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Cost-effective screening of nutritional and genetic anemias with a portable light scattering system (Conference Presentation)
Author(s): Zachary J. Smith; Lieshu Tong; Josef Kauer; Xi Chen; Hu Dou; Kaiqin Chu
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Paper 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.

Paper Details

Date Published: 4 March 2019
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Proc. SPIE 10869, Optics and Biophotonics in Low-Resource Settings V, 108690R (4 March 2019); doi: 10.1117/12.2506572
Show Author Affiliations
Zachary J. Smith, Univ. of Science and Technology of China (China)
Lieshu Tong, Univ. of Science and Technology of China (China)
Josef Kauer, Beuth Hochschule für Technik Berlin (Germany)
Xi Chen, The Affiliated Children's Hospital, Chongqing Medical Univ. (China)
Hu Dou, The Affiliated Children's Hospital, Chongqing Medical Univ. (China)
Kaiqin Chu, Univ. of Science and Technology of China (China)


Published in SPIE Proceedings Vol. 10869:
Optics and Biophotonics in Low-Resource Settings V
David Levitz; Aydogan Ozcan, Editor(s)

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