Bibliometric Analyses of Applications of Artificial Intelligence on Tuberculosis

Cabanillas-Lazo, Miguel; Quispe-Vicuña, Carlos; Pascual-Guevara, Milagros; Barja-Oré, John; Guerrero, María Eugenia; Munive-Degregori, Arnaldo; Mayta-Tovalino, Frank

doi:10.4103/ijmy.ijmy_134_22

The International Journal of Mycobacteriology

2022

DOI: 10.4103/ijmy.ijmy_134_22

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Bibliometric Analyses of Applications of Artificial Intelligence on Tuberculosis

Miguel Cabanillas-Lazo

Carlos Quispe-Vicuña

Milagros Pascual-Guevara

et al.

Abstract: Background: Tuberculosis is one of the leading causes of death worldwide affecting mainly low- and middle-income countries. Therefore, the objective is to analyze the bibliometric characteristics of the application of artificial intelligence (AI) in tuberculosis in Scopus. Methods: A bibliometric study, the Scopus database was used using a search strategy composed of controlled and free terms regarding tuberculosis and AI. The search fields “TITLE,” “AB… Show more

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Cited by 3 publications

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Deciphering drug discovery and microbial pathogenesis research in tuberculosis during the two decades of postgenomic era using entity mining approach

Kumar,

Shankar,

Akhter

2023

Arch Microbiol

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Deciphering drug discovery and microbial pathogenesis research in tuberculosis during the two decades of postgenomic era using entity mining approach

Kumar,

Shankar,

Akhter

2023

Arch Microbiol

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Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis

Creswell,

Vo,

Qin

et al. 2023

BMC Global Public Health

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Despite 30 years as a public health emergency, tuberculosis (TB) remains one of the world’s deadliest diseases. Most deaths are among persons with TB who are not reached with diagnosis and treatment. Thus, timely screening and accurate detection of TB, particularly using sensitive tools such as chest radiography, is crucial for reducing the global burden of this disease. However, lack of qualified human resources represents a common limiting factor in many high TB-burden countries. Artificial intelligence (AI) has emerged as a powerful complement in many facets of life, including for the interpretation of chest X-ray images. However, while AI may serve as a viable alternative to human radiographers and radiologists, there is a high likelihood that those suffering from TB will not reap the benefits of this technological advance without appropriate, clinically effective use and cost-conscious deployment. The World Health Organization recommended the use of AI for TB screening in 2021, and early adopters of the technology have been using the technology in many ways. In this manuscript, we present a compilation of early user experiences from nine high TB-burden countries focused on practical considerations and best practices related to deployment, threshold and use case selection, and scale-up. While we offer technical and operational guidance on the use of AI for interpreting chest X-ray images for TB detection, our aim remains to maximize the benefit that programs, implementers, and ultimately TB-affected individuals can derive from this innovative technology.

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Regional and National Trends in Tuberculosis Research in South Asian Association for Regional Cooperation Countries: Post-COVID-19 Pandemic Machine Learning Factorial Analysis

Ahmad,

Zyoud,

Murad

et al. 2024

The International Journal of Mycobacteriology

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Background: The number of tuberculosis (TB)-related morbidities and mortalities is still high in the South-east Asian region. This study was performed to characterize and visualize the post-coronavirus disease 2019 (COVID-19) TB research in South Asian Association for Regional Cooperation (SAARC). Methods: The Web of Science Core Collection database was utilized. A total of 4822 documents were included in the final analysis according to the predefined eligibility criteria. The data were exported to the R package and VOSviewer software for factorial analysis and network visualization, respectively. Results: The included documents were published in English between 2020 and 2024 in 1255 journals. These documents were authored by 17005 authors (3.53 authors/document). The authors collaboration index was noticed 3.61. In total, 74.16% documents were published as the article. The highest number of documents were published in 2022 (n = 1089). The documents published in 2020 received the highest number of mean total citations per article (n = 8.64). The most published journal was Cureus Journal of Medical Science (n = 228). The most prolific author was Gupta A (n = 115). The most active institution was the All India Institute of Medical Sciences (n = 587). The top most trending topics were Mycobacterium tuberculosis, TB elimination, molecular docking, extrapulmonary TB, Cartridge Based Nucleic Acid Amplification Test, and multidrug resistance. India was the most productive country and had the strongest research collaboration with the United States of America (USA), the United Kingdom (UK), and South Africa. Pakistan was mainly collaborating with the USA, the UK, Saudi Arabia, and China. The India and Pakistan collaboration was observed only in 66 documents. Conclusion: There is a poor output of scientific publication on TB in most SAARC countries. However, it is recognized that India has produced the highest number of scientific publications. The detection of undiagnosed post-COVID-19 pandemic TB cases is crucial to control further cases in the region. An effective regional cooperation should be established among institutions, universities, and countries to achieve the World Health Organization End TB goals.

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Bibliometric Analyses of Applications of Artificial Intelligence on Tuberculosis

Cited by 3 publications

References 20 publications

Deciphering drug discovery and microbial pathogenesis research in tuberculosis during the two decades of postgenomic era using entity mining approach

Deciphering drug discovery and microbial pathogenesis research in tuberculosis during the two decades of postgenomic era using entity mining approach

Early user perspectives on using computer-aided detection software for interpreting chest X-ray images to enhance access and quality of care for persons with tuberculosis

Regional and National Trends in Tuberculosis Research in South Asian Association for Regional Cooperation Countries: Post-COVID-19 Pandemic Machine Learning Factorial Analysis

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