Early diagnosis of sepsis-associated AKI: based on destruction-replenishment contrast-enhanced ultrasonography.

Objective: Establish a deep learning ultrasound radiomics model based on destruction-replenishment contrast-enhanced ultrasound (DR-CEUS) for the early prediction of acute kidney injury (SA-AKI).

Method: This paper proposes a deep learning ultrasound radiomics model (DLUR). Deep learning models were separately established using ResNet18, ResNet50, ResNext18, and ResNext50 networks. Based on the features extracted from the fully connected layers of the optimal model, a deep learning ultrasound radiomics model (DLUR) was established using three classification models (built with 3 classifiers). The predictive performance of the best DLUR model was compared with the visual assessments of two groups of ultrasound physicians with varying levels of experience. The performance of each model and the ultrasound physicians was evaluated by assessing the receiver operating characteristic (ROC) curves. The area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were subsequently calculated.

Results: Compared to the ResNet18 model, the DLUR model based on logistic regression (DLUR-LR) demonstrated the best predictive performance, showing a Net Reclassification Improvement (NRI) value of 0.210 (p < 0.05). The Integrated Discrimination Improvement (IDI) value for the corresponding stage was 0.169 (p < 0.05). Additionally, the performance of the DLUR-LR model also surpassed that of senior ultrasound physicians (AUC, 0.921 vs. 0.829, p < 0.05).

Conclusion: By combining deep learning and ultrasound radiomics, a deep learning ultrasound radiomics model with outstanding predictive efficiency and robustness has demonstrated excellent capability in the early prediction of acute kidney injury (SA-AKI).