ArticleCommunications AI & computing2026
mAIcrobe: an open-source framework for high-throughput bacterial image analysis.
Article in Communications AI & computing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Partaker: deep-learning-based single-cell-resolution analysis of multi-dimensional, long-term, microfluidic-based time-lapse microscopy data.Bioinformatics (Oxford, England) · 2026Article
- A broadly conserved gram-positive lipoprotein regulates cell elongation.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Microscopy of bacterial cells is crucial for studying bacterial growth, cell division, or responses to antibiotics. However, analyzing these images can be challenging because bacteria vary widely in shape and behaviour. Many existing tools require specialised computational expertise, which can limit their accessibility. Here we present mAIcrobe, an open-source image analysis framework that makes advanced bacterial microscopy analysis more accessible by combining deep learning-based segmentation methods, including StarDist, CellPose, and U-Net, with quantitative morphological profiling and a flexible neural network-based classification model. mAIcrobe can analyse a wide range of bacterial species, from spherical
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.