Anaemia affects approximately one quarter of the global population and is associated with a range of health concerns, including impaired physical and cognitive development, complications during pregnancy and childbirth, and reduced overall wellbeing. Conversely, iron overload, while less common, poses serious risks such as liver cirrhosis and heart failure. Despite these concerns, both conditions are frequently under-recognised due to their often non-specific symptoms and the limited opportunities for routine haemoglobin screening, particularly among pregnant women and vulnerable populations. Senior Assistant Professor Mikiko Shimizu and her colleagues at Fujita Health University are addressing this issue through the development of a smartphone-based application capable of estimating haemoglobin levels using machine learning analysis of conjunctival images. By enabling individuals to screen for both anaemia and iron overload at any time and in non-clinical environments, the application supports earlier identification and intervention, thereby reducing the likelihood of progression to more severe disease. The research team has refined image capture techniques and data analysis models to improve accuracy and usability, while also working to expand access to non-invasive screening methods suitable for infants and young children. Future aims include integrating personalised dietary guidance and deploying the system within university health programmes to further investigate the relationship between iron status, wellbeing, and performance. This work has the potential to significantly enhance self-care practices, improve maternal and public health outcomes, and reduce economic burdens through preventative healthcare approaches.
Mikiko Shimizu (Tue,) studied this question.