{"entity": "researcher", "timestamp": "2026-08-20T21:01:17.050Z", "family": "Hallstr\u00f6m", "given": "Erik", "initials": "E", "orcid": "0000-0002-0426-3217", "affiliations": ["Department of Information Technology, Uppsala University, Uppsala, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/e85304ff6d4b4c13b9d37bdedb2b5e1d.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/e85304ff6d4b4c13b9d37bdedb2b5e1d"}}, "publications": [{"entity": "publication", "iuid": "4bd6fe1e82da43469fd5cd6db22e0585", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/4bd6fe1e82da43469fd5cd6db22e0585.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/4bd6fe1e82da43469fd5cd6db22e0585"}}, "title": "Rapid label-free identification of seven bacterial species using microfluidics, single-cell time-lapse phase-contrast microscopy, and deep learning-based image and video classification.", "authors": [{"family": "Hallstr\u00f6m", "given": "Erik", "initials": "E", "orcid": "0000-0002-0426-3217", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e85304ff6d4b4c13b9d37bdedb2b5e1d.json"}}, {"family": "Kandavalli", "given": "Vinodh", "initials": "V"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/833afe3444d84c24be12ea1468563bea.json"}}, {"family": "Hast", "given": "Anders", "initials": "A"}], "type": "journal article", "published": "2025-09-08", "journal": {"title": "PLoS ONE", "issn": "1932-6203", "volume": "20", "issue": "9", "pages": "e0330265", "issn-l": "1932-6203"}, "abstract": "For effective treatment of bacterial infections, it is essential to identify the species causing the infection as early as possible. Current methods typically require hours of overnight culturing of a bacterial sample and a larger quantity of cells to function effectively. This study uses one-hour phase-contrast time-lapses of single-cell bacterial growth collected from microfluidic chip traps, also known as a \"mother machine\". These time-lapses are then used to train deep artificial neural networks (Convolutional Neural Networks and Vision Transformers) to identify the species. We have previously demonstrated this approach on four different species, which is now extended to seven common pathogens causing human infections: Pseudomonas aeruginosa, Escherichia coli, Klebsiella pneumoniae, Acinetobacter baumannii, Enterococcus faecalis, Proteus mirabilis, and Staphylococcus aureus. Furthermore, we expand upon our previous work by evaluating real-time performance as additional frames are captured during testing, and investigating the role of training set size, data quality, and data augmentation as well as the contribution of texture and morphology to performance. The experiments suggest that spatiotemporal features can be learned from video data of bacterial cell divisions, with both texture and morphology contributing to classifier decision. The method could be used simultaneously with phenotypic antibiotic susceptibility testing (AST) in the microfluidic chip. The best models attained an average precision of 93.5% and a recall of 94.7% (0.997 AUC) on a trap basis in a separate, unseen experiment with mixed species after around one hour. However, in a real-world scenario, one can assume many traps will contain the actual species causing the infection. Still, several challenges remain, such as isolating bacteria directly from blood and validating the method on diverse clinical isolates. This proof of principle study brings us closer to real-time diagnostics that could transform the initial treatment of acute infections.", "doi": "10.1371/journal.pone.0330265", "pmid": "40920893", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12416834"}, {"db": "pii", "key": "PONE-D-25-18176"}], "notes": [], "created": "2026-08-20T12:44:34.564Z", "modified": "2026-08-20T12:44:34.638Z"}, {"entity": "publication", "iuid": "8f4b6ef2d5cf4742afcb58951ad948d9", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/8f4b6ef2d5cf4742afcb58951ad948d9.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/8f4b6ef2d5cf4742afcb58951ad948d9"}}, "title": "CombiANT reader: Deep learning-based automatic image processing tool to robustly quantify antibiotic interactions.", "authors": [{"family": "Hallstr\u00f6m", "given": "Erik", "initials": "E", "orcid": "0000-0002-0426-3217", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/e85304ff6d4b4c13b9d37bdedb2b5e1d.json"}}, {"family": "Fatsis-Kavalopoulos", "given": "Nikos", "initials": "N", "orcid": "0000-0002-5081-0138", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/910a27f773b64bb6a1a15611b0524f34.json"}}, {"family": "Bimpis", "given": "Manos", "initials": "M"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/833afe3444d84c24be12ea1468563bea.json"}}, {"family": "Hast", "given": "Anders", "initials": "A"}, {"family": "Andersson", "given": "Dan I", "initials": "DI"}], "type": "journal article", "published": "2025-07-00", "journal": {"title": "PLOS Digit Health", "issn": "2767-3170", "volume": "4", "issue": "7", "pages": "e0000669", "issn-l": null}, "abstract": "Antibiotic resistance is a severe danger to human health, and combination therapy with several antibiotics has emerged as a viable treatment option for multi-resistant strains. CombiANT is a recently developed agar plate-based assay where three reservoirs on the bottom of the plate create a diffusion landscape of three antibiotics that allows testing of the efficiency of antibiotic combinations. This test, however, requires manually assigning nine reference points to each plate, which can be prone to errors, especially when plates need to be graded in large batches and by different users. In this study, an automated deep learning-based image processing method is presented that can accurately segment bacterial growth and measure distances between key points on the CombiANT assay at sub-millimeter precision. The software was tested on 100 plates using photos captured by three different users with their mobile phone cameras, comparing the automated analysis with the human scoring. The result indicates significant agreement between the users and the software ([Formula: see text] mm mean absolute error) and remains consistent when applied to different photos of the same assay despite varying photo qualities and lighting conditions. The speed and robustness of the automated analysis could streamline clinical workflows and make it easier to tailor treatment to specific infections. It could also aid large-scale antibiotic research by quickly processing hundreds of experiments in batch, obtaining better data, and ultimately supporting the development of better treatment strategies. The software can easily be integrated into a potential smartphone application, making it accessible in resource-limited environments. Integrating deep learning-based smartphone image analysis with simple agar-based tests like CombiANT could unlock powerful tools for combating antibiotic resistance.", "doi": "10.1371/journal.pdig.0000669", "pmid": "40627666", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12237020"}, {"db": "pii", "key": "PDIG-D-24-00449"}], "notes": [], "created": "2026-08-20T12:43:27.665Z", "modified": "2026-08-20T12:43:27.802Z"}]}