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Updated: Jan 25, 2026

Rapid Identification of Gram Negative Bacteria from Blood Culture Broth Using MALDI-TOF Mass Spectrometry
Published on: May 28, 2014
An annotated dataset of Gram stains from positive blood cultures
Qiaolian Yi1, Xiaoyan Gou2, Renyuan Zhu2
1Department of Laboratory Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100730, China. yiqiaolian@pumch.cn.
Abstract:
Bloodstream infections (BSIs) of high morbidity and mortality are across all age groups, and urgent for accurate intervention. Gram stain interpretation of positive blood cultures (PBCs) is crucial for early diagnosing BSIs, yet this manual process is labor-intensive, time-consuming, and highly operator-dependent. Artificial intelligence (AI)-assisted microscopic interpretation of stained smears presents beneficial to microbiology diagnostics. Addressing the auto-identification of blood-culture Gram stains, this study introduces a dataset of Gram-stain smears collected in clinical practice. The dataset includes 505 microscopic images, covering up to 57 species associated with BSIs, with a total of 7528 annotations. These annotations categorized by staining characteristics and morphological features into cocci, bacilli, and fungi. We trained and validated an object detection model based on the YOLOv10 architecture on this dataset to automatically localize and classify these morphological categories in microscopic images. The publicly released dataset will help developments that utilize artificial intelligence to auto-interpretate the Gram stains from PBCs for routine clinical application.
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