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Related Experiment Video

Updated: Sep 9, 2025

Subjective Refraction Test Using a Smartphone for Vision Screening
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Subjective Refraction Test Using a Smartphone for Vision Screening

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Smartphone-Based Anemia Screening via Conjunctival Imaging with 3D-Printed Spacer: A Cost-Effective Geospatial Health

A M Arunnagiri1, M Sasikala1, N Ramadass2

  • 1Department of Biomedical Engineering, College of Engineering Guindy, Anna University, Chennai, 600025, Tamil Nadu, India.

Current Medical Imaging
|September 5, 2025
PubMed
Summary

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This study introduces a non-invasive anemia detection tool using smartphone eye images. The developed mobile app achieves high accuracy, offering a cost-effective solution for global anemia screening.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computational Health

Background:

  • Anemia, a prevalent blood disorder, necessitates accurate and accessible diagnostic methods.
  • Current anemia diagnosis relies on invasive procedures like blood tests.
  • There is a need for non-invasive, cost-effective anemia screening tools.

Purpose of the Study:

  • To develop a non-invasive anemia detection tool utilizing eye conjunctiva images.
  • To create a cost-effective and portable solution for anemia screening.
  • To integrate anemia detection into a mobile application for regional prevalence mapping.

Main Methods:

  • Eye conjunctiva images were acquired using DSLR, smartphone, and 3D-printed spacer macro lens modalities.
  • Image analysis employed You Only Look Once (YOLOv8), Segment Anything Model (SAM), and K-means clustering.
Keywords:
3D printed spacerAndroid mobile application.AnemiaEye conjunctiva imagesMulti-layer perceptronSAMYOLOv8

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  • An MLP classifier was used to categorize subjects as anemic, moderately anemic, or normal.

    • Main Results:

    • SAM segmentation features demonstrated higher statistical significance (p < 0.05) than K-means.
    • The 3D-printed spacer macro lens modality showed statistically significant differences compared to DSLR (p < 0.05).
    • Smartphone camera with a 3D spacer achieved 98.3% classification accuracy, comparable to DSLR's 98.8%.

    Conclusions:

    • A portable, cost-effective, and user-friendly mobile application for non-invasive anemia screening was developed.
    • The application aids in identifying anemic clusters for targeted healthcare interventions.
    • The tool supports global health initiatives, including Sustainable Development Goal 3.