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

NPJ Digital Medicine
Kevin de HaanAydogan Ozcan

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 ...Continue Reading

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Citations

Sep 2, 2020·Computers in Biology and Medicine·Tae Keun YooIk Hee Ryu
Jan 26, 2021·International Journal of Nanomedicine·Sandeep KumarKi-Hyun Kim
Feb 16, 2021·Analytical and Bioanalytical Chemistry·Soumyabrata BanikNirmal Mazumder

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Software Mentioned

Windows
qquad
MATLAB
mathop
Python
TensorFlow

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