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 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.

Consensus-based technical recommendations for clinical translation of renal diffusion-weighted MRI

“…The consensus support for manual ROI placement is also interesting given the recent trend for machine learning (ML) and artificial intelligence (AI) in the medicine. Some efforts were made recently to adopt these techniques to renal DWI, especially in the detection of early acute renal allograft rejection [90][91][92][93]. However, great care is needed when trying to translate these approaches into the clinical arena, particularly in terms of clinical validation and measured patient-centric outcomes.…”

Role of Artificial Intelligence in Kidney Disease

“…Abdeltawab H et al also developed a deep-learning based CAD system based on the fusion of both imaging markers and clinical biomarkers for the early detection of acute renal transplant rejection. 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 [91].…”

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature

“…Though RT is a better option over dialysis, the recipient's kidney is always at a risk of rejection, and hence early identification of such complications is necessary. Abdeltawab et al [52] came up with a non-invasive method for the timely diagnosis of acute RT rejection. The authors developed a novel deep-learning-based computer-aided diagnostic system drawn upon both imaging and clinical biomarkers.…”