A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction

Abstract This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinica...

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Autores principales:	Hisham Abdeltawab, Mohamed Shehata, Ahmed Shalaby, Fahmi Khalifa, Ali Mahmoud, Mohamed Abou El-Ghar, Amy C. Dwyer, Mohammed Ghazal, Hassan Hajjdiab, Robert Keynton, Ayman El-Baz
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5122771f8eff417ead0e170207bac3a5
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spelling	oai:doaj.org-article:5122771f8eff417ead0e170207bac3a52021-12-02T15:09:38ZA Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction10.1038/s41598-019-42431-32045-2322https://doaj.org/article/5122771f8eff417ead0e170207bac3a52019-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-42431-3https://doaj.org/toc/2045-2322Abstract This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.Hisham AbdeltawabMohamed ShehataAhmed ShalabyFahmi KhalifaAli MahmoudMohamed Abou El-GharAmy C. DwyerMohammed GhazalHassan HajjdiabRobert KeyntonAyman El-BazNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-11 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hisham Abdeltawab Mohamed Shehata Ahmed Shalaby Fahmi Khalifa Ali Mahmoud Mohamed Abou El-Ghar Amy C. Dwyer Mohammed Ghazal Hassan Hajjdiab Robert Keynton Ayman El-Baz A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction
description	Abstract This paper introduces a deep-learning based computer-aided diagnostic (CAD) system for the early detection of acute renal transplant rejection. For noninvasive detection of kidney rejection at an early stage, the proposed CAD system is based on the fusion of both imaging markers and clinical biomarkers. The former are derived from diffusion-weighted magnetic resonance imaging (DW-MRI) by estimating the apparent diffusion coefficients (ADC) representing the perfusion of the blood and the diffusion of the water inside the transplanted kidney. The clinical biomarkers, namely: creatinine clearance (CrCl) and serum plasma creatinine (SPCr), are integrated into the proposed CAD system as kidney functionality indexes to enhance its diagnostic performance. The ADC maps are estimated for a user-defined region of interest (ROI) that encompasses the whole kidney. The estimated ADCs are fused with the clinical biomarkers and the fused data is then used as an input to train and test a convolutional neural network (CNN) based classifier. The CAD system is tested on DW-MRI scans collected from 56 subjects from geographically diverse populations and different scanner types/image collection protocols. The overall accuracy of the proposed system is 92.9% with 93.3% sensitivity and 92.3% specificity in distinguishing non-rejected kidney transplants from rejected ones. These results demonstrate the potential of the proposed system for a reliable non-invasive diagnosis of renal transplant status for any DW-MRI scans, regardless of the geographical differences and/or imaging protocol.
format	article
author	Hisham Abdeltawab Mohamed Shehata Ahmed Shalaby Fahmi Khalifa Ali Mahmoud Mohamed Abou El-Ghar Amy C. Dwyer Mohammed Ghazal Hassan Hajjdiab Robert Keynton Ayman El-Baz
author_facet	Hisham Abdeltawab Mohamed Shehata Ahmed Shalaby Fahmi Khalifa Ali Mahmoud Mohamed Abou El-Ghar Amy C. Dwyer Mohammed Ghazal Hassan Hajjdiab Robert Keynton Ayman El-Baz
author_sort	Hisham Abdeltawab
title	A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction
title_short	A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction
title_full	A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction
title_fullStr	A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction
title_full_unstemmed	A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction
title_sort	novel cnn-based cad system for early assessment of transplanted kidney dysfunction
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/5122771f8eff417ead0e170207bac3a5
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A Novel CNN-Based CAD System for Early Assessment of Transplanted Kidney Dysfunction

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