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

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...

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Autores principales:	Kevin de Haan, Hatice Ceylan Koydemir, Yair Rivenson, Derek Tseng, Elizabeth Van Dyne, Lissette Bakic, Doruk Karinca, Kyle Liang, Megha Ilango, Esin Gumustekin, Aydogan Ozcan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/e595bac286b64f02bb247b171d271337
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spelling	oai:doaj.org-article:e595bac286b64f02bb247b171d2713372021-12-02T15:45:21ZAutomated screening of sickle cells using a smartphone-based microscope and deep learning10.1038/s41746-020-0282-y2398-6352https://doaj.org/article/e595bac286b64f02bb247b171d2713372020-05-01T00:00:00Zhttps://doi.org/10.1038/s41746-020-0282-yhttps://doaj.org/toc/2398-6352Abstract 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.Kevin de HaanHatice Ceylan KoydemirYair RivensonDerek TsengElizabeth Van DyneLissette BakicDoruk KarincaKyle LiangMegha IlangoEsin GumustekinAydogan OzcanNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Kevin de Haan Hatice Ceylan Koydemir Yair Rivenson Derek Tseng Elizabeth Van Dyne Lissette Bakic Doruk Karinca Kyle Liang Megha Ilango Esin Gumustekin Aydogan Ozcan Automated screening of sickle cells using a smartphone-based microscope and deep learning
description	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.
format	article
author	Kevin de Haan Hatice Ceylan Koydemir Yair Rivenson Derek Tseng Elizabeth Van Dyne Lissette Bakic Doruk Karinca Kyle Liang Megha Ilango Esin Gumustekin Aydogan Ozcan
author_facet	Kevin de Haan Hatice Ceylan Koydemir Yair Rivenson Derek Tseng Elizabeth Van Dyne Lissette Bakic Doruk Karinca Kyle Liang Megha Ilango Esin Gumustekin Aydogan Ozcan
author_sort	Kevin de Haan
title	Automated screening of sickle cells using a smartphone-based microscope and deep learning
title_short	Automated screening of sickle cells using a smartphone-based microscope and deep learning
title_full	Automated screening of sickle cells using a smartphone-based microscope and deep learning
title_fullStr	Automated screening of sickle cells using a smartphone-based microscope and deep learning
title_full_unstemmed	Automated screening of sickle cells using a smartphone-based microscope and deep learning
title_sort	automated screening of sickle cells using a smartphone-based microscope and deep learning
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e595bac286b64f02bb247b171d271337
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Automated screening of sickle cells using a smartphone-based microscope and deep learning

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