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
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.045 | 0.055 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.014 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".