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Objective <p>To utilize machine learning techniques to screen contrast-enhanced ultrasound (CEUS) parameters and clinical characteristics, aiming to differentiate diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in patients with diabetic kidney injury.</p> Methods <p>Data from 120 diabetic patients (240 kidneys) with chronic kidney disease (CKD) were analyzed. The data included basic clinical features for each kidney and renal vascular data obtained through CEUS. Statistical analysis, tenfold cross-validation and random forest method were used for data processing. Receiver operating characteristic (ROC) curves were employed to depict the diagnostic performance of the indicators.</p> Results <p>The random forest model integrating CEUS parameters and clinical characteristics achieved an average classification accuracy of 87.6% in differentiating kidney injury types. ROC curve analysis showed an AUC of 0.918.</p> Conclusion <p>Through machine learning, CEUS quantitative parameters and clinical features of the screened model can be used as important noninvasive biomarkers to identify kidney injury in T2DM patients with DN. Ai-assisted CEUS and specific clinical features are a fast and reliable tool for DN screening.</p>

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Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月31日

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  • Lin Lin,
  • Lin Yan,
  • Nan Li,
  • Yiru Wang,
  • Yukun Luo

摘要

Objective

To utilize machine learning techniques to screen contrast-enhanced ultrasound (CEUS) parameters and clinical characteristics, aiming to differentiate diabetic nephropathy (DN) from non-diabetic renal disease (NDRD) in patients with diabetic kidney injury.

Methods

Data from 120 diabetic patients (240 kidneys) with chronic kidney disease (CKD) were analyzed. The data included basic clinical features for each kidney and renal vascular data obtained through CEUS. Statistical analysis, tenfold cross-validation and random forest method were used for data processing. Receiver operating characteristic (ROC) curves were employed to depict the diagnostic performance of the indicators.

Results

The random forest model integrating CEUS parameters and clinical characteristics achieved an average classification accuracy of 87.6% in differentiating kidney injury types. ROC curve analysis showed an AUC of 0.918.

Conclusion

Through machine learning, CEUS quantitative parameters and clinical features of the screened model can be used as important noninvasive biomarkers to identify kidney injury in T2DM patients with DN. Ai-assisted CEUS and specific clinical features are a fast and reliable tool for DN screening.