The use of artificial intelligence and machine learning methods in first trimester pre-eclampsia screening: a systematic review protocol

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, first-trimester 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 first-trimester screening for PE. Methods A systematic review of peer-reviewed as well as the grey literature cohort or case-control studies will be conducted. Relevant information will be accessed from the following databases; PubMed, Google Scholar, Web of Science, 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. ### Competing Interest Statement I have read the journal's policy and the authors of this manuscript have the following competing interests: Casper Wilstrup is founder and CEO of Abzu, a company developing AI/ML based research tools. ### Funding Statement The authors received no specific funding for this work. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Not Applicable The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Not relevant for Systematic review I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Not Applicable I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Not Applicable I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Not Applicable No datasets will be generated during the current study. All relevant data from this study will be made available upon study completion. * AI : artificial intelligence ML : machine learning NOS : Newcastle-Ottawa Quality Assessment Scale PE : pre-eclampsia PRISMA : preferred reporting items for systematic reviews and meta-analyses PRISMA-P : PRISMA-protocols PROSPERO : the international prospective register of systematic reviews