Combined application of deep learning and conventional computer vision for kidney ultrasound image classification in chronic kidney disease: preliminary study.

Authors

Patrick Tomas Svrcek, Junbong Jang, Connie Ge, Hajeong Lee, Young H Kim

Published in

Ultrasonography (Seoul, Korea). Jun 15, 2025. Epub Jun 15, 2025.

Abstract

This study evaluates the feasibility of combining deep learning (DL) and conventional computer vision techniques to classify kidney ultrasound (US) images for the presence or absence of chronic kidney disease (CKD).
A retrospective analysis was conducted on 258 kidneys (124 normal and 134 with CKD). A DL model was trained using midsagittal US images of the right kidney and corresponding contour maps to automate measurements of parenchymal thickness and parenchyma-to-sinus ratios. These features were integrated with a convolutional neural network for classification. The ground truth was determined based on clinical CKD diagnosis and laboratory data.
The combined DL and conventional feature extraction model achieved an accuracy of 82%, with a specificity of 93% and a negative predictive value of 97%. This approach outperformed models that relied solely on raw US images using DL, which achieved an accuracy of 64%. The inclusion of contour-based parenchymal measurements enhanced classification performance.
The integration of DL with automated feature extraction enables accurate classification of CKD using minimal user input. This proof-of-concept study highlights the potential of combining artificial intelligence-driven analysis with traditional metrics to serve as a noninvasive adjunct for CKD diagnosis and monitoring.

PMID:
40755093
Bibliographic data and abstract were imported from PubMed on 04 Aug 2025.

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