article · Medicine in Novel Technology and Devices
Anaemia is a major public health concern caused by reduced red blood cells, frequently affecting young children in Africa and other developing regions. Without early treatment, the condition can lead to lasting impairments in cognitive, emotional, and social development. Existing computer-aided diagnostic tools have frequently faced constraints caused by limited training datasets. To address this shortage, a new dataset named CP-AnemiC was assembled, comprising conjunctival pallor images paired with haemoglobin level measurements from 710 children aged 6 to 59 months across several hospitals in Ghana. In addition, a joint deep neural network was developed to simultaneously classify anaemia status and estimate haemoglobin levels directly from eye images. Comprehensive evaluations confirm that the joint model performs effectively across both detection and measurement tasks.
Paediatric anaemia can cause severe, long-term developmental and cognitive deficits if it is not detected and treated early. By pairing clinical eye imagery with precise laboratory haemoglobin measurements, this work establishes a robust data foundation and computational benchmark to improve non-invasive screening approaches for young children in developing regions.
This research could support the development of non-invasive, digital screening applications and point-of-care diagnostic software for paediatric healthcare workers. The technology sits at an applied and tested research stage, having demonstrated technical efficacy on hospital-collected image data, though translation into frontline clinical software would still require dedicated productisation and formal regulatory clearance.
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Anemia is a universal public health issue, which occurs as the result of a reduction in red blood cells. This disease is common among children in Africa and other developing countries. If not treated early, children may suffer long-term consequences such as impairment in social, emotional, and cognitive functioning. Early detection of anemia in children is highly desirable for effective treatment measures. While there has been research into the development of computer-aided diagnosis (CAD) systems for anemia diagnosis, a significant proportion of these studies encountered limitations when working with limited datasets. To overcome the existing issues, this paper proposes a large dataset, named CP-AnemiC, comprising 710 individuals (range of age, 6–59 months), gathered from several hospitals in Ghana. The conjunctiva image-based dataset is supported with Hb levels (g/dL) annotations for accurate diagnosis of anemia. A joint deep neural network is developed that simultaneously classifies anemia and estimates hemoglobin levels (g/dL) based on the conjunctival pallor images. This paper conducts a comprehensive experiment on the CP-AnemiC dataset. The experimental results demonstrate the efficacy of the joint deep neural network in both the tasks of anemia classification and Hb levels (g/dL) estimation.
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DOI: 10.1016/j.medntd.2023.100244
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