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".

翻译：脓毒症是一种危及生命的疾病，需要快速诊断与治疗。传统的微生物学方法耗时且昂贵。为应对这些挑战，本研究开发了深度学习算法，旨在从脓毒症患者阳性血样本的革兰氏染色涂片显微图像中识别14种细菌和3种类酵母真菌。研究共使用了16,637张革兰氏染色显微图像。分析采用Cellpose 3模型进行分割，并采用基于注意力的深度多示例学习进行分类。我们的模型对细菌和真菌的分类准确率分别为77.15%和71.39%，ROC AUC值分别为0.97和0.88。对于痤疮皮肤杆菌、屎肠球菌、嗜麦芽窄食单胞菌和光滑念珠菌，获得了最高值，可达96.2%。在形态学相近的物种（如人葡萄球菌和溶血葡萄球菌）以及由于形态多样性高的白色念珠菌内部，观察到分类存在困难。本研究证实了我们的模型在微生物分类方面的潜力，但也表明需要进一步优化和扩展训练数据集。未来，该技术因其简便性和可及性，可支持微生物诊断，缩短诊断时间并提高脓毒症治疗的有效性。本出版物中呈现的部分成果已由欧洲专利局专利申请EP24461637.1"一种用于识别血液中微生物的计算机实现方法及其数据处理系统"所涵盖。