全数据集拟合误差（基于术前IOLM/CASIA角膜曲率预测与术后实际功率差异）
包含88只眼的术后CASIA实际功率与术前角膜曲率数据预测功率之间的拟合误差，用于评估白内障手术人工晶体计算模型准确性。
基本信息
资源简介
全数据集拟合误差（基于术前IOLM/CASIA角膜曲率预测与术后实际功率差异）
【一句话概览】
本数据集是 figshare 公开的结构化表格数据集，收录 1 个文件、合计 9.73 KB，主题涵盖 pseudophakic eye model、front surface measurement、feedforward neural network、power vector components、keratometric power restricted，可用于统计建模、特征工程与队列分析，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Fit error (difference between the C0 and C45 power vector components derived from the postoperatively measured CASIA real power and the respective components predicted with the feedforward shallow neural network or the multivariate linear regression) for the entire dataset (N = 88).》。配套论文《Prediction of corneal power vectors after cataract surgery with toric lens implantation–A vector analysis》发表于 PLOS ONE（2023），DOI：10.1371/journal.pone.0288316。源站首次发布：2023-09-08。
【数据内容】
源站原始描述：On the left side / right side the prediction error derived from the prediction models from the preoperative IOLM / CASIA keratometric data is shown. The prediction with the neural network performs slightly better as compared to the multivariate linear regression, and the prediction based on the preoperative CASIA data is slightly superior to the prediction based on the preoperative IOLM data. The power vector components in 45°/135° were reversed in sign for all left eyes in our dataset in order…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 3.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0288316.t003。
【主题与分类】
源站学科分类：Medicine、Genetics、Physiology、Biotechnology、Immunology、Mathematical Sciences not elsewhere classified。主题标签：pseudophakic eye model、front surface measurement、feedforward neural network、power vector components、keratometric power restricted、total corneal power、real corneal power、equivalent corneal power、corneal power vectors、shifted towards astigmatism。
【适用方向】
结构化表格数据可直接用于统计建模、特征工程与队列分析。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Fit_error_difference_between_the_C0_and_C45_power_vector_components_derived_from_the_postoperatively_measured_CASIA_real_power_and_the_respective_components_predicted_with_the_feedforward_shallow_neural_network_or_the_multivariate_linear_re/24109780
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Langenbucher, Achim; Szentmáry, Nóra; Cayless, Alan; Wendelstein, Jascha; Hoffmann, Peter (2023). Fit error (difference between the C0 and C45 power vector components derived from the postoperatively measured CASIA real power and the respective components predicted with the feedforward shallow neura…
下载信息
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提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Fit error (difference between the C0 and C45 power vector components derived from the postoperatively measured CASIA real power and the respective components predicted with the feedforward shallow neural network or the multivariate linear regression) for the entire dataset (N = 88).
- DOI：10.1371/journal.pone.0288316.t003
- 发布日期：2023-09-08
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Fit_error_difference_between_the_C0_and_C45_power_vector_components_derived_from_the_postoperatively_measured_CASIA_real_power_and_the_respective_components_predicted_with_the_feedforward_shallow_neural_network_or_the_multivariate_linear_re/24109780
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/42298135"
文件清单
Table 3.xls（9.73 KB）
文件总体积 9.73 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
On the left side / right side the prediction error derived from the prediction models from the preoperative IOLM / CASIA keratometric data is shown. The prediction with the neural network performs slightly better as compared to the multivariate linear regression, and the prediction based on the preoperative CASIA data is slightly superior to the prediction based on the preoperative IOLM data. The power vector components in 45°/135° were reversed in sign for all left eyes in our dataset in order to present the data in the same orientation as for right eyes. Mean, SD, Median, and 2.5% / 97.5% refer to the arithmetic mean, standard deviation, median, and the lower and upper bounds of the 95% confidence interval respectively.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0288316.t003（source: https://plos.figshare.com/articles/dataset/Fit_error_difference_between_the_C0_and_C45_power_vector_components_derived_from_the_postoperatively_measured_CASIA_real_power_and_the_respective_components_predicted_with_the_feedforward_shallow_neural_network_or_the_multivariate_linear_re/24109780）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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