The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol
Introduction Perinatal depression and anxiety (PDA) is associated with a high risk of maternal mortality. Existing data shows that 95% of maternal mortality in low- and middle-income countries (LMICs) is due to resource constraints and negligence in addressing perinatal mental health (PMH). Research...
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BMJ Publishing Group
2025-04-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/15/4/e091531.full |
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| author | Uchechi Shirley Anaduaka Ayomide Oluwaseyi Oladosu Samantha Katsande Clinton Sekyere Frempong Success Awuku-Amador |
| author_facet | Uchechi Shirley Anaduaka Ayomide Oluwaseyi Oladosu Samantha Katsande Clinton Sekyere Frempong Success Awuku-Amador |
| author_sort | Uchechi Shirley Anaduaka |
| collection | DOAJ |
| description | Introduction Perinatal depression and anxiety (PDA) is associated with a high risk of maternal mortality. Existing data shows that 95% of maternal mortality in low- and middle-income countries (LMICs) is due to resource constraints and negligence in addressing perinatal mental health (PMH). Research conducted in more developed countries has demonstrated the potential of artificial intelligence (AI) to assist in predicting, identifying, diagnosing and treating PDA. However, there is limited knowledge regarding the utilisation of AI in LMICs where PDA disproportionately affects women. Therefore, this study aims to investigate the role of AI in predicting, identifying, diagnosing and treating PDA among pregnant women and mothers in LMICs.Methods and analysis This systematic review will use a patient and public involvement (PPI) approach to systematically investigate the role of AI in predicting, identifying, diagnosing, and treating PDA among pregnant women and mothers in LMICs. The study will combine secondary evidence from academic databases and primary evidence from focus group discussions and a workshop and webinar to comprehensively analyse all relevant published and reported evidence on PDA and AI from the period between January 2010 and May 2024. To gather the necessary secondary data, reputable interdisciplinary databases in the field of maternal health and AI will be used, including ACM Digital Library, CINAHL, MEDLINE, PsycINFO, Scopus and Web of Science. The extracted data will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, ensuring transparency and comprehensiveness in reporting the findings. Finally, the extracted studies will be synthesised using the integrative data synthesis approach.Ethics and dissemination Given the PPI approach to be employed by this study which involves multi-stakeholders including mothers with lived experience, ethical approvals have been sought from the University of Ghana and University of Alberta. Additionally, during the review process, to ensure that the articles included in this study uphold ethical standards, only peer-reviewed articles from reputable journals/databases will be included in this review. The findings from this systematic review will be disseminated through workshops, webinars, conferences, academic publications, social media and all relevant platforms available to the researchers.PROSPERO registration number PROSPERO (10/06/24) CRD42024549455. |
| format | Article |
| id | doaj-art-87fdb87d863944deb1e6ee1705811392 |
| institution | OA Journals |
| issn | 2044-6055 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-87fdb87d863944deb1e6ee17058113922025-08-20T02:14:45ZengBMJ Publishing GroupBMJ Open2044-60552025-04-0115410.1136/bmjopen-2024-091531The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocolUchechi Shirley Anaduaka0Ayomide Oluwaseyi Oladosu1Samantha Katsande2Clinton Sekyere Frempong3Success Awuku-Amador41 School of Public Health, University of Alberta, Edmonton, Alberta, Canada2 School of Graduate Studies, Lingnan University, Tuen Mun, Hong Kong3 Action on Preeclampsia Ghana, Accra, Ghana4 Department of Population and Behavioural Sciences, School of Public Health, University of Health and Allied Sciences, Hohoe, Ghana5 Digitalized for Jobs (D4J), GEA/GIZ, Accra, GhanaIntroduction Perinatal depression and anxiety (PDA) is associated with a high risk of maternal mortality. Existing data shows that 95% of maternal mortality in low- and middle-income countries (LMICs) is due to resource constraints and negligence in addressing perinatal mental health (PMH). Research conducted in more developed countries has demonstrated the potential of artificial intelligence (AI) to assist in predicting, identifying, diagnosing and treating PDA. However, there is limited knowledge regarding the utilisation of AI in LMICs where PDA disproportionately affects women. Therefore, this study aims to investigate the role of AI in predicting, identifying, diagnosing and treating PDA among pregnant women and mothers in LMICs.Methods and analysis This systematic review will use a patient and public involvement (PPI) approach to systematically investigate the role of AI in predicting, identifying, diagnosing, and treating PDA among pregnant women and mothers in LMICs. The study will combine secondary evidence from academic databases and primary evidence from focus group discussions and a workshop and webinar to comprehensively analyse all relevant published and reported evidence on PDA and AI from the period between January 2010 and May 2024. To gather the necessary secondary data, reputable interdisciplinary databases in the field of maternal health and AI will be used, including ACM Digital Library, CINAHL, MEDLINE, PsycINFO, Scopus and Web of Science. The extracted data will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, ensuring transparency and comprehensiveness in reporting the findings. Finally, the extracted studies will be synthesised using the integrative data synthesis approach.Ethics and dissemination Given the PPI approach to be employed by this study which involves multi-stakeholders including mothers with lived experience, ethical approvals have been sought from the University of Ghana and University of Alberta. Additionally, during the review process, to ensure that the articles included in this study uphold ethical standards, only peer-reviewed articles from reputable journals/databases will be included in this review. The findings from this systematic review will be disseminated through workshops, webinars, conferences, academic publications, social media and all relevant platforms available to the researchers.PROSPERO registration number PROSPERO (10/06/24) CRD42024549455.https://bmjopen.bmj.com/content/15/4/e091531.full |
| spellingShingle | Uchechi Shirley Anaduaka Ayomide Oluwaseyi Oladosu Samantha Katsande Clinton Sekyere Frempong Success Awuku-Amador The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol BMJ Open |
| title | The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol |
| title_full | The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol |
| title_fullStr | The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol |
| title_full_unstemmed | The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol |
| title_short | The role of artificial intelligence in the prediction, identification, diagnosis and treatment of perinatal depression and anxiety among women in LMICs: a systematic review protocol |
| title_sort | role of artificial intelligence in the prediction identification diagnosis and treatment of perinatal depression and anxiety among women in lmics a systematic review protocol |
| url | https://bmjopen.bmj.com/content/15/4/e091531.full |
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