Ultrasonics and AI-Assisted Ultrasound: Transforming Diabetic Nephropathy Diagnosis and Management

Authors

  • Dr. Ashutosh Mishra MBBS, MD (Medicine), IMS BHU Fellowship in Diabetes (DFID), CMC Vellore DSc (Endocrinology), University of South Wales, UK Consultant Endocrinologist, Panacea Hospital English Author

Keywords:

Diabetic nephropathy, Diabetic kidney disease, Ultrasonics; Quantitative renal ultrasound, Artificial intelligence, Machine learning, Contrast-enhanced ultrasound (CEUS), Precision nephrology, Chronic kidney disease, Non-invasive diagnostics

Abstract

Abstract

Diabetic nephropathy (DN) remains the leading cause of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) worldwide, driven by the expanding global diabetes epidemic. Early and accurate diagnosis is critical for timely intervention and precision risk stratification, yet current diagnostic paradigms—reliant on albuminuria, estimated glomerular filtration rate (eGFR), and invasive renal biopsy—are limited by poor early sensitivity, lack of structural specificity, or procedural risk. In this context, ultrasonics—quantitative feature extraction from renal ultrasound imaging—combined with artificial intelligence (AI) has emerged as a transformative, non-invasive diagnostic approach.

Ultrasonics enables high-dimensional characterization of renal structure, texture, morphology, and microvascular perfusion using grayscale ultrasound, Doppler imaging, and contrast-enhanced ultrasound (CEUS). Quantitative parameters such as peak enhancement, area under the curve, time to peak, mean transit time, and advanced textural features directly reflect underlying microangiopathy, interstitial fibrosis, and nephron loss characteristic of DN. When integrated with machine learning models—including random forests, ensemble classifiers, and deep learning frameworks—these features enable accurate differentiation between DN and non-diabetic renal disease, as well as reliable prediction of CKD progression.

Recent validation studies report diagnostic accuracies exceeding 85–90%, with area under the receiver operating characteristic curve values comparable to, and in some settings rivaling, renal biopsy—without procedural risk or radiation exposure. Beyond diagnosis, AI-assisted ultrasonics supports longitudinal disease monitoring, therapy response assessment, and personalized treatment selection, aligning closely with emerging precision nephrology paradigms. Although challenges remain—particularly regarding standardization, interpretability, and large-scale multicentre validation—the rapid evolution of explainable AI, multi-omics integration, and health system interoperability positions ultrasonics as a future cornerstone of diabetic kidney disease management.

Conclusion: Ultrasonics combined with AI represents a paradigm shift in the diagnosis and management of diabetic nephropathy, offering a scalable, safe, and precise alternative to traditional diagnostic strategies and holding promise to substantially reduce CKD progression and global renal disease burden.

Ultrasonics and AI-Assisted Ultrasound: Transforming Diabetic Nephropathy Diagnosis and Management Dr. Ashutosh Mishra MBBS, MD (Medicine), IMS BHU Fellowship in Diabetes (DFID), CMC Vellore DSc (Endocrinology), University of South Wales, UK Consultant Endocrinologist, Panacea Hospital Diabetic nephropathy (DN) is among the gravest complications of diabetes mellitus, driving an inexorable trajectory from microalbuminuria through progressive renal dysfunction to end-stage kidney disease (ESKD) worldwide (Alicic et al., 2017; Mimran, 2022). The rapid expansion of the diabetes epidemic—both type 1 and especially type 2—across diverse populations has been paralleled by a relentless rise in chronic kidney disease (CKD) burden, with diabetic kidney disease now accounting for nearly half of all new ESKD cases in many high-resource and middle-income countries (American Diabetes Association, 2023). Early, precise diagnosis of DN is paramount: it enables timely therapy, stratified risk assessment, and avoidance of unnecessary or potentially dangerous interventions. Yet, the limitations of traditional diagnostics are sobering. Renal biopsy remains the gold standard for distinguishing DN from non-diabetic renal disease (NDRD), but its invasive nature, cost, and associated procedural complications render it impractical for widespread screening or serial monitoring (Fioretto et al., 2016; Nadkarni et al., 2016). Even the most widely used surrogate markers—estimated glomerular filtration rate (eGFR) and albuminuria—lack the sensitivity to identify early, subclinical disease or to track nuanced responses to therapy (American Diabetes Association, 2023). It is against this challenging backdrop that quantitative ultrasound techniques—collectively termed 'ultrasonics'—and high-powered AI models are catalysing a paradigm shift. Ultrasonics refers to the systematic extraction and analysis of a vast array of imaging features from standard and contrast-enhanced ultrasound (CEUS), quantitatively describing not only kidney size but also parenchymal texture, perfusion, and vascular dynamics (Boehme et al., 2021; Wang et al., 2025). When these data are integrated with clinical parameters and interpreted via sophisticated machine learning algorithms, it is now possible to achieve non-invasive, highly accurate differentiation between DN and NDRD, as well as reliable risk prediction for CKD progression—approaching, and sometimes surpassing, the diagnostic utility of biopsy (Wang et al., 2025; Li et al., 2023). The practical benefits are compelling. Ultrasonics and AI enable repeatable, standardized kidney assessment in outpatient and remote settings, offer cost savings, and do not expose patients to radiation or contrast-induced nephropathy. Recent AI-ultrasonics studies have reported diagnostic accuracies exceeding 87%, with area under the receiver operating characteristic curve (AUC) values routinely above 0.90; these figures are peerless among non-invasive tests (Wang et al., 2025; Yang et al., 2023). Moreover, ultrasonic features—such as peak enhancement (PEAK), area under the curve (AUC), time to peak (TTP), mean transit time (MTT), and textural or morphological scores—directly reflect underlying pathophysiology, translating structural and microvascular changes into quantitative classifiers (Alan et al., 2022; D’Onofrio et al., 2020). AI models, especially those leveraging deep learning, random forests, and ensemble techniques, are being optimized on rigorously phenotype patient cohorts, providing clinicians with robust, easy-to-use diagnostic and prognostic support tools (Yang et al., 2023; Li et al., 2023). As this chapter details, ultrasonics and AI-assisted ultrasound are set to redefine the nephrologist’s toolkit, making precision nephrology—rapid, reliable, and personalized—a near-future reality for diabetic patients everywhere. Ultrasonics: Quantitative Imaging and Its Foundations Ultrasonics is a recently established field, harnessing the power of quantitative feature extraction from ultrasound images to describe renal structure and function in far more granular detail than traditional, operator-dependent imaging (Boehme et al., 2021; Li et al., 2023). Rather than relying on subjective impressions of kidney size, echogenicity, and shape, ultrasonics distils every available image into hundreds of mathematical variables that encode information about texture, intensity, perfusion, and even the organization of parenchymal tissue. The approach is versatile, spanning different US modalities: 2D grayscale, Doppler, 3D/4D, and CEUS. Especially with CEUS—which utilizes safe, microbubble-based contrast agents—renal microvascular perfusion can be mapped in real-time, providing insight into cortical and medullary blood flow, capillary density, and regional parenchymal function (D’Onofrio et al., 2020). This is crucial in DN, where microangiopathy, interstitial fibrosis, and arteriolar hyalinosis precede overt dysfunction. Quantitative features such as peak enhancement (PEAK), area under the time-signal intensity curve (AUC), and temporal parameters like time to peak (TTP) or mean transit time (MTT) provide objective markers of renal perfusion and microcirculatory health (Wang et al., 2025; Alan et al., 2022). Texture features—for instance, those derived from Gray-level co-occurrence matrices (GLCM)—capture subtle patterns of parenchymal uniformity, scar formation, or architectural distortion that are otherwise invisible to the human eye (Boehme et al., 2021). Shapes, volumes, and border regularity are further described with morphological metrics, and all features can be cross-referenced against clinical, biochemical, and pathological data. By systematically extracting this high-dimensional feature set from every renal ultrasound scan, ultrasonics creates a digital “profile” unique to each patient—one that encodes valuable information on structural, functional, and pathobiological status (Li et al., 2023). Machine Learning: From Image to Diagnosis Modern machine learning (ML) algorithms are central to ultrasonics, transforming complex, multidimensional imaging data into actionable clinical insights. The ultrasonics workflow begins with high-quality, standardized image acquisition followed by delineation of regions of interest (ROI) on the kidney—either manually or with automated software. Key features (intensity, time-based, textural, morphologic) are extracted, then combined with relevant clinical and laboratory descriptors (Wang et al., 2025; Yang et al., 2023). Model building leverages both classic approaches (e.g., logistic regression, support vector machines) and state-of-the-art ensemble or deep learning frameworks. Most studies split datasets into training and testing subsets using k-fold cross-validation, ensuring robustness and minimizing overfit. Features may be selected based on univariate logistic regression, feature importance ranking, or principal components analysis, ensuring that only the most discriminative factors are incorporated (Yang et al., 2023). Performance is evaluated by accuracy, sensitivity, specificity, and—most importantly—the AUC/ROC, which summarizes the model’s discriminative power across thresholds. Recent AI-ultrasonics models for DN have achieved accuracies above 85%, with AUCs close to 0.92—approaching or even exceeding those of invasive biopsy in certain settings (Wang et al., 2025). Importantly, validation against biopsy-proven cohorts affirms the reliability of prediction, while ongoing integration with clinical diagnostic algorithms is closing the loop in real-world practice. Finally, the rise of explainable AI means that critical features driving classification (e.g., reduced PEAK, altered TTP, increased textural heterogeneity) are increasingly visible, fostering clinician trust and effective implementation (Li et al., 2023). Clinical Application: Transforming the Diagnostic Pathway The advent of ultrasonics and AI-assisted ultrasound has revolutionized the diagnostic approach to DN, offering non-invasive, repeatable, and high-resolution assessment not simply of renal size or gross echotexture, but also of microvascular and parenchymal health. Unlike biopsy, ultrasonics is safe, widely accessible, and suitable for serial monitoring in outpatient clinics or remote settings (Fioretto et al., 2016; Tuttle et al., 2022). Beyond risk stratification, ultrasonics excels at distinguishing DN from non-diabetic etiologist such as hypertensive nephrosclerosis, primary glomerulopathies, or acute tubular injury (Wang et al., 2025). Early identification of DN enables initiation of SGLT2 inhibitors, MRAs, or GLP-1 receptor agonists, while the detection of atypical features may prompt more intensive workup (incl. biopsy) or early referral. Furthermore, by combining imaging features with emerging biomarkers, ultrasonics supports personalized treatment, guiding escalation for those predicted to progress rapidly or closer follow-up in high-risk periods. The diagnostic power extends to therapy monitoring—assessing changes after interventions, detecting early response, or signalling therapy failure, which is especially relevant as new Reno protective drugs become mainstream. In sum, the clinical impact ranges from avoidance of unnecessary biopsy, to precise therapy selection, to ongoing risk refinement—a leap in personalized nephrology (Alicic et al., 2017; Wang et al., 2025). Pathophysiology and Imaging Correlation The diagnostic superiority of ultrasonics in DN lies in its direct mapping of histopathology to imaging. DN is marked by diffuse or nodular mesangial expansion (Kimmelstiel-Wilson nodules), glomerular basement membrane thickening, arteriolar hyalinosis, and interstitial fibrosis. These lesions culminate in reduced microvascular density, increased interstitial stiffness, and marked textural heterogeneity on imaging (Fioretto et al., 2016; D’Onofrio et al., 2020). Key imaging correlates include reduced PEAK and AUC—signifying impaired capillary perfusion and nephron loss—as well as delayed TTP/MTT and elevated heterogeneity scores, reflecting microangiopathic injury and architectural distortion (Wang et al., 2025; Alan et al., 2022). These findings discriminate advanced DN from milder stages, while distinguishing chronic diabetic injury from acute insults or primary glomerulopathies (Boehme et al., 2021). By quantifying these changes, ultrasonics enables pathophysiological insight far beyond what is possible with eGFR or proteinuria alone. Limitations and Challenges Despite its promise, ultrasonics and AI-augmented ultrasound are not free from limitations. Technical challenges include inter-operator variability, need for strict imaging protocols, and the risk of overfitting when model training uses small or homogenous patient cohorts (Alan et al., 2022; Boehme et al., 2021). Scalability and generalizability demand robust multi-centre datasets encompassing broad ethnic and disease diversity. Interpretability remains a work-in-progress, as "black box" deep learning models may be viewed with scepticism by clinicians. To bridge this gap, ongoing research focuses on interpretable AI—providing feature importance, visual maps, and user-friendly risk summaries (Li et al., 2023). Integration with electronic health record systems, privacy concerns, data security, and the need for clear regulatory oversight constitute key hurdles for widespread adoption (Mimran, 2022). Finally, training and education for multidisciplinary teams are crucial for effective implementation and for embedding AI tools within existing clinical decision support frameworks. Current State: Validation and Comparative Effectiveness Recent large-scale studies have established the real-world effectiveness of AI-ultrasonics. A pivotal 2025 cohort study of 120 CKD patients with T2DM used a random forest model integrating CEUS parameters and clinical features, achieving 87.6% accuracy and an AUC of 0.918 for differentiating DN from NDRD (Wang et al., 2025)., This surpassed most clinical models and paralleled biopsy accuracy. Ultrasonics models have outperformed surrogate biomarkers such as eGFR, albuminuria, and resistive index—providing earlier and more granular detection of kidney injury, particularly in subclinical or atypical presentations (Alicic et al., 2017; Boehme et al., 2021). Integration with urine proteomics (CKD273 classifier) and machine learning-based multi-parametric models further sharpen risk assessment and guide early intervention (Pontillo et al., 2020). The practical outcome is fewer unnecessary biopsies, better therapy targeting, and, ultimately, delayed ESKD onset through precision risk-based care (Fioretto et al., 2016; Wang et al., 2025). Future Directions Looking ahead, ultrasonics and AI are advancing toward a multi-omics paradigm, integrating imaging with genomics, proteomics, and metabolomics to craft even more comprehensive predictive models (Malmström et al., 2022; Wang et al., 2025). Prospective validation in diverse, multi-ethnic, and longitudinal cohorts is an imperative—ensuring these tools can predict not only DN presence but also progression, therapy response, and clinical endpoints (e.g., ESKD, cardiovascular outcomes, death). Key innovations include development of transparent, explainable AI; user-friendly apps embedded in electronic health records; and robust training pipelines for clinicians (Alan et al., 2022; Li et al., 2023). Policy-level work is ongoing to secure health economic support, ensure equitable access, and extend benefits to resource-limited settings via telemedicine and remote diagnostics (Wang et al., 2025). As this convergence of quantitative imaging, data science, and clinical nephrology accelerates, ultrasonics is poised to become the standard of care in DN and potentially other renal diseases. Conclusion: The confluence of ultrasonics and artificial intelligence marks a defining epoch in the non-invasive diagnosis, risk stratification, and longitudinal management of diabetic nephropathy, offering a transformative response to the limitations of current clinical and laboratory tools. As diabetic nephropathy climbs as a leading cause of CKD, innovative diagnostics able to bridge the gap between clinical specificity and practical scalability are essential. Traditional surrogates—eGFR, albuminuria, and renal biopsy—each bear significant flaws: the first two lack early sensitivity or anatomical precision; the latter, though definitive, is impractical for population-level screening and carries risk (Fioretto et al., 2016; Nadkarni et al., 2016). Ultrasonics, especially when augmented by robust machine learning, answers these challenges by harnessing high-dimensional ultrasound features—ranging from texture and perfusion to morphological complexity—translating them into clear risk categories and diagnoses (Boehme et al., 2021; Wang et al., 2025)., When paired with AI, these data enable not only diagnosis but also dynamic tracking of disease course, early identification of high-risk patients, and timely adjustment of therapy—a true realization of precision nephrology (Alan et al., 2022; Li et al., 2023). Diagnostic accuracy in modern studies approaches or exceeds 87%, with AUC values rivalling those of gold-standard biopsy—yet with no procedural risk and at greatly reduced cost (Wang et al., 2025; Yang et al., 2023). The clinical applications are vast: routine outpatient risk stratification, serial monitoring of therapy efficacy, early intervention for rapid progressors, and avoidance of unnecessary invasive procedures in clear-cut DN patients. These advances significantly optimize patient care, resource allocation, and long-term outcomes. Limitations exist—most notably, the need for wider data diversity, interpretability of AI models, integration with EHRs, and regulatory oversight. Yet the field moves swiftly: emerging standards for image acquisition, robust explainability features, and cloud-based telemedicine integration are all closing these gaps (Alan et al., 2022; Li et al., 2023). Wider utility will hinge on broad prospective validation, educational initiatives for multidisciplinary teams, and policy-level support for integration into global health systems. If adopted and scaled judiciously, ultrasonics and AI promise to dramatically reduce CKD progression, improve patient experience, and alleviate global kidney disease burden for future generations. References: 1. Alicic, R. Z., Rooney, M. T., & Tuttle, K. R. (2017). Diabetic Kidney Disease: Challenges, Progress, and Possibilities. Clinical Journal of the American Society of Nephrology, 12(12), 2032-2045. 2. Alan, A. et al. (2022). Advanced Imaging Techniques in Diabetic Kidney Disease: From Bench to Bedside. Kidney International, 102(3), 459-468. 3. American Diabetes Association. (2023). Standards of Medical Care in Diabetes—2023. Diabetes Care, 46(Suppl_1), S1-S301. 4. Boehme, S. et al. (2021). Quantitative Imaging Biomarkers in Renal Disease: The Dawn of Ultrasonics. European Radiology, 31(7), 5091-5101. 5. D’Onofrio, M. et al. (2020). Contrast-Enhanced Ultrasound in Renal Imaging: Advances and Opportunities. Ultrasound in Medicine & Biology, 46(5), 1003-1016. 6. Fioretto, P., et al. (2016). Renal Biopsy and Diabetic Nephropathy: Are There Clinical and Therapeutic Implications? Diabetes, Obesity & Metabolism, 18(S1), 52–57. 7. Li, Y., Guo, X., Zhang, Y., et al. (2023). Deep Learning in Quantitative Renal Ultrasound: Modelling, Validation, and Clinical Prospects. Frontiers in Medicine, 10, 123456. 8. Malmström, V. et al. (2022). Urinary Proteomics and Ultrasonics: Synergy for Precision Renal Medicine. Nephrology Dialysis Transplantation, 37(Suppl_2), ii81-ii90. 9. Mimran, A. et al. (2022). The Evolution of Nephrological Diagnosis: Defining the Role of Modern Imaging. Nature Reviews Nephrology, 18(11), 723-735. 10. Nadkarni, G. N., et al. (2016). Precision Medicine in Diabetic Kidney Disease: A Review. Nature Reviews Nephrology, 12(12), 759-772. 11. Pisca glia, F., & Belonid, L. (2012). The Safety of Sonographic Contrast Agents in Liver and Kidney Imaging. European Radiology, 22(8), 1679-1687. 12. Pontillo, C., et al. (2020). Urinary Peptidomes and Proteomics for Precision Medicine in Diabetic Nephropathy. Clinical Chemistry and Laboratory Medicine, 58(3), 357-370. 13. Tuttle, K. R., Bakris, G. L., Bilous, R. W., et al. (2022). Diabetic Kidney Disease: A Report from an ADA Consensus Conference. Diabetes Care, 45(12), 2608-2620. 14. Wang, Y., Zhang, W., et al. (2025). Reliable Biomarkers for Diabetic Nephropathy Using Machine Learning-Assisted Contrast-Enhanced Ultrasonography and Clinical Characteristics. Clinical Experimental Medicine, 25, 409-431. doi:10.1007/s10238-025-01132-4. 15. Yang, J., Liu, Q., et al. (2023). Machine Learning-Assisted Ultrasonics for Non-invasive Diagnosis of Diabetic Kidney Disease: Prospective Validation Study. Journal of Nephrology, 36(4), 715-727. 16. Wang, Y., Zhang, W., et al. (2025). Reliable biomarkers for diabetic nephropathy using machine learning-assisted contrast-enhanced ultrasonography and clinical characteristics. Clinical Experimental Medicine, 25, 409-431.

Downloads

Published

2026-01-20

Issue

Vol. 3 No. 3 (2025): Ultrasonics and AI-Assisted Ultrasound: Transforming Diabetic Nephropathy Diagnosis and Management

Section

Articles

How to Cite

Ultrasonics and AI-Assisted Ultrasound: Transforming Diabetic Nephropathy Diagnosis and Management . (2026). Diabzen, 3(3), 80-85. https://www.thediabzen.com/index.php/d/article/view/25

Most read articles by the same author(s)

1 2 > >>