{"title":"利用机器学习对变形杆菌进行抗生素特征分类:对多维放射组学特征的研究","authors":"","doi":"10.1016/j.compbiomed.2024.109131","DOIUrl":null,"url":null,"abstract":"<div><p>Antimicrobial resistance (AMR) presents a significant threat to global healthcare. <em>Proteus mirabilis</em> 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 <em>P. mirabilis</em> for resistance classification integrated with radiomics feature analysis and ML models.</p><p>From 150 <em>P. mirabilis</em> 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 <em>P. mirabilis</em> 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 <em>P. mirabilis</em> resistance classification.</p><p>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 <em>P. mirabilis</em> strains.</p></div>","PeriodicalId":10578,"journal":{"name":"Computers in biology and medicine","volume":null,"pages":null},"PeriodicalIF":7.0000,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features\",\"authors\":\"\",\"doi\":\"10.1016/j.compbiomed.2024.109131\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Antimicrobial resistance (AMR) presents a significant threat to global healthcare. <em>Proteus mirabilis</em> 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 <em>P. mirabilis</em> for resistance classification integrated with radiomics feature analysis and ML models.</p><p>From 150 <em>P. mirabilis</em> 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 <em>P. mirabilis</em> 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 <em>P. mirabilis</em> resistance classification.</p><p>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 <em>P. mirabilis</em> strains.</p></div>\",\"PeriodicalId\":10578,\"journal\":{\"name\":\"Computers in biology and medicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":7.0000,\"publicationDate\":\"2024-09-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computers in biology and medicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0010482524012162\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computers in biology and medicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0010482524012162","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
抗菌药耐药性(AMR)对全球医疗保健构成了重大威胁。奇异变形杆菌(Proteus mirabilis)会导致导管相关性尿路感染(CAUTIs),并表现出越来越强的抗生素耐药性。传统的诊断方法仍然依赖于基于培养的方法,而这种方法仍然非常耗时。在这里,我们研究了使用机器学习(ML)对细菌耐药性进行分类的方法,利用直接的显微镜成像对 mirabilis 进行耐药性分类,并将放射组学特征分析和 ML 模型整合在一起。从确诊为 CAUTI 患者的导尿管中分离出的 150 株 mirabilis 菌株中,有 30% 采用标准盘扩散法显示出多药耐药性,60% 在微孔板检测中显示出很强的生物膜活性。作为一种更快速的替代方法,我们在图像预处理步骤(图像去噪、归一化和掩膜创建)之后,引入了基于小波和常规显微镜成像的特征提取/选择。通过这些特征,可以对不同的 ML 模型进行训练和测试,并进行 5 倍交叉验证,以进行 P. mirabilis 抗性分类。在这些模型中,随机森林(RF)算法表现出最高的性能,在本研究中考虑的其他 ML 算法中,ACC = 0.95,特异性 = 0.97,灵敏度 = 0.88,AUC = 0.98。
Antibiotic profile classification of Proteus mirabilis using machine learning: An investigation into multidimensional radiomics features
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.
期刊介绍:
Computers in Biology and Medicine is an international forum for sharing groundbreaking advancements in the use of computers in bioscience and medicine. This journal serves as a medium for communicating essential research, instruction, ideas, and information regarding the rapidly evolving field of computer applications in these domains. By encouraging the exchange of knowledge, we aim to facilitate progress and innovation in the utilization of computers in biology and medicine.