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Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing.

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Bibliographic Details
Title: Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing.
Authors: Vo HT; School of Science, Engineering & Technology (SSET), RMIT University, Ho Chi Minh, Viet Nam., Nguyen S; School of Science, Engineering & Technology (SSET), RMIT University, Ho Chi Minh, Viet Nam., Tran AT; School of Science, Engineering & Technology (SSET), RMIT University, Ho Chi Minh, Viet Nam., Nguyen H; School of Science, Engineering & Technology (SSET), RMIT University, Ho Chi Minh, Viet Nam., Ho HB; Oxford University Clinical Research Unit, Ho Chi Minh, Viet Nam.; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom., Fowler PW; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.; Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, University of Oxford, Oxford, United Kingdom.; National Institute of Health Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, United Kingdom., Walker TM; Oxford University Clinical Research Unit, Ho Chi Minh, Viet Nam.; Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom., Nguyen TT; School of Science, Engineering & Technology (SSET), RMIT University, Ho Chi Minh, Viet Nam.
Source: Computational and structural biotechnology journal [Comput Struct Biotechnol J] 2025 May 26; Vol. 27, pp. 2208-2218. Date of Electronic Publication: 2025 May 26 (Print Publication: 2025).
Publication Type: Journal Article
Journal Info: Publisher: Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology Country of Publication: Netherlands NLM ID: 101585369 Publication Model: eCollection Cited Medium: Print ISSN: 2001-0370 (Print) Linking ISSN: 20010370 NLM ISO Abbreviation: Comput Struct Biotechnol J Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2001-0370
DOI:10.1016/j.csbj.2025.05.030