Session: 803. Emerging Diagnostic Tools and Techniques II
Hematology Disease Topics & Pathways:
Anemias, sickle cell disease, Diseases, sickle cell trait, bioengineering, Hemoglobinopathies, Technology and Procedures, Clinically relevant, newborn screening
Methods: In 2019, the World Health Organization (WHO) listed Hb electrophoresis as an essential in vitro diagnostic (IVD) technology for diagnosing SCD and sickle cell trait. We have leveraged the common Hb electrophoresis method and developed a POC microchip electrophoresis test, Hemoglobin Variant/Anemia (HbVA). This technology is being commercialized under the product name “Gazelle” by Hemex Health Inc. for Hb variant identification with integrated anemia detection (Fig. 1A&B). We hypothesized that computer vision and deep learning will enhance the accuracy and reproducibility of blood Hb level prediction and anemia detection in cellulose acetate based Hb electrophoresis, which is a clinical standard test for Hb variant screening and diagnosis worldwide (Fig. 1C). To test this hypothesis, we integrated, for the first time, a new, computer vision and artificial neural network (ANN) based deep learning imaging and data analysis algorithm, to Hb electrophoresis. Here, we show the feasibility of this new, computer vision and deep learning enabled diagnostic approach via testing of 46 subjects, including individuals with anemia and homozygous (HbSS) or heterozygous (HbSC or Sβ-thalassemia) SCD.
Results and Discussion: HbVA computer vision tracked the electrophoresis process real-time and the deep learning neural network algorithm determined Hb levels which demonstrated significant correlation with a Pearson Correlation Coefficient of 0.95 compared to the results of reference standard CBC (Fig.1D). Furthermore, HbVA demonstrated high reproducibly with a mean absolute error of 0.55 g/dL and a bias of -0.10 g/dL (95% limits of agreement: 1.5 g/dL) according to Bland-Altman analysis (Fig. 1E). Anemia determination was achieved with 100% sensitivity and 92.3% specificity with a receiver operating characteristic area under the curve (AUC) of 0.99 (Fig. 1F). Within the same test, subjects with SCD were identified with 100% sensitivity and specificity (Fig. 1G). Overall, the results suggested that computer vision and deep learning methods can be used to extract new information from Hb electrophoresis, enabling, for the first time, reproducible, accurate, and integrated blood Hb level prediction, anemia detection, and Hb variant identification in a single affordable test at the POC.
Disclosures: An: Hemex Health, Inc.: Patents & Royalties. Hasan: Hemex Health, Inc.: Patents & Royalties. Ahuja: Genentech: Consultancy; Sanofi-Genzyme: Consultancy; XaTec Inc.: Consultancy; XaTec Inc.: Research Funding; XaTec Inc.: Divested equity in a private or publicly-traded company in the past 24 months; Genentech: Honoraria; Sanofi-Genzyme: Honoraria. Little: GBT: Research Funding; Bluebird Bio: Research Funding; BioChip Labs: Patents & Royalties: SCD Biochip (patent, no royalties); Hemex Health, Inc.: Patents & Royalties: Microfluidic electropheresis (patent, no royalties); NHLBI: Research Funding; GBT: Membership on an entity's Board of Directors or advisory committees. Gurkan: Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding; BioChip Labs: Patents & Royalties; Xatek Inc.: Patents & Royalties; Dx Now Inc.: Patents & Royalties.
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