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Radiomic identification of anemia features in monochromatic conjunctiva photographs in school-age children
Shaun G Hong1, Sang Mok Park1, Semin Kwon1
1Purdue University, Weldon School of Biomedical Engineering, West Lafayette, Indiana, United States.
Insights
New radiomics analysis of conjunctiva photos identifies spatial and textural microvasculature features as potential anemia biomarkers in children. This approach offers a simpler, noninvasive method for anemia screening in resource-limited settings.
Area of Science:
- Biomedical imaging
- Machine learning
- Global health
Background:
- Anemia is a significant global health issue, particularly impacting school-age children's development.
- Delayed anemia detection can lead to severe health complications.
- Current diagnostic methods often require specialized equipment and complex analysis.
Purpose of the Study:
- To introduce a novel, noninvasive method for anemia detection using spatial and textural characteristics of conjunctiva microvasculature.
- To explore the potential of radiomics applied to conjunctiva images for anemia screening.
- To develop a simplified approach for point-of-care anemia detection in resource-limited settings.
Main Methods:
- Radiomics, a machine learning technique, was applied to conjunctiva photographs from 565 children (aged 5-15).
- Analysis included 12,441 palpebral and 12,375 bulbar conjunctiva images captured via smartphone.
- The study focused on spatial and textural features of the microvasculature, moving beyond colorimetric analysis.
Main Results:
- Significant associations were found between spatial and textural features of the conjunctiva microvasculature and anemia status.
- These features demonstrate potential as reliable biomarkers for anemia in school-age children.
- The findings support the use of radiomics for analyzing morphological alterations beyond direct visualization.
Conclusions:
- The proposed radiomics framework offers a simplified alternative to traditional color-based or spectral anemia detection methods.
- This approach reduces hardware and algorithmic complexity for anemia screening.
- It lays the groundwork for accessible, noninvasive point-of-care anemia diagnostics in regions like sub-Saharan Africa.
Significance:
Anemia remains a substantial global health challenge. Delayed detection often leads to various health complications. In school-age children, anemia can impair both cognitive and physical development. Timely detection is particularly critical for this vulnerable population as effective interventions are available even in resource-limited settings.
Aim:
Most existing methods for assessing conjunctiva paleness or redness in anemia detection rely on colorimetric analyses or spectral imaging, which require sophisticated color processing methods or specialized equipment. We introduce an alternative that takes advantage of purely spatial and textural characteristics of the conjunctiva microvasculature for anemia detection.
Approach:
Radiomics, an emerging machine learning approach for conventional medical imaging, is applied to conjunctiva photos to analyze morphological alterations in the microvasculature beyond direct visualization. Radiomic analyses are conducted on 12,441 palpebral and 12,375 bulbar conjunctiva photos, captured using three different smartphone models from 565 children aged 5 to 15 years.
Results:
Spatial and textural features extracted from the palpebral and bulbar conjunctivae are significantly associated with anemia status in school-age children, demonstrating their potential as biomarkers of anemia.
Conclusions:
Instead of relying on color-based or spectral analyses of pallor in the conjunctiva, the proposed framework lays the groundwork for simplifying the hardware and algorithmic requirements of point-of-care, noninvasive anemia screening in sub-Saharan Africa and other resource-limited settings.
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