Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis

Lee, Kwang Seob; Lim, Hyung Jae; Kim, Hyung Sun; Park, Joon Seong; Yoo, Jae-Woo; Yong, Dongeun

doi:10.1128/spectrum.01769-21

Cited by 6 publications

(2 citation statements)

References 38 publications

(36 reference statements)

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“…It was stated that 263 test images were used during the experiments. It was reported that the proposed method reaches 90.9% accuracy even at low bacterial density [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

et al. 2023

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Urine sediment examination is one of the main tests used in the diagnosis of many diseases. Thanks to this test, many diseases can be detected in advance. Examining the results of this test is an intensive and time-consuming process. Therefore, it is very important to automatically interpret the urine sediment test results using computer-aided systems. In this study, a data set consisting of eight classes was used. The data set used in the study consists of 8509 particle images obtained by examining the particles in the urine sediment. A hybrid model based on textural and Convolutional Neural Networks (CNN) was developed to classify the images in the related data set. The features obtained using textural-based methods and the features obtained from CNN-based architectures were combined after optimizing using the Minimum Redundancy Maximum Relevance (mRMR) method. In this way, we aimed to extract different features of the same image. This increased the performance of the proposed model. The CNN-based ResNet50 architecture and textural-based Local Binary Pattern (LBP) method were used for feature extraction. Finally, the optimized and combined feature map was classified at different machine learning classifiers. In order to compare the performance of the model proposed in the study, results were also obtained from different CNN architectures. A high accuracy value of 96.0% was obtained in the proposed model.

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“…It was stated that 263 test images were used during the experiments. It was reported that the proposed method reaches 90.9% accuracy even at low bacterial density [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

et al. 2023

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“…Many researchers use AI for bacterial detection. Lee et al developed a method for rapid detection of bacteria in urine using laser scattering combined with deep learning analysis ( 24 ). Wang et al indicated that a combination of Raman spectroscopy and deep learning algorithms can accurately identify bacteria at the genus and species levels ( 25 ).…”

Section: Introductionmentioning

confidence: 99%

Simple and Rapid Discrimination of Methicillin-Resistant Staphylococcus aureus Based on Gram Staining and Machine Vision

Shi

Shen

et al. 2023

Microbiol Spectr

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Oxacillin sodium salt destroys the integrity of the cell wall of MSSA immediately, appearing Gram negative, whereas MRSA is relatively stable and still appears Gram positive. This color change can be detected by microscopic examination and MV analysis. This new strategy has significantly reduced the time to detect resistance. The results show that using oxacillin sodium salt combined with Gram staining and MV analysis is a new, simple and rapid method for identification of MRSA.

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A fully interpretable machine learning model for increasing the effectiveness of urine screening

Del Ben,

Da Col,

Cobârzan

et al. 2023

American Journal of Clinical Pathology

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Objectives This article addresses the need for effective screening methods to identify negative urine samples before urine culture, reducing the workload, cost, and release time of results in the microbiology laboratory. We try to overcome the limitations of current solutions, which are either too simple, limiting effectiveness (1 or 2 parameters), or too complex, limiting interpretation, trust, and real-world implementation (“black box” machine learning models). Methods The study analyzed 15,312 samples from 10,534 patients with clinical features and the Sysmex Uf-1000i automated analyzer data. Decision tree (DT) models with or without lookahead strategy were used, as they offer a transparent set of logical rules that can be easily understood by medical professionals and implemented into automated analyzers. Results The best model achieved a sensitivity of 94.5% and classified negative samples based on age, bacteria, mucus, and 2 scattering parameters. The model reduced the workload by an additional 16% compared to the current procedure in the laboratory, with an estimated financial impact of €40,000/y considering 15,000 samples/y. Identified logical rules have a scientific rationale matched to existing knowledge in the literature. Conclusions Overall, this study provides an effective and interpretable screening method for urine culture in microbiology laboratories, using data from the Sysmex UF-1000i automated analyzer. Unlike other machine learning models, our model is interpretable, generating trust and enabling real-world implementation.

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Rapid Bacterial Detection in Urine Using Laser Scattering and Deep Learning Analysis

Cited by 6 publications

References 38 publications

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

Automatic Classification of Particles in the Urine Sediment Test with the Developed Artificial Intelligence-Based Hybrid Model

Simple and Rapid Discrimination of Methicillin-Resistant Staphylococcus aureus Based on Gram Staining and Machine Vision

A fully interpretable machine learning model for increasing the effectiveness of urine screening

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