表1：利用角膜断层成像和生物力学参数综合指数检测儿童亚临床圆锥角膜的机器学习分析

数据集获取（Figshare）

• 源站标题：Table1_Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical keratoconus.doc
• DOI：10.3389/fbioe.2023.1273500.s001
• 发布日期：2023-12-06
• 文件数：1 个
• 文件总体积：858.11 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table1_Machine_learning_analysis_with_the_comprehensive_index_of_corneal_tomographic_and_biomechanical_parameters_in_detecting_pediatric_subclinical_keratoconus_doc/24750852

命令行下载

curl -L -o "Table1_Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical k.doc" "https://ndownloader.figshare.com/files/43485399"

文件清单

• Table1_Machine learning analysis with the comprehensive index of corneal tomographic and biomechanical parameters in detecting pediatric subclinical k.doc（858.11 KB）

文件总体积 858.11 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

Background: Keratoconus (KC) occurs at puberty but diagnosis is focused on adults. The early diagnosis of pediatric KC can prevent its progression and improve the quality of life of patients. This study aimed to evaluate the ability of corneal tomographic and biomechanical variables through machine learning analysis to detect subclinical keratoconus (SKC) in a pediatric population.
Methods: Fifty-two KC, 52 SKC, and 52 control pediatric eyes matched by age and gender were recruited in a case-control study. The corneal tomographic and biomechanical parameters were measured by professionals. A linear mixed-effects test was used to compare the differences among the three groups and a least significant difference analysis was used to conduct pairwise comparisons. The receiver operating characteristic (ROC) curve and the Delong test were used to evaluate diagnostic ability. Variables were used in a multivariate logistic regression in the machine learning analysis, using a stepwise variable selection to decrease overfitting, and comprehensive indices for detecting pediatric SKC eyes were produced in each step.
Results: PE, BAD-D, and TBI had the highest area under the curve (AUC) values in identifying pediatric KC eyes, and the corresponding cutoff values were 12 μm, 2.48, and 0.6, respectively. For discriminating SKC eyes, the highest AUC (95% CI) was found in SP A1 with a value of 0.84 (0.765, 0.915), and BAD-D was the best parameter among the corneal tomographic parameters with an AUC (95% CI) value of 0.817 (0.729, 0.886). Three models were generated in the machine learning analysis, and Model 3 (y = 0.400PE + 1.982 DA ratio max [2 mm]−0.072 * SP A1−3.245) had the highest AUC (95% CI) value, with 90.4% sensitivity and 76.9% specificity, and the cutoff value providing the best Youden index was 0.19.
Conclusion: The criteria of parameters for diagnosing pediatric KC and SKC eyes were inconsistent with the adult population. Combined corneal tomographic and biomechanical parameters could enhance the early diagnosis of young patients and improve the inadequate representation of pediatric KC research.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fbioe.2023.1273500.s001（source: https://frontiersin.figshare.com/articles/dataset/Table1_Machine_learning_analysis_with_the_comprehensive_index_of_corneal_tomographic_and_biomechanical_parameters_in_detecting_pediatric_subclinical_keratoconus_doc/24750852）。