparison with NICE guidelines and ACOG recommendations. Ultrasound Obstet Gynecol. 2017; 49(6):756-760. DOI: 10.1002/uog.17455. View

14.

Say L, Chou D, Gemmill A, Tuncalp O, Moller A, Daniels J . Global causes of maternal death: a WHO systematic analysis. Lancet Glob Health. 2014; 2(6):e323-33. DOI: 10.1016/S2214-109X(14)70227-X. View

15.

Tan M, Syngelaki A, Poon L, Rolnik D, OGorman N, Delgado J . Screening for pre-eclampsia by maternal factors and biomarkers at 11-13 weeks' gestation. Ultrasound Obstet Gynecol. 2018; 52(2):186-195. DOI: 10.1002/uog.19112. View

16.

Grill S, Rusterholz C, Zanetti-Dallenbach R, Tercanli S, Holzgreve W, Hahn S . Potential markers of preeclampsia--a review. Reprod Biol Endocrinol. 2009; 7:70. PMC: 2717076. DOI: 10.1186/1477-7827-7-70. View

17.

Rolnik D, Wright D, Poon L, OGorman N, Syngelaki A, de Paco Matallana C . Aspirin versus Placebo in Pregnancies at High Risk for Preterm Preeclampsia. N Engl J Med. 2017; 377(7):613-622. DOI: 10.1056/NEJMoa1704559. View

18.

Christiansen M, Wilstrup C, Hedley P . Explainable "white-box" machine learning is the way forward in preeclampsia screening. Am J Obstet Gynecol. 2022; 227(5):791. DOI: 10.1016/j.ajog.2022.06.057. View

19.

Akolekar R, Zaragoza E, Poon L, Pepes S, Nicolaides K . Maternal serum placental growth factor at 11 + 0 to 13 + 6 weeks of gestation in the prediction of pre-eclampsia. Ultrasound Obstet Gynecol. 2008; 32(6):732-9. DOI: 10.1002/uog.6244. View

20.

Moodley J, Soma-Pillay P, Buchmann E, Pattinson R . Hypertensive disorders in pregnancy: 2019 National guideline. S Afr Med J. 2019; 109(9):12723. View