Abstract：
OBJECTIVE: This study aimed to explore the value of ultrasound (US) images in chronic kidney disease (CKD) screening by constructing a CKD screening model based on grey-scale US images.
METHODS: According to the CKD diagnostic criteria, 1049 patients from Tongde Hospital of Zhejiang Province were retrospectively enrolled in the study. A total of 4365 renal US images were collected from these patients. Convolutional neural networks were used for feature extractions and a screening model was constructed by fusing ResNet34 and texture features to identify CKD and its stage. A comparative analysis was performed to compare the diagnosis results of the model with physicians.
RESULTS: When diagnosing CKD or non-CKD, the receiver operating characteristic curve (AUC) of our model was 0.918 and that of the senior physician group was 0.869 (p < .05). For the diagnosis of CKD stage, the AUC of our model for CKD G1-G3 was 0.781, 0.880, and 0.905, respectively, while the AUC of the senior physician group for CKD G1-G3 was 0.506, 0.586, and 0.796, respectively; all differences were statistically significant (p < .05). The diagnostic efficiency of our model for CKD G4 and G5 reached the level of the senior physicians group. Specifically, the AUC of our model for CKD G4-G5 was 0.867 and 0.931, respectively, while the AUC of the senior physician group for CKD G4-G5 was 0.838 and 0.963, respectively (all p > .05).
CONCLUSIONS: Our deep learning radiomics model is more effective than senior physicians in the diagnosis of early CKD.
METHODS: According to the CKD diagnostic criteria, 1049 patients from Tongde Hospital of Zhejiang Province were retrospectively enrolled in the study. A total of 4365 renal US images were collected from these patients. Convolutional neural networks were used for feature extractions and a screening model was constructed by fusing ResNet34 and texture features to identify CKD and its stage. A comparative analysis was performed to compare the diagnosis results of the model with physicians.
RESULTS: When diagnosing CKD or non-CKD, the receiver operating characteristic curve (AUC) of our model was 0.918 and that of the senior physician group was 0.869 (p < .05). For the diagnosis of CKD stage, the AUC of our model for CKD G1-G3 was 0.781, 0.880, and 0.905, respectively, while the AUC of the senior physician group for CKD G1-G3 was 0.506, 0.586, and 0.796, respectively; all differences were statistically significant (p < .05). The diagnostic efficiency of our model for CKD G4 and G5 reached the level of the senior physicians group. Specifically, the AUC of our model for CKD G4-G5 was 0.867 and 0.931, respectively, while the AUC of the senior physician group for CKD G4-G5 was 0.838 and 0.963, respectively (all p > .05).
CONCLUSIONS: Our deep learning radiomics model is more effective than senior physicians in the diagnosis of early CKD.
摘要：
目的：本研究通过构建基于灰度超声图像的慢性肾脏病（CKD）筛查模型，探讨超声图像在CKD筛查中的应用价值。
方法：根据CKD诊断标准，回顾性研究浙江省同德医院1049例患者。从这些患者中收集了总共4365张肾脏US图像。使用卷积神经网络进行特征提取，并通过融合ResNet34和纹理特征来构建筛选模型，以识别CKD及其阶段。进行了比较分析，以将模型的诊断结果与医师进行比较。
结果：诊断CKD或非CKD时，我们模型的受试者工作特征曲线（AUC）为0.918，高级医师组为0.869（p<.05）。对于CKD分期的诊断，我们的CKDG1-G3模型的AUC分别为0.781、0.880和0.905，而高级医师组CKDG1-G3的AUC分别为0.506、0.586和0.796；所有差异均有统计学意义（p<0.05）。我们的模型对CKDG4和G5的诊断效率达到了高级医师组的水平。具体来说,我们的CKDG4-G5模型的AUC分别为0.867和0.931，而高级医师组CKDG4-G5的AUC分别为0.838和0.963（均p>.05）。
结论：我们的深度学习影像组学模型在诊断早期CKD方面比高级医师更有效。
方法：根据CKD诊断标准，回顾性研究浙江省同德医院1049例患者。从这些患者中收集了总共4365张肾脏US图像。使用卷积神经网络进行特征提取，并通过融合ResNet34和纹理特征来构建筛选模型，以识别CKD及其阶段。进行了比较分析，以将模型的诊断结果与医师进行比较。
结果：诊断CKD或非CKD时，我们模型的受试者工作特征曲线（AUC）为0.918，高级医师组为0.869（p<.05）。对于CKD分期的诊断，我们的CKDG1-G3模型的AUC分别为0.781、0.880和0.905，而高级医师组CKDG1-G3的AUC分别为0.506、0.586和0.796；所有差异均有统计学意义（p<0.05）。我们的模型对CKDG4和G5的诊断效率达到了高级医师组的水平。具体来说,我们的CKDG4-G5模型的AUC分别为0.867和0.931，而高级医师组CKDG4-G5的AUC分别为0.838和0.963（均p>.05）。
结论：我们的深度学习影像组学模型在诊断早期CKD方面比高级医师更有效。
