Ensemble deep learning model for accurate assessment of renal fibrosis in chronic kidney disease using two-dimensional shear wave elastography images.

BACKGROUND: The accuracy of shear wave elastography for non-invasive assessment of renal fibrosis (RF) in chronic kidney disease (CKD) needs further improvement. We developed a tool using an ensemble deep learning model (EDLM) that can accurately assess RF in CKD patients based solely on two-dimensional shear wave elastography (2D-SWE) images. METHODS: Retrospective data were collected from CKD patients between April 2019 and October 2024, along with renal 2D-SWE images obtained before biopsy. Pathological evaluation was the reference standard of RF. All patients were randomly divided into training, validation, and test sets in a 7:1:2 ratio. An EDLM integrating three convolutional neural networks (ResNet18, DenseNet121, and EfficientNet-b7) through a voting strategy at the output level was developed and validated using 2D-SWE images. The diagnostic performance of the EDLM was compared with that of radiologists. RESULTS: A total of 286 CKD patients (mean age ± standard deviation: 41.86 ± 14.94, males: 162) and 858 2D-SWE images (mild RF: 405, moderate-severe RF: 453) were included. In the test set, EDLM achieved an accuracy of 93.0% (95% CI: 88.1, 95.9), negative predictive value of 89.6% (95% CI: 81.5, 94.5), positive predictive value of 96.4% (95% CI: 90.0, 98.8), specificity of 96.3% (95% CI: 89.7, 98.7), and sensitivity of 90.0% (95% CI: 82.1, 94.7). The area under the receiver operating characteristic curves of the EDLM was 0.989, surpassing experienced radiologist by 0.186 (P < 0.001) and less experienced radiologist by 0.279 (P < 0.001). CONCLUSION: EDLM based on 2D-SWE images significantly improved the diagnostic performance of RF in CKD. The EDLM was expected to be a potential tool for accurately non-invasive assessment of RF in CKD.