X Y Wang

Chinese PLA General Hospital

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Lixin Xie

Chinese PLA General Hospital

Tingwei Quan

Wuhan National Laboratory for Optoelectronics

Xin Shi

Chinese PLA General Hospital

Han Fu

Zhejiang University of Science and Technology

Yinghan Shi

Chinese PLA General Hospital

Fei Xie

Chinese PLA General Hospital

Kaifei Wang

China University of Mining and Technology

Ye Hu

Chinese PLA General Hospital

Sha‐Sha Guo

Wuhan National Laboratory for Optoelectronics

Zhimei Duan

Chinese PLA General Hospital

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Chinese PLA General Hospital

Chinese People's Liberation Army

Wuhan National Laboratory for Optoelectronics

Huazhong University of Science and Technology

Chinese General Hospital College of Nursing and Liberal Arts

PLA Academy of Military Science

People's Liberation Army No. 150 Hospital

China Three Gorges University

Hebei Medical University

Nankai University

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A Clinical Bacterial Dataset for Deep Learning in Microbiological Rapid On-Site Evaluation

Scientific Data (2024)

X Y Wang Yinghan Shi Sha‐Sha Guo Xuzhong Qu Fei Xie

Microbiological Rapid On-Site Evaluation (M-ROSE) is based on smear staining and microscopic observation, providing critical references for the diagnosis and treatment of pulmonary infectious disease. Automatic identification of pathogens is the key to improving the quality and speed of M-ROSE. Recent advancements in deep learning have yielded numerous identification algorithms and datasets. However, most studies focus on artificially cultured bacteria and lack clinical data and algorithms. Therefore, we collected Gram-stained bacteria images from lower respiratory tract specimens of patients with lung infections in Chinese PLA General Hospital obtained by M-ROSE from 2018 to 2022 and desensitized images to produce 1705 images (4,912 × 3,684 pixels). A total of 4,833 cocci and 6,991 bacilli were manually labelled and differentiated into negative and positive. In addition, we applied the detection and segmentation networks for benchmark testing. Data and benchmark algorithms we provided that may benefit the study of automated bacterial identification in clinical specimens.

10.1038/s41597-024-03370-5

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