AI-Driven Rapid Identification of Bacterial and Fungal Pathogens in Blood Smears of Septic Patients

2025-03-17

AI Summary

To address the time-consuming and costly bottleneck of conventional microbiological culture in rapid sepsis diagnosis, this study proposes a weakly supervised, multi-pathogen identification method for Gram-stained blood smear microscopic images. We introduce the first integration of attention-based deep multiple instance learning (MIL) with the Cellpose 3 cell segmentation framework to enable fine-grained classification of 14 bacterial and 3 yeast-like fungal species. A ROC-driven class optimization strategy is incorporated to enhance discriminative robustness. The method supports clinically interpretable weakly supervised training and is protected by a European patent (EP24461637.1). Evaluated on real clinical samples, it achieves 77.15% accuracy and 0.97 AUC for bacterial identification, and 71.39% accuracy and 0.88 AUC for fungal identification. Notably, accuracy exceeds 96.2% for four critical pathogens, including *Cutibacterium acnes*.

Technology Category

• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Computer Vision: Segmentation
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Security and Privacy: Data transparency and provenance
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

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".

Problem

• Rapid identification of bacterial and fungal pathogens in sepsis patients.
• Overcoming time-consuming and expensive traditional microbiological methods.
• Improving sepsis treatment effectiveness through AI-driven microbial classification.

Innovation

• Deep learning for pathogen identification
• Cellpose 3 model for image segmentation
• Attention-based Deep Multiple Instance Learning