Automated screening of sickle cells using a smartphone-based microscope and deep learning

Kevin De Haan,Doruk Karinca,Aydogan Ozcan,Derek Tseng,Hatice Ceylan Koydemir,Esin Gumustekin,Kyle Liang,Yair Rivenson,Elizabeth Van Dyne,Megha Ilango,Lissette Bakic

doi:10.1038/s41746-020-0282-y

Kevin De Haan, Doruk Karinca + Show 9 more

Open Access

https://doi.org/10.1038/s41746-020-0282-y

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Abstract

Sickle cell disease (SCD) is a major public health priority throughout much of the world, affecting millions of people. In many regions, particularly those in resource-limited settings, SCD is not consistently diagnosed. In Africa, where the majority of SCD patients reside, more than 50% of the 0.2–0.3 million children born with SCD each year will die from it; many of these deaths are in fact preventable with correct diagnosis and treatment. Here, we present a deep learning framework which can perform automatic screening of sickle cells in blood smears using a smartphone microscope. This framework uses two distinct, complementary deep neural networks. The first neural network enhances and standardizes the blood smear images captured by the smartphone microscope, spatially and spectrally matching the image quality of a laboratory-grade benchtop microscope. The second network acts on the output of the first image enhancement neural network and is used to perform the semantic segmentation between healthy and sickle cells within a blood smear. These segmented images are then used to rapidly determine the SCD diagnosis per patient. We blindly tested this mobile sickle cell detection method using blood smears from 96 unique patients (including 32 SCD patients) that were imaged by our smartphone microscope, and achieved ~98% accuracy, with an area-under-the-curve of 0.998. With its high accuracy, this mobile and cost-effective method has the potential to be used as a screening tool for SCD and other blood cell disorders in resource-limited settings.

Highlights

Sickle cell disease (SCD) is the most common hematologic inherited disorder worldwide and a public health priority[1]
SCD is an inherited disorder caused by a point mutation in hemoglobin formation, which causes the polymerization of hemoglobin and distortion of red blood cells in the deoxygenated state
While our framework automatically performs cell classification and slide-. Using this cost-effective mobile microscope, we performed level SCD diagnosis, any manual follow-up by a trained expert slide-level automated diagnosis of SCD by rapidly classifying requires digital images that can be accurately interpreted. This is thousands of red blood cells within a large field-of-view using a an important need satisfied by our image enhancement neural deep learning-based framework that takes

Summary

INTRODUCTION

Sickle cell disease (SCD) is the most common hematologic inherited disorder worldwide and a public health priority[1]. Using this cost-effective mobile microscope, we performed level SCD diagnosis, any manual follow-up by a trained expert slide-level automated diagnosis of SCD by rapidly classifying requires digital images that can be accurately interpreted This is thousands of red blood cells within a large field-of-view using a an important need satisfied by our image enhancement neural deep learning-based framework that takes

METHODS

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Findings

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