A deep learning–based study on automated CT diagnosis of kidney stones, hydronephrosis and pyonephrosis
- 1The School of Computer and Communication Engineering, University of Science and Technology Beijing
- 2Department of Urology, Shenzhen Bao’an Shiyan People’s Hospital
Abstract
Automated identification of urinary system diseases on non-contrast computed tomography (NCCT) can facilitate early diagnosis and inform treatment decisions. However, kidney stones, hydronephrosis, and pyonephrosis share overlapping NCCT appearances and require diagnostic cues at different spatial scales, making accurate identification within a unified framework challenging. To address this, we constructed an integrated CT-based framework for automated identification of urinary system diseases. First, the collected CT data were split into training, validation, and test sets and processed using a unified preprocessing pipeline for resolution standardization and normalization. Subsequently, we evaluated multiple image enhancement strategies, including histogram equalization, CLAHE, Laplacian sharpening, and brightness enhancement. We conducted a systematic comparison of these enhancement methods to quantify their effects on disease identification performance. To support unified automated diagnosis of kidney stones, hydronephrosis, and pyonephrosis, we developed a multi-disease renal diagnostic network, MSF-TEA Net. Through multi-scale feature fusion and multi-evidence collaborative modeling, it effectively represents the image features of different pathological states. This model introduces Tri-Evidence Attention (TEA), which models the high-density features of small calculi targets, the global morphological changes of fluid accumulation, and the inflammatory texture features of pus accumulation. Through adaptive weighted fusion, the network enhances discrimination and diagnostic performance when complex lesions coexist. Experimental results show that, within the unified MSF-TEA Net framework, Laplacian sharpening achieves the best performance among the tested enhancement methods, with a test accuracy of 94.40% ± 1.13% and an AUC of 99.30%, outperforming the other enhancement strategies. Ablation studies further confirm the contributions of PPM, ASPP, and TEA. Overall, the proposed unified framework enables reliable identification of kidney stones, hydronephrosis, and pyonephrosis, supporting clinical decision-making.
Related
- Validation of Machine Learning Models for classification of bladder dysfunction and risk of hydronephrosis in patients with Spina Bifida using videourodynamic data
- Graph theory-based machine learning for automated fibromyalgia diagnosis using fMRI during an emotional task
- Pyonephrosis—An Updated Morphological Glimpse into the Contemporary Management of an Immemorial Pathology: A Narrative Review
- Continuous Diagnostic Indices from BanffNET Automated Lesion Scores for Kidney Transplant Pathology: Development and Evaluation of a Diagnostic Prediction Model
- Editorial: Kidney cancer awareness month 2025: current progress and future prospects on kidney cancer prevention, diagnosis and treatment
BibTeX
@article{gao2026deep,
title = {{A deep learning–based study on automated CT diagnosis of kidney stones, hydronephrosis and pyonephrosis}},
author = {Hanlin Gao and Hongyao Chen and Yihe Wang and Weiping Wang and Chao Yao and Xiaobin Zhu and Chenjing Guo},
journal = {BMC Medical Imaging},
year = {2026},
month = jul,
doi = {10.1186/s12880-026-02581-z},
url = {https://doi.org/10.1186/s12880-026-02581-z},
note = {CC BY},
}
RIS
TY - JOUR
TI - A deep learning–based study on automated CT diagnosis of kidney stones, hydronephrosis and pyonephrosis
AU - Hanlin Gao
AU - Hongyao Chen
AU - Yihe Wang
AU - Weiping Wang
AU - Chao Yao
AU - Xiaobin Zhu
AU - Chenjing Guo
PY - 2026
DA - 2026/07/01
JO - BMC Medical Imaging
DO - 10.1186/s12880-026-02581-z
UR - https://doi.org/10.1186/s12880-026-02581-z
AB - Automated identification of urinary system diseases on non-contrast computed tomography (NCCT) can facilitate early diagnosis and inform treatment decisions. However, kidney stones, hydronephrosis, and pyonephrosis share overlapping NCCT appearances and require diagnostic cues at different spatial scales, making accurate identification within a unified framework challenging. To address this, we constructed an integrated CT-based framework for automated identification of urinary system diseases. First, the collected CT data were split into training, validation, and test sets and processed using a unified preprocessing pipeline for resolution standardization and normalization. Subsequently, we evaluated multiple image enhancement strategies, including histogram equalization, CLAHE, Laplacian sharpening, and brightness enhancement. We conducted a systematic comparison of these enhancement methods to quantify their effects on disease identification performance. To support unified automated diagnosis of kidney stones, hydronephrosis, and pyonephrosis, we developed a multi-disease renal diagnostic network, MSF-TEA Net. Through multi-scale feature fusion and multi-evidence collaborative modeling, it effectively represents the image features of different pathological states. This model introduces Tri-Evidence Attention (TEA), which models the high-density features of small calculi targets, the global morphological changes of fluid accumulation, and the inflammatory texture features of pus accumulation. Through adaptive weighted fusion, the network enhances discrimination and diagnostic performance when complex lesions coexist. Experimental results show that, within the unified MSF-TEA Net framework, Laplacian sharpening achieves the best performance among the tested enhancement methods, with a test accuracy of 94.40% ± 1.13% and an AUC of 99.30%, outperforming the other enhancement strategies. Ablation studies further confirm the contributions of PPM, ASPP, and TEA. Overall, the proposed unified framework enables reliable identification of kidney stones, hydronephrosis, and pyonephrosis, supporting clinical decision-making.
SN - 1471-2342
ER -