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

Journal: Scientific reports
PMID: 30976081

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.

Authors

  • Hisham Abdeltawab
    Bioengineering Department, University of Louisville, Louisville, KY, USA.
  • Mohamed Shehata
    Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY, United States.
  • Ahmed Shalaby
    2 Department of Bioengineering, University of Louisville, Louisville, KY, USA.
  • Fahmi Khalifa
    Bioengineering Department, University of Louisville, Louisville, KY, USA.
  • Ali Mahmoud
    Department of Bioengineering, University of Louisville, USA.
  • Mohamed Abou El-Ghar
    Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura, Egypt.
  • Amy C Dwyer
    Kidney Disease Program, University of Louisville, Louisville, KY, USA.
  • Mohammed Ghazal
    3 Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, United Arab Emirates.
  • Hassan Hajjdiab
    Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi, UAE.
  • Robert Keynton
    Department of Bioengineering, University of Louisville, USA.
  • Ayman El-Baz
    Bioengineering Department, The University of Louisville, Louisville, KY, USA.

Keywords

External Resources

Popular Topics
Recent Journals