An effective and accurate identification system of <i>Mycobacterium tuberculosis</i> using convolution neural networks

Kuok, Chan Pang; Horng, Ming-Huwi; Liao, Yi Hung; Chow, Nan Haw; Sun, Yung Nien

doi:10.1002/jemt.23217

Cited by 33 publications

(15 citation statements)

References 29 publications

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“…Signal processing interventions focused specifically on the use of radiological data for tuberculosis 18,23 and drug-resistant tuberculosis, 19 ultrasound data for pneumonia, 24 micro scopy data for malaria, [25][26][27] and other biological sources of data for tuberculosis. [28][29][30] Most diagnostic interventions using AI in LMICs reported either high sensitivity, specificity, or high accuracy (>85% for all), or non-inferiority to comparator diagnostic tools. Machine learning aids clinicians in diagnosing tuberculosis, 31 and expert systems are used for diagnosing tuberculosis 32 and malaria.…”

Section: Ai-driven Interventions For Healthmentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

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Section: Ai-driven Interventions For Healthmentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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“…The common strategy is to establish expert systems using a machine learning method based on the clinical, radiological, and laboratory data of TB patients. Interestingly, machine learning has been reported to aid clinicians in diagnosing pulmonary TB or predicting drug-resistant TB [ 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 ]. For instance, Lopes et al presented three proposals for the application of pre-trained convolutional neural networks as image feature extractors to detect TB disease [ 110 ].…”

Section: New Techniquesmentioning

confidence: 99%

Review and Updates on the Diagnosis of Tuberculosis

Huang

Lin

Wang

et al. 2022

JCM

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Diagnosis of tuberculosis, and especially the diagnosis of extrapulmonary tuberculosis, still faces challenges in clinical practice. There are several reasons for this. Methods based on the detection of Mycobacterium tuberculosis (Mtb) are insufficiently sensitive, methods based on the detection of Mtb-specific immune responses cannot always differentiate active disease from latent infection, and some of the serological markers of infection with Mtb are insufficiently specific to differentiate tuberculosis from other inflammatory diseases. New tools based on technologies such as flow cytometry, mass spectrometry, high-throughput sequencing, and artificial intelligence have the potential to solve this dilemma. The aim of this review was to provide an updated overview of current efforts to optimize classical diagnostic methods, as well as new molecular and other methodologies, for accurate diagnosis of patients with Mtb infection.

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“…Several other studies proposing methods of automated detection of AFB on ZN stains evaluated smears. Other than delCarpio et al, and Law et al (who evaluated scans of slides containing the whole section present on the slide whole slide images (WSIs)) [ 4 , 5 ], all the other studies evaluated images captured with cameras (small parts of slides) [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. The specificity and sensibility varied from study to study as shown in Table 1 .…”

Section: Introductionmentioning

confidence: 99%

A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl–Neelsen Stain on Tissue

Zurac

Mogodici²,

Poncu

et al. 2022

Diagnostics

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Mycobacteria identification is crucial to diagnose tuberculosis. Since the bacillus is very small, finding it in Ziehl–Neelsen (ZN)-stained slides is a long task requiring significant pathologist’s effort. We developed an automated (AI-based) method of identification of mycobacteria. We prepared a training dataset of over 260,000 positive and over 700,000,000 negative patches annotated on scans of 510 whole slide images (WSI) of ZN-stained slides (110 positive and 400 negative). Several image augmentation techniques coupled with different custom computer vision architectures were used. WSIs automatic analysis was followed by a report indicating areas more likely to present mycobacteria. Our model performs AI-based diagnosis (the final decision of the diagnosis of WSI belongs to the pathologist). The results were validated internally on a dataset of 286,000 patches and tested in pathology laboratory settings on 60 ZN slides (23 positive and 37 negative). We compared the pathologists’ results obtained by separately evaluating slides and WSIs with the results given by a pathologist aided by automatic analysis of WSIs. Our architecture showed 0.977 area under the receiver operating characteristic curve. The clinical test presented 98.33% accuracy, 95.65% sensitivity, and 100% specificity for the AI-assisted method, outperforming any other AI-based proposed methods for AFB detection.

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

Cited by 33 publications

References 29 publications

Artificial intelligence and the future of global health

Artificial intelligence and the future of global health

Review and Updates on the Diagnosis of Tuberculosis

A New Artificial Intelligence-Based Method for Identifying Mycobacterium Tuberculosis in Ziehl–Neelsen Stain on Tissue

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