This study examines biomedical engineering innovations in diagnostic devices for resource-limited settings in Equatorial Guinea. A mixed-methods approach was employed, combining quantitative data on diagnostic accuracy and qualitative feedback from end-users. Diagnostic devices demonstrated an average accuracy rate of 95% with a 3% confidence interval for error detection in resource-limited conditions. This innovative approach to diagnostic device design shows promise for improving healthcare accessibility in underserved regions. Further studies should explore cost-effectiveness and scalability before full-scale implementation. The maintenance outcome was modelled as $Y_{it}=\beta_0+\beta_1X_{it}+u_i+\varepsilon_{it}$, with robustness checked using heteroskedasticity-consistent errors.