ArticleFrontiers in microbiology2025
HoloMoA: a holography and deep learning tool for the identification of antimicrobial mechanisms of action and the detection of novel MoA.
Article in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
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11 authors.
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Abstract
We propose an innovative technology to classify the Mechanism of Action (MoA) of antimicrobials and predict their novelty, called HoloMoA. Our rapid, robust, affordable and versatile tool is based on the combination of time-lapse Digital Inline Holographic Microscopy (DIHM) and Deep Learning (DL). In combination with hologram reconstruction. DIHM enables a label-free, time-resolved visualization of bacterial cell morphology and quantitative phase map to reveal phenotypic responses to antimicrobials, while DL techniques are powerful tools to extract discriminative features from image sequences and classify them. We assessed the performance of HoloMoA on
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Registered trials
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