⚡ Quick Summary
This systematic review protocol aims to explore the role of artificial intelligence (AI) in the prediction, identification, diagnosis, and treatment of perinatal depression and anxiety (PDA) among women in low- and middle-income countries (LMICs). Given that 95% of maternal mortality in LMICs is linked to inadequate mental health support, this research could significantly impact maternal health outcomes. 🌍
🔍 Key Details
- 📅 Study Period: January 2010 to May 2024
- 🔍 Focus: Role of AI in perinatal mental health
- 📚 Databases Used: ACM Digital Library, CINAHL, MEDLINE, PsycINFO, Scopus, Web of Science
- 🛠️ Methodology: Systematic review with patient and public involvement (PPI)
- 📊 Reporting Framework: PRISMA guidelines
🔑 Key Takeaways
- 🤰 Perinatal depression and anxiety (PDA) significantly affect maternal health, especially in LMICs.
- 💻 AI technologies have shown promise in improving mental health outcomes in more developed countries.
- 📈 Systematic review will synthesize both secondary and primary evidence on AI’s role in PDA.
- 👥 Multi-stakeholder involvement ensures that the perspectives of mothers with lived experiences are included.
- 🔒 Ethical standards will be upheld by including only peer-reviewed articles in the review.
- 🌐 Findings will be disseminated through various platforms, including workshops and social media.
- 📝 PROSPERO Registration: CRD42024549455
📚 Background
Perinatal depression and anxiety (PDA) are critical issues that can lead to severe consequences, including maternal mortality. In LMICs, the lack of resources and attention to perinatal mental health exacerbates these challenges. While AI has been effectively utilized in more affluent settings to address similar issues, its application in LMICs remains underexplored. This study seeks to bridge that gap by investigating how AI can be leveraged to improve outcomes for mothers and their children. 🤱
🗒️ Study
The systematic review will employ a patient and public involvement (PPI) approach, integrating insights from mothers and stakeholders to ensure a comprehensive understanding of the challenges faced in LMICs. The research will analyze data from reputable academic databases and gather primary evidence through focus group discussions, workshops, and webinars. This multifaceted approach aims to provide a thorough examination of the existing literature on AI’s role in addressing PDA. 📊
📈 Results
As this is a protocol for a systematic review, specific results are yet to be determined. However, the study aims to synthesize findings that will highlight the effectiveness of AI in predicting, identifying, diagnosing, and treating PDA. The anticipated outcomes could provide valuable insights into how AI technologies can be adapted for use in LMICs, potentially leading to improved maternal health services. 📉
🌍 Impact and Implications
The implications of this study are profound. By systematically reviewing the role of AI in perinatal mental health, we can identify effective strategies that could be implemented in LMICs to reduce maternal mortality rates associated with PDA. This research could pave the way for innovative solutions that enhance mental health support for mothers, ultimately leading to healthier families and communities. 🌟
🔮 Conclusion
This systematic review protocol underscores the critical need for research into the application of AI in perinatal mental health, particularly in LMICs. By focusing on the intersection of technology and maternal health, we can explore new avenues for improving outcomes for mothers and their children. The future of maternal mental health care could be transformed through the integration of AI, and we look forward to the findings that will emerge from this important study. 🚀
💬 Your comments
What are your thoughts on the potential of AI in addressing perinatal depression and anxiety? We invite you to share your insights and engage in this important conversation! 💬 Please leave your comments below or connect with us on social media:
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.
Abstract
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.
Author: [‘Anaduaka US’, ‘Oladosu AO’, ‘Katsande S’, ‘Frempong CS’, ‘Awuku-Amador S’]
Journal: BMJ Open
Citation: Anaduaka US, et al. 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. 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. 2025; 15:e091531. doi: 10.1136/bmjopen-2024-091531