Mycobacterium tuberculosis recognition with conventional microscopy

Costa-Filho, C. F. F.; Levy, P. C.; Xavier, Clahildek M.; Costa, Marly Guimarães Fernandes; Fujimoto, Luciana Botinelly Mendonça; Salem, Júlia Ignez

doi:10.1109/embc.2012.6347426

Cited by 19 publications

(7 citation statements)

References 7 publications

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“…Image algorithms for AFB recognition have significantly improved, especially in sputum samples [120,121]. Technological advancements, including improved red versus green contrast in color-based pixel segmentation [122] and sophisticated AI algorithms [123], have significantly contributed to progress. A CNN algorithm based on whole-slide image (WSI) patch analysis showed superior accuracy [124], indicating that AI workflow could rule out negative cases without human review, streamlining diagnostics and reducing routine workloads.…”

Section: Mycobacteriamentioning

confidence: 99%

The Impact of Artificial Intelligence on Microbial Diagnosis

Alsulimani,

Akhter,

Jameela

et al. 2024

Microorganisms

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Traditional microbial diagnostic methods face many obstacles such as sample handling, culture difficulties, misidentification, and delays in determining susceptibility. The advent of artificial intelligence (AI) has markedly transformed microbial diagnostics with rapid and precise analyses. Nonetheless, ethical considerations accompany AI adoption, necessitating measures to uphold patient privacy, mitigate biases, and ensure data integrity. This review examines conventional diagnostic hurdles, stressing the significance of standardized procedures in sample processing. It underscores AI’s significant impact, particularly through machine learning (ML), in microbial diagnostics. Recent progressions in AI, particularly ML methodologies, are explored, showcasing their influence on microbial categorization, comprehension of microorganism interactions, and augmentation of microscopy capabilities. This review furnishes a comprehensive evaluation of AI’s utility in microbial diagnostics, addressing both advantages and challenges. A few case studies including SARS-CoV-2, malaria, and mycobacteria serve to illustrate AI’s potential for swift and precise diagnosis. Utilization of convolutional neural networks (CNNs) in digital pathology, automated bacterial classification, and colony counting further underscores AI’s versatility. Additionally, AI improves antimicrobial susceptibility assessment and contributes to disease surveillance, outbreak forecasting, and real-time monitoring. Despite a few limitations, integration of AI in diagnostic microbiology presents robust solutions, user-friendly algorithms, and comprehensive training, promising paradigm-shifting advancements in healthcare.

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Section: Mycobacteriamentioning

confidence: 99%

The Impact of Artificial Intelligence on Microbial Diagnosis

Alsulimani,

Akhter,

Jameela

et al. 2024

Microorganisms

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“…Mtb are slowly replicating bacteria, so smear microscopy provides data considerably faster than waiting for organisms to grow in culture, which is the conventional gold standard for TB diagnosis in clinical microbiology practice [12]. When done well, microscopy has a high specificity (99%) for detecting Mtb cells, and it has also become more sensitive since switching from Ziehl-Neelsen to fluorescent auramine-based methods (from 0.34-0.94 to 0.52-0.97 according to one systematic review) [16,17]. The wide ranges of diagnostic sensitivity described in that paper also reflect the technique's complexity and subjectivity.…”

Section: Importance Of Microscopymentioning

confidence: 99%

“…Remaining within the scope of colour space transformation, the initial approach taken was to create a scalar selection from the following colour spaces: RGB, HSI, YCbCr, and Lab [16]. The components and removal of the components of these colour spaces were employed for pixel classification in the segmentation step.…”

Section: Image Gradient-based Approachesmentioning

confidence: 99%

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

Zachariou,

Arandjelović,

Sloan

2023

BioMedInformatics

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Tuberculosis (TB) is one of the leading infectious causes of death worldwide. The effective management and public health control of this disease depends on early detection and careful treatment monitoring. For many years, the microscopy-based analysis of sputum smears has been the most common method to detect and quantify Mycobacterium tuberculosis (Mtb) bacteria. Nonetheless, this form of analysis is a challenging procedure since sputum examination can only be reliably performed by trained personnel with rigorous quality control systems in place. Additionally, it is affected by subjective judgement. Furthermore, although fluorescence-based sample staining methods have made the procedure easier in recent years, the microscopic examination of sputum is a time-consuming operation. Over the past two decades, attempts have been made to automate this practice. Most approaches have focused on establishing an automated method of diagnosis, while others have centred on measuring the bacterial load or detecting and localising Mtb cells for further research on the phenotypic characteristics of their morphology. The literature has incorporated machine learning (ML) and computer vision approaches as part of the methodology to achieve these goals. In this review, we first gathered publicly available TB sputum smear microscopy image sets and analysed the disparities in these datasets. Thereafter, we analysed the most common evaluation metrics used to assess the efficacy of each method in its particular field. Finally, we generated comprehensive summaries of prior work on ML and deep learning (DL) methods for automated TB detection, including a review of their limitations.

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“…Many conventional systems, such as those developed by MoradiAmin, Memari, Samadzadehaghdam, Kermani, & Talebi (2016), Khutlang, Krishnan, Whitelaw, &Douglas (2010) andCostaFilho et al (2012), followed the detection-classification stage structure and have achieved satisfactory results. Song et al (2017) and Momenzadeh, Vard, Talebi, Mehri Dehnavi, & Rabbani (2018) proposed the computer-aided diagnostic systems for microscopic images using segmentation and classification methods.…”

Section: Introductionmentioning

confidence: 99%

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

Kuok

Horng

Liao

et al. 2019

Microscopy Res & Technique

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Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy remains the primary diagnostic laboratory test. However, microscopic examination is always time‐consuming and tedious. Therefore, an effective computer‐aided image identification system is needed to provide timely assistance in diagnosis. The current identification system usually suffers from complex color variations of the images, resulting in plentiful of false object detection. To overcome the dilemma, we propose a two‐stage Mycobacterium tuberculosis identification system, consisting of candidate detection and classification using convolution neural networks (CNNs). The refined Faster region‐based CNN was used to distinguish candidates of M. tuberculosis and the actual ones were classified by utilizing CNN‐based classifier. We first compared three different CNNs, including ensemble CNN, single‐member CNN, and deep CNN. The experimental results showed that both ensemble and deep CNNs were on par with similar identification performance when analyzing more than 19,000 images. A much better recall value was achieved by using our proposed system in comparison with conventional pixel‐based support vector machine method for M. tuberculosis bacilli detection.

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Mycobacterium tuberculosis recognition with conventional microscopy

Cited by 19 publications

References 7 publications

The Impact of Artificial Intelligence on Microbial Diagnosis

The Impact of Artificial Intelligence on Microbial Diagnosis

Automated Methods for Tuberculosis Detection/Diagnosis: A Literature Review

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks

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