Automated detection of pyogenic liver abscess and diagnosis of Klebsiella pneumoniae infection based on CECT images with deep learning: A multicenter study

作者：Zhou J, Peng X, Yang Y, Shang Z, Feng X, Chen Y, Wang Q, Yan Q, Zeng H, Chen Y, Wang N, Zhang Y, Zou E, Dong N, Chen G, Wang Y · 发表于：European journal of radiology · 年份：2025 · DOI：10.1016/j.ejrad.2025.112462 · 研究领域：Antimicrobial resistance、Deep learning、Klebsiella pneumoniae、Pyogenic liver abscess、Radiomics

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. CONCLU...