DataSheet1：基于人工智能的全膝关节置换术患者特异性术前规划算法
包含5409份全膝关节置换术术前计划，涵盖制造商默认与39位医生修正计划，用于AI患者特异性术前规划建模。
基本信息
资源简介
DataSheet1：基于人工智能的全膝关节置换术患者特异性术前规划算法
【一句话概览】
本数据集是 figshare 公开的文本数据集，收录 1 个文件、合计 130.42 KB，主题涵盖 total knee arthroplasty、patient-specific、preoperative planning、machine learning、orthopedic surgery，可用于医学自然语言处理（实体识别、信息抽取等），CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《DataSheet1_Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.pdf》。配套论文《Significant differences between manufacturer and surgeon in the accuracy of final component size prediction with CT-based patient-specific instrumentation for total knee arthroplasty》发表于 Knee Surgery, Sports Traumatology, Arthroscopy（2018），DOI：10.1007/s00167-018-4876-8。源站首次发布：2022-04-28。
【数据内容】
源站原始描述：Previous studies have shown that the manufacturer’s default preoperative plans for total knee arthroplasty with patient-specific guides require frequent, time-consuming changes by the surgeon. Currently, no research has been done on predicting preoperative plans for orthopedic surgery using machine learning. Therefore, this study aims to evaluate whether artificial intelligence (AI) driven planning tools can create surgeon and patient-specific preoperative plans that require fewer changes by the…
中文译文（机器翻译，仅供快速理解，请以原文为准）：既往研究表明，使用患者特异性导板的全膝关节置换术中，制造商默认的术前计划需要外科医生频繁且耗时地修改。目前，尚无研究使用机器学习预测骨科手术的术前计划。因此，本研究旨在评估人工智能（AI）驱动的规划工具能否创建针对特定外科医生和患者的术前计划，从而减少外科医生需要进行的修改。收集了包含 5409 份术前计划的数据集，其中包括制造商默认计划以及由 39 名外科医生修正后的计划。从术前计划中提取了特征，这些特征以针对特定外科医生和患者的方式描述植入物尺寸、位置和方向。基于这些特征，采用非线性回归模型预测外科医生修正后的术前计划。与制造商默认计划相比，外科医生对使用 AI 生成的术前计划所需进行的平均修改次数减少了 39
【数据结构与技术规格】
文件数 1 个，合计 130.42 KB。DataSheet1_Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.pdf：application/pdf，130.42 KB。数据集 DOI：10.3389/frobt.2022.840282.s001。
【主题与分类】
源站学科分类：Adaptive Agents and Intelligent Robotics、Artificial Life、Control Systems, Robotics and Automation、Computer Vision、Artificial Intelligence and Image Processing、Image Processing。主题标签：total knee arthroplasty、patient-specific、preoperative planning、machine learning、orthopedic surgery、support vector machine、artificial intelligence。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Artificial_Intelligence_Based_Patient-Specific_Preoperative_Planning_Algorithm_for_Total_Knee_Arthroplasty_pdf/19672236
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Lambrechts, Adriaan; Wirix-Speetjens, Roel; Maes, Frederik; Van Huffel, Sabine (2022). DataSheet1_Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.pdf. Frontiers. Dataset
【使用注意】
· 源站最后修改于 2023-06-03，引用时请注意版本时效。
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
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：DataSheet1_Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.pdf
- DOI：10.3389/frobt.2022.840282.s001
- 发布日期：2022-04-28
- 文件数：1 个
- 文件总体积：130.42 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/DataSheet1_Artificial_Intelligence_Based_Patient-Specific_Preoperative_Planning_Algorithm_for_Total_Knee_Arthroplasty_pdf/19672236
命令行下载
curl -L -o "DataSheet1_Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.pdf" "https://ndownloader.figshare.com/files/34933545"
文件清单
DataSheet1_Artificial Intelligence Based Patient-Specific Preoperative Planning Algorithm for Total Knee Arthroplasty.pdf（130.42 KB）
文件总体积 130.42 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
Previous studies have shown that the manufacturer’s default preoperative plans for total knee arthroplasty with patient-specific guides require frequent, time-consuming changes by the surgeon. Currently, no research has been done on predicting preoperative plans for orthopedic surgery using machine learning. Therefore, this study aims to evaluate whether artificial intelligence (AI) driven planning tools can create surgeon and patient-specific preoperative plans that require fewer changes by the surgeon. A dataset of 5409 preoperative plans, including the manufacturer’s default and the plans corrected by 39 surgeons, was collected. Features were extracted from the preoperative plans that describe the implant sizes, position, and orientation in a surgeon- and patient-specific manner. Based on these features, non-linear regression models were employed to predict the surgeon’s corrected preoperative plan. The average number of corrections a surgeon has to make to the preoperative plan generated using AI was reduced by 39.7% compared to the manufacturer’s default plan. The femoral and tibial implant size in the manufacturer’s plan was correct in 68.4% and 73.1% of the cases, respectively, while the AI-based plan was correct in 82.2% and 85.0% of the cases, respectively, compared to the surgeon approved plan. Our method successfully demonstrated the use of machine learning to create preoperative plans in a surgeon- and patient-specific manner for total knee arthroplasty.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/frobt.2022.840282.s001（source: https://frontiersin.figshare.com/articles/dataset/DataSheet1_Artificial_Intelligence_Based_Patient-Specific_Preoperative_Planning_Algorithm_for_Total_Knee_Arthroplasty_pdf/19672236）。
数据缺失？
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