Currently, one-third of humanity is still suffering from anemia. In China the most common forms of anemia are iron deficiency and Thalassemia minor. Differentiating these two is the key to effective treatment. Iron deficiency is caused by malnutrition and can be cured by iron supplementation. Thalassemia is a hereditary disease in which the hemoglobin β chain is lowered or absent. Iron therapy is not effective, and there is evidence that iron therapy may be harmful to patients with Thalassemia. Both anemias can be diagnosed using red blood cell morphology: Iron deficiency presents a smaller mean cell volume compared to normal cells, but with a wide distribution; Thalassemia, meanwhile, presents a very small cell size and tight particle size distribution. Several researchers have proposed diagnostic indices based on red cell morphology to differentiate these two diseases. However, these indices lack sensitivity and specificity and are constructed without statistical rigor. Using multivariate methods we demonstrate a new classification method based on red cell morphology that diagnoses anemia in a Chinese population with enough accuracy for its use as a screening method. We further demonstrate a low cost instrument that precisely measures red cell morphology using elastic light scattering. This instrument is combined with an automated analysis program that processes scattering data to report red cell morphology without the need for user intervention. Despite using consumer-grade components, when comparing our experimental results with gold-standard measurements, the device can still achieve the high precision required for sensing clinically significant changes in red cell morphology.
Lieshu Tong, Xinrui Wang, Dengling Xie, Xiaoya Chen, Kaiqin Chu, Hu Dou, and Zachary J. Smith, "Evaluation of anemia diagnosis based on elastic light scattering (Conference Presentation)," Proc. SPIE 10055, Optics and Biophotonics in Low-Resource Settings III, 100550E (Presented at SPIE BiOS: January 28, 2017; Published: 19 April 2017); https://doi.org/10.1117/12.2250686.5371876607001.
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