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

Authors :
Xiuli Wang
Yinghan Shi
Shasha Guo
Xuzhong Qu
Fei Xie
Zhimei Duan
Ye Hu
Han Fu
Xin Shi
Tingwei Quan
Kaifei Wang
Lixin Xie
Source :
Scientific Data, Vol 11, Iss 1, Pp 1-8 (2024)
Publication Year :
2024
Publisher :
Nature Portfolio, 2024.

Abstract

Abstract 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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20524463
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Scientific Data
Publication Type :
Academic Journal
Accession number :
edsdoj.3fc8a19022a84f5185ad2b64bd3bd698
Document Type :
article
Full Text :
https://doi.org/10.1038/s41597-024-03370-5