使用新颖性检测算法和机器学习自动诊断心脏瓣膜退化

数据集获取（Figshare）

• 源站标题：Automated diagnosis of heart valve degradation using novelty detection algorithms and machine learning
• DOI：10.1371/journal.pone.0222983
• 发布日期：2019-09-26
• 文件数：2 个
• 文件总体积：471.18 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Automated_diagnosis_of_heart_valve_degradation_using_novelty_detection_algorithms_and_machine_learning/9908558

命令行下载

curl -L -o "pone.0222983.s001.zip" "https://ndownloader.figshare.com/files/17814578"
curl -L -o "pone.0222983.s002.zip" "https://ndownloader.figshare.com/files/17814581"

文件清单

• pone.0222983.s001.zip（95.89 KB）
• pone.0222983.s002.zip（375.30 KB）

文件总体积 471.18 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

The blood flow through the major vessels holds great diagnostic potential for the identification of cardiovascular complications and is therefore routinely assessed with current diagnostic modalities. Heart valves are subject to high hydrodynamic loads which render them prone to premature degradation. Failing native aortic valves are routinely replaced with bioprosthetic heart valves. This type of prosthesis is limited by a durability that is often less than the patient’s life expectancy. Frequent assessment of valvular function can therefore help to ensure good long-term outcomes and to plan reinterventions. In this article, we describe how unsupervised novelty detection algorithms can be used to automate the interpretation of blood flow data to improve outcomes through early detection of adverse cardiovascular events without requiring repeated check-ups in a clinical environment. The proposed method was tested in an in-vitro flow loop which allowed simulating a failing aortic valve in a laboratory setting. Aortic regurgitation of increasing severity was deliberately introduced with tube-shaped inserts, preventing complete valve closure during diastole. Blood flow recordings from a flow meter at the location of the ascending aorta were analyzed with the algorithms introduced in this article and a diagnostic index was defined that reflects the severity of valvular degradation. The results indicate that the proposed methodology offers a high sensitivity towards pathological changes of valvular function and that it is capable of automatically identifying valvular degradation. Such methods may be a step towards computer-assisted diagnostics and telemedicine that provide the clinician with novel tools to improve patient care.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0222983（source: https://plos.figshare.com/articles/dataset/Automated_diagnosis_of_heart_valve_degradation_using_novelty_detection_algorithms_and_machine_learning/9908558）。