mHealth in sub-Saharan Africa and Europe: A systematic review comparing the use and availability of mHealth approaches in sub-Saharan Africa and Europe

Review of Genetic and Artificial Intelligence approaches to improving Gestational Diabetes Mellitus Screening and Diagnosis in sub-Saharan Africa

Background: Adverse outcomes from gestational diabetes mellitus (GDM) in the mother and newborn are well established. Genetic variants may predict GDM and Artificial Intelligence (AI) can potentially assist with improved screening and early identification in lower resource settings. There is limited information on genetic variants associated with GDM in sub-Saharan Africa and the implementation of AI in GDM screening in sub-Saharan Africa is largely unknown. Methods: We reviewed the literature on what is known about genetic predictors of GDM in sub-Saharan African women. We searched PubMed and Google Scholar for single nucleotide polymorphisms (SNPs) involved in GDM predisposition in a sub-Saharan African population. We report on barriers that limit the implementation of AI that could assist with GDM screening and offer possible solutions. Results: In a Black South African cohort, the minor allele of the SNP rs4581569 existing in the PDX1 gene was significantly associated with GDM. We were not able to find any published literature on the implementation of AI to identify women at risk of GDM before second trimester of pregnancy in sub-Saharan Africa. Barriers to successful integration of AI into healthcare systems are broad but solutions exist. Conclusions: More research is needed to identify SNPs associated with GDM in sub-Saharan Africa. The implementation of AI and its applications in the field of healthcare in the sub-Saharan African region is a significant opportunity to positively impact early identification of GDM.

Rule-based expert system for the diagnosis of maternal complications during pregnancy: For low resource settings

Objectives

Maternal complications are health challenges linked to pregnancy, encompassing conditions like gestational diabetes, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. The diagnosis of common pregnancy complications is challenging due to the similarity in signs and symptoms with general pregnancy indicators, especially in settings with scarce resources where access to healthcare professionals, diagnostic tools, and patient record management is limited. This paper presents a rule-based expert system tailored for diagnosing three prevalent maternal complications: preeclampsia, gestational diabetes mellitus (GDM), and maternal sepsis.

Methods

The risk factors associated with each disease were identified from various sources, including local health facilities and literature reviews. Attributes and rules were then formulated for diagnosing the disease, with a Mamdani-style fuzzy inference system serving as the inference engine. To enhance usability and accessibility, a web-based user interface has been also developed for the expert system. This interface allows users to interact with the system seamlessly, making it easy for them to input relevant information and obtain accurate disease diagnose.

Results

The proposed expert system demonstrated a 94% accuracy rate in identifying the three maternal complications (preeclampsia, GDM, and maternal sepsis) using a set of risk factors. The system was deployed to a custom-designed web-based user interface to improve ease of use.

Conclusions

With the potential to support health services provided during antenatal care visits and improve pregnant women's health outcomes, this system can be a significant advancement in low-resource setting maternal healthcare.