[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-study-detail:100590958":3},{"organization":4,"armGroups":7,"interventions":19,"overallOfficials":24,"centralContacts":28,"locations":37,"responsibleParty":52,"collaborators":54,"id":63,"slug":64,"hasResults":65,"nctId":66,"briefTitle":67,"officialTitle":68,"acronym":69,"eligibilityCriteria":70,"healthyVolunteers":71,"sex":72,"minAge":73,"maxAge":74,"enrollmentInfo":75,"targetDuration":18,"studyType":78,"phases":79,"briefSummary":81,"conditions":82,"keywords":86,"overallStatus":97,"whyStopped":18,"lastUpdateSubmitDate":98,"lastUpdatePostDateStruct":99,"startDateStruct":102,"completionDateStruct":104,"leadSponsor":106,"locationsCount":107},{"fullName":5,"class":6},"National University Hospital, Singapore","OTHER",[8,14],{"label":9,"type":10,"description":11,"interventionNames":12},"AI-risk stratification arm","EXPERIMENTAL","During their first trimester antenatal visit, participants will be risk stratified by clinicians with the assistance of AI. The risk prediction of 'high risk' or 'low risk' will be immediately made known to the clinician during the visit.\n\nSimilarly, during the 31 -34 weeks visit following release of oral glucose tolerance test and growth scan results, participants will be risk stratified by clinicians again with the assistance of AI.",[13],"Other: AI-risk Stratification",{"label":15,"type":16,"description":17,"interventionNames":18},"Non-AI arm (Current Risk Stratification)","NO_INTERVENTION","During their first trimester antenatal visit, participants will be risk stratified by both clinicians alone and with the assistance of AI. However, the risk prediction of 'high risk' or 'low risk' will not be disclosed to the clinician at all. It will only be revealed to the study investigators at the end of the study.\n\nSimilarly, during the 31 -34 weeks visit following release of oral glucose tolerance test and growth scan results, participants will be risk stratified by clinicians again with and without the assistance of AI. However, the risk prediction of 'high risk' or 'low risk' will not be disclosed to the clinician at all. It will only be revealed to the study investigators at the end of the study.\n\nClinicians will use their own judgement as to the risk of the participants' pregnancies. They have to adhere to the same specific 'high-risk' and 'low-risk' management protocol.",null,[20],{"type":6,"name":21,"description":22,"armGroupLabels":23,"otherNames":18},"AI-risk Stratification","With the results being disclosed as 'high-risk' or 'low-risk' in the experimental arm, clinicians have to adhere to a specific 'high-risk' and 'low-risk' management protocol for participants.",[9],[25],{"name":26,"affiliation":5,"role":27},"Sarah Li, MRCOG, MPH","PRINCIPAL_INVESTIGATOR",[29,33],{"name":26,"role":30,"phone":31,"phoneExt":18,"email":32},"CONTACT","(+65)97833106","sarah_wl_li@nuhs.edu.sg",{"name":34,"role":30,"phone":35,"phoneExt":18,"email":36},"Harshaana Ramlal, BSc (Hons)","(+65) 90065802","ramlal_harshaana@nuhs.edu.sg",[38],{"facility":39,"status":18,"city":40,"state":18,"zip":18,"country":40,"countryCode":41,"cosmosGeoPoint":42,"geoPoint":47,"contacts":48},"National University Hospital","Singapore","SG",{"type":43,"coordinates":44},"Point",[45,46],103.85007,1.28967,{"lat":46,"lon":45},[49],{"name":50,"role":30,"phone":51,"phoneExt":18,"email":32},"Sarah Li Dr","+65 97833106",{"type":53,"investigatorFullName":18,"investigatorTitle":18,"investigatorAffiliation":18,"oldNameTitle":18,"oldOrganization":18},"SPONSOR",[55,57,60],{"name":56,"class":6},"National University of Singapore",{"name":58,"class":59},"National University of Singapore, Saw Swee Hock School of Public Health","UNKNOWN",{"name":61,"class":62},"Agency for Science, Technology and Research (A*STAR)","OTHER_GOV","100590958","ai-powered-cura-application-for-identifying-at-risk-pregnancies-in-obstetric-management-100590958",false,"NCT06974188","AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management","AI-Powered CURAᵀᴹ Application for Identifying At-Risk Pregnancies in Obstetric Management: A Randomized Controlled Trial (CURAte)","CURAte","Inclusion Criteria:\n\n* Age: 21 years old to 50 years old\n* Singleton Pregnancy\n* No more than 13 weeks' and 6 days' gestation at recruitment\n* Able to provide written, informed consent\n\nExclusion Criteria:\n\n* Not proficient in the English language (AI intervention is only available in English at this stage)",true,"FEMALE","21 Years","50 Years",{"count":76,"type":77},1700,"ESTIMATED","INTERVENTIONAL",[80],"NA","It is important to identify high pregnancies early through screening so that appropriate care and intervention may be instituted. An AI-assisted risk categorisation approach may be advantageous compared with traditional means of screening. The purpose of this study is to determine if the adoption of an AI-assisted approach in general pregnancy risk screening will improve the accuracy of antenatal risk categorization into high- and low- risk pregnancy groups, ultimately resulting in fewer poor maternal and fetal\u002Fneonatal outcomes.",[83,84,85],"High-risk Pregnancy","Pregnancy","Antenatal Health",[87,88,89,90,91,92,93,94,95,96],"Machine-learning","Artificial Intelligence","Antenatal Risk Stratification","Software as a Medical Device","Machine Learning Algorithm","Pregnancy Complications","Neonatal Complications","Pregnancy outcomes","Obstetric complications","Antenatal care","NOT_YET_RECRUITING","2025-07-14",{"date":100,"type":101},"2025-07-15","ACTUAL",{"date":103,"type":77},"2025-08",{"date":105,"type":77},"2027-03",{"name":5,"class":6},1]