Developmental screening tool: Accuracy and feasibility of non-invasive anaemia estimation

Abstract

Anaemia is predicted as one of the serious communal health issue in the world. The deficiency exists most common among children and women. A substantial issue prevails in providing quality healthcare services to rural communities, which remains a challenge to health service providers throughout the world. Traditionally physician and health workers recognized anaemia from certain clinical findings, such as pallor of the conjunctivae, nail beds, lips, tongue, and oral mucosa. Confirmation of anaemic condition through physical examination of Dorsum of a tongue or lower bulbar conjunctiva is a subjective analysis.

Invasive methods have a possibility to spread infection through the needle. The existing non-invasive techniques need costly equipment and qualified technicians. Growing developments in science and technologies play an important role in medicine. This proposal introduces a new non-invasive diagnostic tool correlating the hemoglobin with conjunctiva pallor colour scores and classification using neural networks.

In this study, the eye images were obtained using a mobile camera were processed using the HSI model, which estimates different colour scores of the selected region. These scores were correlated with laboratory haemoglobin value. Feedforward neural network and Elman neural network were used for classifying anaemic and non-anaemic cases. This proposed tool will be useful for the health workers to identify the mass screening of anaemia in rural areas.