Presentation
4 March 2019 Cost-effective screening of nutritional and genetic anemias with a portable light scattering system (Conference Presentation)
Author Affiliations +
Abstract
Anemia affects more than ¼ of the world’s population, mostly concentrated in low-resource areas, and carries serious health risks. Yet current screening methods are inadequate due to their inability to separate iron deficiency anemia (IDA) from genetic anemias such as thalassemia trait (TT), thus preventing targeted supplementation of oral iron. Here we present a cost-effective and accurate approach to diagnose anemia and anemia type using measures of cell morphology determined through machine learning applied to optical light scattering measurements. A partial least squares model shows that our system can accurately extract mean cell volume, red cell size heterogeneity, and mean cell hemoglobin concentration with high accuracy. These clinical parameters (or the raw data itself) can be submitted to machine learning algorithms such as quadratic discriminants or support vector machines to classify a patient into healthy, IDA, or TT. A clinical trial conducted on over 268 Chinese children, of which 49 had IDA and 24 had TT, shows >98% sensitivity and specificity for diagnosing anemia, with 81% sensitivity and 86% specificity for discriminating IDA and TT. The majority of the misdiagnoses are IDA patients with particularly severe anemia, possibly requring hospital care. Therefore, in a screening paradigm where anyone testing positive for TT is sent to the hospital for gold-standard diagnosis and care, we maximize patient benefit while minimizing use of scarce resources.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Zachary J. Smith, Lieshu Tong, Josef Kauer, Xi Chen, Hu Dou, and Kaiqin Chu "Cost-effective screening of nutritional and genetic anemias with a portable light scattering system (Conference Presentation)", Proc. SPIE 10869, Optics and Biophotonics in Low-Resource Settings V, 108690R (4 March 2019); https://doi.org/10.1117/12.2506572
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KEYWORDS
Genetics

Light scattering

Iron

Machine learning

Clinical trials

Diagnostics

Optical testing

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