<i>An annotated dataset of gram stains from positive blood cultures</i>

Bloodstream infections (BSIs) of high morbidity and mortality are across the age spectrum, and urgent for correct intervention. Gram stain interpretation of positive blood cultures (PBCs) is crucial for early diagnosing bloodstream infections, yet this manual process is labor-intensive, time-consuming, and highly operator-dependent. Artificial intelligence-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 pictures, and covers species variation up to 57 species of BSIs with 7,528 annotations, containing Gram-positive cocci in clusters, Gram-positive cocci in chains/pairs, Gram-negative rods, or fungus based on staining and morphology. The published dataset will help developments that utilize artificial intelligence to auto-interpretate the Gram stains from PBCs for routine clinical application.高发病率与高死亡率的血流感染（Bloodstream Infections, BSIs）可累及全年龄段人群，亟需开展精准干预。血培养阳性（Positive Blood Cultures, PBCs）样本的革兰染色（Gram stain）解读是血流感染早期诊断的关键环节，但该人工操作流程劳动强度大、耗时冗长且高度依赖操作人员专业水平。人工智能辅助的染色涂片镜检解读，对微生物学诊断具有重要应用价值。针对血培养革兰染色的自动识别任务，本研究发布了一套采集自临床实践的革兰染色涂片数据集。该数据集共包含505张图像，涵盖57种血流感染相关病原体，累计标注7528处，标注类别基于染色与形态学特征，包括簇状革兰阳性球菌、链状/成对革兰阳性球菌、革兰阴性杆菌及真菌。本公开数据集将助力面向常规临床应用的、基于人工智能的血培养阳性样本革兰染色自动解读技术研发。