Saved in:
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2503.14542 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866908274245238784 |
|---|---|
| author | Sroka-Oleksiak, Agnieszka Pardyl, Adam Rymarczyk, Dawid Olechowska-Jarząb, Aldona Biegun-Drożdż, Katarzyna Ochońska, Dorota Wronka, Michał Borowa, Adriana Gosiewski, Tomasz Adamczyk, Miłosz Telega, Henryk Zieliński, Bartosz Brzychczy-Włoch, Monika |
| author_facet | Sroka-Oleksiak, Agnieszka Pardyl, Adam Rymarczyk, Dawid Olechowska-Jarząb, Aldona Biegun-Drożdż, Katarzyna Ochońska, Dorota Wronka, Michał Borowa, Adriana Gosiewski, Tomasz Adamczyk, Miłosz Telega, Henryk Zieliński, Bartosz Brzychczy-Włoch, Monika |
| contents | Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expensive. In response to these challenges, deep learning algorithms were developed to identify 14 bacteria species and 3 yeast-like fungi from microscopic images of Gram-stained smears of positive blood samples from sepsis patients.
A total of 16,637 Gram-stained microscopic images were used in the study. The analysis used the Cellpose 3 model for segmentation and Attention-based Deep Multiple Instance Learning for classification. Our model achieved an accuracy of 77.15% for bacteria and 71.39% for fungi, with ROC AUC of 0.97 and 0.88, respectively. The highest values, reaching up to 96.2%, were obtained for Cutibacterium acnes, Enterococcus faecium, Stenotrophomonas maltophilia and Nakaseomyces glabratus. Classification difficulties were observed in closely related species, such as Staphylococcus hominis and Staphylococcus haemolyticus, due to morphological similarity, and within Candida albicans due to high morphotic diversity.
The study confirms the potential of our model for microbial classification, but it also indicates the need for further optimisation and expansion of the training data set. In the future, this technology could support microbial diagnosis, reducing diagnostic time and improving the effectiveness of sepsis treatment due to its simplicity and accessibility. Part of the results presented in this publication was covered by a patent application at the European Patent Office EP24461637.1 "A computer implemented method for identifying a microorganism in a blood and a data processing system therefor". |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14542 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients Sroka-Oleksiak, Agnieszka Pardyl, Adam Rymarczyk, Dawid Olechowska-Jarząb, Aldona Biegun-Drożdż, Katarzyna Ochońska, Dorota Wronka, Michał Borowa, Adriana Gosiewski, Tomasz Adamczyk, Miłosz Telega, Henryk Zieliński, Bartosz Brzychczy-Włoch, Monika Image and Video Processing Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning Sepsis is a life-threatening condition which requires rapid diagnosis and treatment. Traditional microbiological methods are time-consuming and expensive. In response to these challenges, deep learning algorithms were developed to identify 14 bacteria species and 3 yeast-like fungi from microscopic images of Gram-stained smears of positive blood samples from sepsis patients. A total of 16,637 Gram-stained microscopic images were used in the study. The analysis used the Cellpose 3 model for segmentation and Attention-based Deep Multiple Instance Learning for classification. Our model achieved an accuracy of 77.15% for bacteria and 71.39% for fungi, with ROC AUC of 0.97 and 0.88, respectively. The highest values, reaching up to 96.2%, were obtained for Cutibacterium acnes, Enterococcus faecium, Stenotrophomonas maltophilia and Nakaseomyces glabratus. Classification difficulties were observed in closely related species, such as Staphylococcus hominis and Staphylococcus haemolyticus, due to morphological similarity, and within Candida albicans due to high morphotic diversity. The study confirms the potential of our model for microbial classification, but it also indicates the need for further optimisation and expansion of the training data set. In the future, this technology could support microbial diagnosis, reducing diagnostic time and improving the effectiveness of sepsis treatment due to its simplicity and accessibility. Part of the results presented in this publication was covered by a patent application at the European Patent Office EP24461637.1 "A computer implemented method for identifying a microorganism in a blood and a data processing system therefor". |
| title | AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients |
| topic | Image and Video Processing Artificial Intelligence Computational Engineering, Finance, and Science Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2503.14542 |