Conectar
Risk screening for pre-eclampsia relies on accurate gestational age assessment, but routine access to ultrasound-based gestational dating remains challenging in many low- and middle-income countries. As part of the formative work for the ‘Preventing pre-eclampsia: Evaluating AspiRin Low-dose regimens following risk Screening’ (PEARLS) platform, we aim to validate and implement an artificial intelligence (AI)-based algorithm for estimation of gestational age, using blind sweeps done with a handheld ultrasound device. This study protocol outlines the accuracy cohort for AI-based gestational age estimation in participating facilities in Ghana, Kenya and South Africa.
This multicountry prospective cohort study will recruit 969 pregnant women at 13 health facilities across Kenya, Ghana and South Africa. The eligible population is pregnant women presenting for antenatal visits from 11+0 to 13+6 weeks’ gestation. Eligible women will have a gestational age assessment by a trained sonographer using fetal biometry (reference standard), followed by gestational age estimation conducted by a trained midwife using the AI-based Intelligent Ultrasound ScanNav FetalCheck system (experimental). Both conventional and AI-based gestational age scans will be conducted with the General Electric VScan Air platform. Women will return for a second visit between 14+0 and 27+6 weeks’ gestation (week of visit is randomly selected) for an assessment with both conventional and AI-based ultrasound. The primary objective is to determine the accuracy and precision of gestational age estimation using an AI ultrasound system in first and second trimesters, as compared with gestational age estimation using crown-rump length measurement by conventional ultrasound in first trimester (11+0 to 13+6 weeks’).
This study has received or sought ethics approval from the following entities: Australia: University of Melbourne, Office of Research Ethics and Integrity (Reference Number: 2024–28489-49438-3) and the Alfred Hospital Ethics Committee (Reference: Project 727/23); Ghana: Ghana Health Service Ethics Review Committee (GHS-ERC Number 002/01/24); Kenya: Kenyatta National Hospital, University of Nairobi ERC (Ref: KNH-ERC/01/MISC/20); South Africa: University of Cape Town, Faculty of Health Science, Human Research Ethics Committee (HREC Ref: 138/2024). Key findings will be disseminated to research teams to inform future scale-up of AI-based pregnancy dating and pre-eclampsia risk screening. Findings from this pilot work will be published in peer-reviewed open-access journals, conferences and meetings to maximise reach of our findings.
by John Amoah-Mensah, Mohamedahmed Mirghani Hassan Mohamed, Reindorf Nartey Borkor, Rhoda Afutu, Nicholas Kwasi-Do Ohene Opoku
BackgroundThis study investigates the transmission dynamics of typhoid fever and assesses the impact of environmental factors and public health interventions on disease spread. Typhoid fever, caused by Salmonella Typhi, remains a major public health concern in regions with poor sanitation, high population density, and limited access to clean water. Although environmental contamination plays a critical role in sustaining transmission, its contribution is often under explored in mathematical modeling studies.
MethodsWe developed a deterministic compartmental model incorporating environmental transmission pathways to better understand the role of contaminated water sources and human-environment interactions in the spread of typhoid fever. The model is formulated as a system of nonlinear ordinary differential equations. The basic reproduction number, R0 was derived using the next-generation matrix approach to determine the threshold conditions for disease persistence. We analyzed the existence and stability of the disease-free and endemic equilibrium points, establishing local and global stability results for R0≤1 and R0 > 1, respectively. Sensitivity analysis on the reproduction number and the endemic equilibrium was conducted to identify parameters with the greatest influence on disease transmission. Furthermore, the model was extended to an optimal control framework incorporating two intervention strategies: public health education campaigns and treatment of contaminated water bodies. Pontryagin’s Maximum Principle was applied to characterize the optimal controls and derive the associated optimality system. Model parameters were estimated using reported typhoid fever data from Ethiopia obtained through the World Health Organization. Numerical simulations were performed to evaluate the impact of individual and combined intervention strategies.
ResultsSimulation results indicate that the combined implementation of environmental sanitation measures and educational interventions significantly reduces disease burden, particularly during outbreak periods.
ConclusionThese findings highlight the importance of integrating environmental management and community-based public health strategies in typhoid control programs.
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