A multimodal computer‐aided diagnostic system for precise identification of renal allograft rejection: Preliminary results

Abstract

PurposeEarly assessment of renal allograft function post‐transplantation is crucial to minimize and control allograft rejection. Biopsy — the gold standard — is used only as a last resort due to its invasiveness, high cost, adverse events (e.g., bleeding, infection, etc.), and the time for reporting. To overcome these limitations, a renal computer‐assisted diagnostic (Renal‐CAD) system was developed to assess kidney transplant function.MethodsThe developed Renal‐CAD system integrates data collected from two image‐based sources and two clinical‐based sources to assess renal transplant function. The imaging sources were the apparent diffusion coefficients (ADCs) extracted from 47 diffusion‐weighted magnetic resonance imaging (DW‐MRI) scans at 11 different b‐values (b0, b50, b100, ..., b1000 s/mm), and the transverse relaxation rate (R2*) extracted from 30 blood oxygen level‐dependent MRI (BOLD‐MRI) scans at 5 different echo times (TEs = 2, 7, 12, 17, and 22 ms). Serum creatinine (SCr) and creatinine clearance (CrCl) were the clinical sources for kidney function evaluation. The Renal‐CAD system initially performed kidney segmentation using the level‐set method, followed by estimation of the ADCs from DW‐MRIs and the R2* from BOLD‐MRIs. ADCs and R2* estimates from 30 subjects that have both types of scans were integrated with their associated SCr and CrCl. The integrated biomarkers were then used as our discriminatory features to train and test a deep learning‐based classifier, namely stacked autoencoders (SAEs) to differentiate non‐rejection (NR) from acute rejection (AR) renal transplants.ResultsUsing a leave‐one‐subject‐out cross‐validation approach along with SAEs, the Renal‐CAD system demonstrated 93.3% accuracy, 90.0% sensitivity, and 95.0% specificity in differentiating AR from NR. Robustness of the Renal‐CAD system was also confirmed by the area under the curve value of 0.92. Using a stratified tenfold cross‐validation approach, the Renal‐CAD system demonstrated its reproducibility and robustness by a diagnostic accuracy of 86.7%, sensitivity of 80.0%, specificity of 90.0%, and AUC of 0.88.ConclusionThe obtained results demonstrate the feasibility and efficacy of accurate, noninvasive identification of AR at an early stage using the Renal‐CAD system.

Artificial intelligence and machine learning for medical imaging: A technology review

“…For instance, instead of using generic loss functions from computer vision tasks, like the mean squared error, one could use loss functions that better target the specificities of our medical problem, such as including mutual information for the conversion of different image modalities [90,193] or dose-volume histograms for radiotherapy dose predictions [212]. Regarding the injection of domain-specific knowledge as input to the models, some examples include the addition of electronic health records and clinical data, like text and laboratory results, to the image data [213][214][215], or having first-order prior or approximations of the expected output [175,[216][217][218][219] Integrating domain-specific knowledge cannot only serve to improve the performances of state-of-the-art AI models, but also to increase the interpretability of the results, which is one of the well-acknowledged limitations of the current ML/DL methods [220][221][222][223]. This is the idea behind the so-called Expert Augmented Machine Learning (EAML), whose goal is to develop algorithms capable of extracting human knowledge from a panel of experts and use it to establish constraints for the model's prediction [224].…”

Artificial intelligence and kidney transplantation

“…Three additional papers from the same group examined the utility of computer-aided diagnostic (CAD) systems for the diagnosis of acute rejection[ 7 - 9 ]. In their first study[ 7 ], the authors used deep-learning algorithms, namely, ‘stacked non-negative constrained auto-encoders’, for the prediction of acute rejection.…”

“…In a third study[ 9 ], they again assessed the utility of the CAD system for the diagnosis of acute rejection using DW-MRI and blood oxygen level-dependent MRI as the image-based sources. The authors also used laboratory data consisting of creatinine and creatinine clearance.…”

“…The authors also used laboratory data consisting of creatinine and creatinine clearance. In addition, they utilized a deep learning-based classifier, namely, ‘stacked autoencoders’, to differentiate non-rejection from acute rejection in renal transplants[ 9 ]. The overall accuracy of the CAD system in detection of acute rejection was around 90%[ 9 ].…”

A Deep Learning-Based Cad System For Renal Allograft Assessment: Diffusion, Bold, And Clinical Biomarkers

“…This vector was then used as the BOLD-MRI markers (B mrks ) to identify renal allograft status. At 2ms, the BOLD-MRI is at its baseline ET; therefore, the pixel-wise T2* and R2* may be calculated as [18,32]:…”