European journal of radiology · 2025
Automated detection of pyogenic liver abscess and diagnosis of Klebsiella pneumoniae infection based on CECT images with deep learning: A multicenter study.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод пока не подготовлен
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Zhou Junxi, Peng Xiuyun, Yang Yi, Shang Zhenqiu, Feng Xinyan, Chen Ye, Wang Qing, Yan Qiaoling, Zeng Hao, Chen Yaqing, Wang Neng, Zhang Yiping, Zou Enguang, Dong Ning, Chen Gang, Wang Yi
Аннотация
BACKGROUND: Accurate identification of bacterial pathogens in pyogenic liver abscess (PLA) remains challenging in low- and middle-income countries (LMICs). We developed and validated an artificial intelligence (AI) model that replicates clinical workflow to detect and diagnose Klebsiella pneumoniae liver abscess (KPLA) using contrast-enhanced computerized tomography (CECT).
METHODS: Patients with PLA were enrolled from three medical centers between April 2011 and April 2024. The Pyogenic Liver Abscess Detection with AI (PLADA) system employed a V-Net deep neural network for automated lesion segmentation. Radiomics features were subsequently extracted and ranked by importance. Six machine learning algorithms were used to develop clinical, radiomics, and integrated clinical-imaging models for KPLA diagnosis in the training cohort, with external validation in a multicenter framework.
RESULTS: A total of 492 PLA patients were identified. The PLADA algorithm accurately segments the lesions with a mean dice coefficient of 0.941 (95 % confidence interval: 0.899-0.983). The clinical-imaging model demonstrated strong discriminatory performance, with a mean area under the curve (AUC) of over 0.9 across all algorithms in the training cohort. The AdaBoost-based clinical-imaging model achieved an AUC of 0.847 (95 % CI: 0.753-0.940) in the external validation cohort, with high sensitivity (71.9 %) and specificity (86.4 %). Decision curve analysis (DCA) confirmed clinical utility.
CONCLUSIONS: The PLADA system provides an accurate, non-invasive method for KPLA diagnosis through a clinically interpretable two-stage AI framework. While limited by a retrospective, single-country dataset, this approach shows particular promise for early detectionof KPLA in resource-limited settings.
METHODS: Patients with PLA were enrolled from three medical centers between April 2011 and April 2024. The Pyogenic Liver Abscess Detection with AI (PLADA) system employed a V-Net deep neural network for automated lesion segmentation. Radiomics features were subsequently extracted and ranked by importance. Six machine learning algorithms were used to develop clinical, radiomics, and integrated clinical-imaging models for KPLA diagnosis in the training cohort, with external validation in a multicenter framework.
RESULTS: A total of 492 PLA patients were identified. The PLADA algorithm accurately segments the lesions with a mean dice coefficient of 0.941 (95 % confidence interval: 0.899-0.983). The clinical-imaging model demonstrated strong discriminatory performance, with a mean area under the curve (AUC) of over 0.9 across all algorithms in the training cohort. The AdaBoost-based clinical-imaging model achieved an AUC of 0.847 (95 % CI: 0.753-0.940) in the external validation cohort, with high sensitivity (71.9 %) and specificity (86.4 %). Decision curve analysis (DCA) confirmed clinical utility.
CONCLUSIONS: The PLADA system provides an accurate, non-invasive method for KPLA diagnosis through a clinically interpretable two-stage AI framework. While limited by a retrospective, single-country dataset, this approach shows particular promise for early detectionof KPLA in resource-limited settings.