基于足底压力计和人工神经网络的脊柱侧弯评估数据集

数据集获取（Figshare）

• 源站标题：Evaluation of scoliosis using baropodometer and artificial neural network
• DOI：10.6084/m9.figshare.7510403.v1
• 发布日期：2018-12-26
• 文件数：10 个
• 文件总体积：827.45 KB
• 源站页面：https://scielo.figshare.com/articles/dataset/Evaluation_of_scoliosis_using_baropodometer_and_artificial_neural_network/7510403

命令行下载

curl -L -o "2446-4740-reng-33-2-121-gf01.jpg" "https://ndownloader.figshare.com/files/13932281"
curl -L -o "2446-4740-reng-33-2-121-gf02.jpg" "https://ndownloader.figshare.com/files/13932290"
curl -L -o "2446-4740-reng-33-2-121-gf03.jpg" "https://ndownloader.figshare.com/files/13932296"
curl -L -o "2446-4740-reng-33-2-121-gf04.jpg" "https://ndownloader.figshare.com/files/13932302"
curl -L -o "Table_1.xls" "https://ndownloader.figshare.com/files/13932308"
curl -L -o "Table_2.xls" "https://ndownloader.figshare.com/files/13932317"
curl -L -o "Table_3.xls" "https://ndownloader.figshare.com/files/13932326"
curl -L -o "Table_4.xls" "https://ndownloader.figshare.com/files/13932335"

文件清单

• 2446-4740-reng-33-2-121-gf01.jpg（104.06 KB）
• 2446-4740-reng-33-2-121-gf02.jpg（90.83 KB）
• 2446-4740-reng-33-2-121-gf03.jpg（169.60 KB）
• 2446-4740-reng-33-2-121-gf04.jpg（429.17 KB）
• Table_1.xls（5.63 KB）
• Table_2.xls（5.63 KB）
• Table_3.xls（5.63 KB）
• Table_4.xls（5.63 KB）
• Table_5.xls（5.63 KB）
• Table_6.xls（5.63 KB）

文件总体积 827.45 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

Abstract Introduction: One of the most recurrent pathologies in the spine is scoliosis. It occurs in the frontal plane and is formed by one or more curves in the spinal column. The scoliosis causes global postural misalignment in an individual. One of the modifications produced by postural misalignment is the way in which an individual distributes weight to the feet. We aimed to implement an electronic system for separating patients with Degree I scoliosis (i.e., 1° to 19° scoliosis according to the Ricard classification) into two groups: C1 (1°-9°) and C2 (10°-9°). The highest percentage of patients with scoliosis is in this range: those who do not need to wear vests or undergo surgery and whose treatment is performed via special physical exercise and frequent evaluations by healthcare professionals. Methods The electronic system consists of a baropodometer and artificial neural networks (ANNs). The classification of patients in the scoliosis groups was performed with MATLAB software and a Single Layer Perceptron network using the backpropagation training algorithm. Evaluations were performed on 63 volunteers. Results The mean classification sensitivity was 93.7% in the C1 group and 94.5% in the C2 group. The classification accuracy was 83.3% in the C1 group and 96.0% in the C2 group. Conclusion The implemented system can contribute to the treatment of patients with scoliosis grades ranging from 1° to 19°, which represents the highest incidence of this pathology, for which the monitoring of the clinical condition using noninvasive techniques is of fundamental importance.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.6084/m9.figshare.7510403.v1（source: https://scielo.figshare.com/articles/dataset/Evaluation_of_scoliosis_using_baropodometer_and_artificial_neural_network/7510403）。