Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran; Department of Bioimaging, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Department of Bacteriology and Virology, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Comput Biol Med. 2024 Nov;182:109131. doi: 10.1016/j.compbiomed.2024.109131. Epub 2024 Sep 10.
Antimicrobial resistance (AMR) presents a significant threat to global healthcare. Proteus mirabilis causes catheter-associated urinary tract infections (CAUTIs) and exhibits increased antibiotic resistance. Traditional diagnostics still rely on culture-based approaches, which remain time-consuming. Here, we study the use of machine learning (ML) to classify bacterial resistance profiles using straightforward microscopic imaging of P. mirabilis for resistance classification integrated with radiomics feature analysis and ML models. From 150 P. mirabilis strains isolated from catheters of patients diagnosed with a CAUTI, 30 % displayed multidrug resistance using the standardized disk diffusion method, and 60 % showed strong biofilm activity in microtiter plate assays. As a more rapid alternative, we introduce wavelet-based and regular microscopy imaging with feature extraction/selection, following image preprocessing steps (image denoising, normalization, and mask creation). These features enable training and testing different ML models with 5-fold cross-validation for P. mirabilis resistance classification. From these models, the Random Forest (RF) algorithm exhibited the highest performance with ACC = 0.95, specificity = 0.97, sensitivity = 0.88, and AUC = 0.98 among the other ML algorithms considered in this study for P. mirabilis resistance classification. This successful application of wavelet-based feature Radiomics analysis with RF model represents a crucial step towards a precise, rapid, and cost-effective method to distinguish antibiotic resistant P. mirabilis strains.
抗微生物药物耐药性 (AMR) 对全球医疗保健构成重大威胁。奇异变形杆菌会引起与导管相关的尿路感染 (CAUTI)，并表现出更高的抗生素耐药性。传统的诊断方法仍然依赖于基于培养的方法，这些方法仍然耗时。在这里，我们研究了使用机器学习 (ML) 通过奇异变形杆菌的简单显微镜成像来对细菌耐药谱进行分类，将耐药分类与放射组学特征分析和 ML 模型相结合。从 150 株从诊断为 CAUTI 的患者的导管中分离出的奇异变形杆菌菌株中，30%的菌株使用标准化圆盘扩散法显示出多药耐药性，60%的菌株在微量滴定板测定中显示出强烈的生物膜活性。作为一种更快速的替代方法，我们引入了基于小波的和常规显微镜成像，同时进行特征提取/选择，然后进行图像预处理步骤（图像去噪、归一化和蒙版创建）。这些特征使我们能够使用 5 倍交叉验证来训练和测试不同的 ML 模型，以对奇异变形杆菌的耐药性进行分类。在这些模型中，随机森林 (RF) 算法的表现最佳，其对奇异变形杆菌耐药性分类的准确性 (ACC) 为 0.95、特异性 (specificity) 为 0.97、敏感性 (sensitivity) 为 0.88、曲线下面积 (AUC) 为 0.98。这项基于小波的特征放射组学分析与 RF 模型的成功应用代表了朝着精确、快速和具有成本效益的方法区分抗生素耐药性奇异变形杆菌菌株迈出的关键一步。
