Yu Zexing, Shi Xue, Song Yang, Li Xin, Li Ling, Ge Huiyu
Department of Ultrasound Medicine, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Front Med (Lausanne). 2025 Mar 25;12:1563153. doi: 10.3389/fmed.2025.1563153. eCollection 2025.
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).
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.
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 ( < 0.05). The Integrated Discrimination Improvement (IDI) value for the corresponding stage was 0.169 ( < 0.05). Additionally, the performance of the DLUR-LR model also surpassed that of senior ultrasound physicians (AUC, 0.921 vs. 0.829, < 0.05).
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).
建立基于破坏-再灌注超声造影（DR-CEUS）的深度学习超声影像组学模型，用于急性肾损伤（SA-AKI）的早期预测。
本文提出一种深度学习超声影像组学模型（DLUR）。分别使用ResNet18、ResNet50、ResNext18和ResNext50网络建立深度学习模型。基于最优模型全连接层提取的特征，使用三种分类模型（由3个分类器构建）建立深度学习超声影像组学模型（DLUR）。将最佳DLUR模型的预测性能与两组经验水平不同的超声医师的视觉评估进行比较。通过评估受试者工作特征（ROC）曲线来评价每个模型和超声医师的性能。随后计算曲线下面积（AUC）、敏感性、特异性、阳性预测值（PPV）、阴性预测值（NPV）和准确性。
与ResNet18模型相比，基于逻辑回归的DLUR模型（DLUR-LR）表现出最佳的预测性能，净重新分类改善（NRI）值为0.210（<0.05）。相应阶段的综合判别改善（IDI）值为0.169（<0.05）。此外，DLUR-LR模型的性能也超过了资深超声医师（AUC，0.921对0.829，<0.05）。
通过将深度学习与超声影像组学相结合，一种具有出色预测效率和稳健性的深度学习超声影像组学模型在急性肾损伤（SA-AKI）的早期预测中表现出了卓越的能力。
