PolyU researchers invent non-invasive diagnostic device Smart-CKD for advancing clinical management of chronic kidney disease

Efficient clinical management of chronic kidney disease (CKD) is crucial for public health, as it is a progressive condition that affects 10% of the global population. Early diagnosis and active monitoring play a vital role in effectively treating and prognosing the common pathological pathway from CKD to end-stage renal disease (ESRD), which is characterised by renal fibrosis. Researchers from the Department of Health Technology and Informatics of the Hong Kong Polytechnic University (PolyU) have developed Smart-CKD (S-CKD), a computer-aided diagnostic tool. It integrates ultrasound (US) data and selected clinical variables to provide clinical insights and assesses the risk of moderate-to-severe renal fibrosis progression in CKD patients.

Early diagnosis and accurate staging of renal fibrosis significantly guide treatment strategies and prognostic assessment, enabling timely preventive measures to avoid or delay disease exacerbations. However, identifying individuals at high risk of advanced renal fibrosis with precision continues to pose a challenge in clinical practice.

By leveraging advanced health technology to tackle this challenge, Prof. Michael Tin Cheung YING, Associate Head and Professor of Department of Health Technology and Informatics at PolyU and Postdoctoral Fellow Dr Ziman CHEN have collaborated with Dr Zhongzhen SU, Vice President of The Fifth Affiliated Hospital of Sun Yat-sen University to successfully invent S-CKD. This innovative diagnostic tool aims to improve disease progress monitoring and clinical management on CKD.

Prof. Ying said, “The implementation of S-CKD holds the potential to assist healthcare practitioners in tailoring medical judgments and optimising post-treatment protocols for CKD patients. By utilising non-invasive medical imaging results and basic demographic data, this tool offers a cost-effective solution for guiding patient management, thereby contributing to notable clinical advantages.”

The research on S-CKD titled “Development and Deployment of a Novel Diagnostic Tool Based on Conventional Ultrasound for Fibrosis Assessment in Chronic Kidney Disease[LS[1] ” was published on Academic Radiology in September 2023. Research results showed S-CKD has excellent predictive accuracy and high clinical application value.

Specifically, S-CKD integrates three pivotal clinical parameters - age, ultrasonic renal length, end-diastolic flow velocity of the interlobar renal artery - which could be collected through regular clinical follow-ups. By leveraging machine learning, S-CKD integrates these data, resulting in a promising diagnostic efficiency of 80%.

Prof. Ying said, “While renal biopsy remains the gold standard for diagnosing and staging renal fibrosis, its invasive nature imposes limitations in conducting multiple observations, follow-ups, and having potential complications. Therefore, there is a pressing need to develop a non-invasive biomarker for precise monitoring and clinical management of renal fibrosis and its progression.”

Significantly, S-CKD is accessible through both an online web-based platform and an offline document-based format, making it a user-friendly auxiliary instrument for flexible clinical applications. It is a real-time, reliable, and non-invasive tool that assists medical practitioners in assessing renal fibrosis risk in CKD patients during routine clinical practices. This diagnostic approach plays a crucial role in guiding treatment decisions, improving patient prognosis, and subsequently offering advantages in clinical management, counselling, decision-making on treatment regimens, and scheduling follow-ups.

Moreover, the research team has developed a random forest (RF) model which combines 2-D shear wave elastography (SWE) data and clinical features of CKD patients for renal fibrosis evaluation. RF is a machine learning algorithm that generates a single result from multiple informative variables. The research titled “Assessment of Renal Fibrosis in Patients with Chronic Kidney Disease Using Shear Wave Elastography and Clinical Features: A Random Forest Approach” was published on Ultrasound in Medicine & Biology in July 2023.

The diagnostic field of renal fibrosis has seen remarkable progress with ultrasound elastography. However, this diagnostic modality highly depends on the operator’s experience and technical proficiency, which poses challenges for implementation, particularly in resource-limited areas. The primary aim of inventing S-CKD is to enable easy and ubiquitous applications in clinical management, even in the face of resource constraints. The input variables for S-CKD can be readily obtained from medical records and routine imaging assessment at a low cost, while the output probability can accurately and dynamically stratify patient risks. This established tool provides accurate information for patient-centered, informed decisions regarding the clinical management of CKD progression through active surveillance and non-invasive assessment.

For another ground-breaking innovation, the research team has introduced ultrasound radiomics analysis, advancing from clinical parameters to ultrasound images analysis. Radiomics, an emerging technology, enables the high-throughput extraction of numerous imaging features from medical imaging data that are invisible to the naked eye. This subsequently builds a radiomics model to achieve non-invasive assessment of renal fibrosis.

In particular, the team has applied radiomics technology to extract features from ultrasound images which may be incomprehensible and invisible to the naked eye. This is achieved by using data-characteristics algorithms based on machine learning. Additionally, this radiomics approach combines ultrasound images with clinical variables to construct a diagnostic model, which is subsequently visualised as a web-page calculator. The research titled “Ultrasound-based Radiomics Analysis in the Assessment of Renal Fibrosis in Patients with Chronic Kidney Disease,” was published on Abdominal Radiology in August 2023.

Current models still rely on medical practitioners to manually measure ultrasound parameters or outline target areas on ultrasound images, leading to inherent subjectivity. Future research will focus on utilising artificial intelligence technologies such as deep learning to develop a fully automated diagnostic model.

Prof. Ying said “We plan to conduct further prospective clinical research on S-CKD, utilising PolyU’s innovative medical technology and facilities, in collaboration with medical institutions in the Greater Bay Area, including Hong Kong partners. Together, we collaborate on research to enhance the impact of S-CKD on clinical management, ultimately improving CKD patients’ prognosis.”

The Health Bureau of Hong Kong has introduced the Chronic Disease Co-Care Pilot Scheme, promoting primary healthcare with a shift from curative treatment to disease prevention. Prof. Ying expressed his hope that the research team's innovative technology could contribute to the plan and advance healthcare development in Hong Kong and the world.