Table_1_AI-Blue-Carba：结合Blue-Carba与深度学习的快速改良碳青霉烯酶产生菌检测数据集

数据集获取（Figshare）

• 源站标题：Table_1_AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning.docx
• DOI：10.3389/fmicb.2020.585417.s003
• 发布日期：2020-11-20
• 文件数：1 个
• 文件总体积：18.12 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_AI-Blue-Carba_A_Rapid_and_Improved_Carbapenemase_Producer_Detection_Assay_Using_Blue-Carba_With_Deep_Learning_docx/13263902

命令行下载

curl -L -o "Table_1_AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning.docx" "https://ndownloader.figshare.com/files/25544234"

文件清单

• Table_1_AI-Blue-Carba: A Rapid and Improved Carbapenemase Producer Detection Assay Using Blue-Carba With Deep Learning.docx（18.12 KB）

文件总体积 18.12 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

A rapid and accurate detection of carbapenemase-producing Gram-negative bacteria (CPGNB) has an immediate demand in the clinic. Here, we developed and validated a method for rapid detection of CPGNB using Blue-Carba combined with deep learning (designated as AI-Blue-Carba). The optimum bacterial suspension concentration and detection wavelength were determined using a Multimode Plate Reader and integrated with deep learning modeling. We examined 160 carbapenemase-producing and non-carbapenemase-producing bacteria using the Blue-Carba test and a series of time and optical density values were obtained to build and validate the machine models. Subsequently, a simplified model was re-evaluated by descending the dataset from 13 time points to 2 time points. The best suitable bacterial concentration was determined to be 1.5 optical density (OD) and the optimum detection wavelength for AI-Blue-Carba was set as 615 nm. Among the 2 models (LRM and LSTM), the LSTM model generated the higher ROC-AUC value. Moreover, the simplified LSTM model trained by short time points (0–15 min) did not impair the accuracy of LSTM model. Compared with the traditional Blue-Carba, the AI-Blue-Carba method has a sensitivity of 95.3% and a specificity of 95.7% at 15 min, which is a rapid and accurate method to detect CPGNB.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmicb.2020.585417.s003（source: https://frontiersin.figshare.com/articles/dataset/Table_1_AI-Blue-Carba_A_Rapid_and_Improved_Carbapenemase_Producer_Detection_Assay_Using_Blue-Carba_With_Deep_Learning_docx/13263902）。