自体肋软骨鼻整形预后预测模型数据集

数据集获取（Figshare）

• 源站标题：Table 1_Development of a prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty: an automated machine learning approach.docx
• DOI：10.3389/fsurg.2025.1594514.s001
• 发布日期：2025-10-02
• 文件数：1 个
• 文件总体积：17.31 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Development_of_a_prognostic_prediction_model_and_visualization_system_for_autologous_costal_cartilage_rhinoplasty_an_automated_machine_learning_approach_docx/30263092

命令行下载

curl -L -o "Table 1_Development of a prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty: an automated machine learni.docx" "https://ndownloader.figshare.com/files/58443400"

文件清单

• Table 1_Development of a prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty: an automated machine learni.docx（17.31 KB）

文件总体积 17.31 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

ObjectiveTo develop an automated machine learning (AutoML)-based prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty (ACCR), addressing the clinical challenges of postoperative complications and satisfaction disparity.
MethodsA retrospective cohort of 447 ACCR patients (2019–2024) was analyzed, integrating 20+ parameters spanning biological, surgical, and behavioral domains. We proposed an improved metaheuristic algorithm (INPDOA) for AutoML optimization, validated against 12 CEC2022 benchmark functions. Bidirectional feature engineering identified critical predictors, and SHAP values quantified variable contributions. A MATLAB-based clinical decision support system (CDSS) was developed for real-time prognosis visualization.
ResultsThe INPDOA-enhanced AutoML model outperformed traditional algorithms, achieving a test-set AUC of 0.867 for 1-month complications and R2 = 0.862 for 1-year Rhinoplasty Outcome Evaluation (ROE) scores. Key predictors included nasal collision within 1 month, smoking, and preoperative ROE scores. Decision curve analysis demonstrated a net benefit improvement over conventional methods. The CDSS reduced prediction latency.
ConclusionThis study establishes the first AutoML-driven prognostic framework for ACCR, effectively bridging the gap between surgical precision and patient-reported outcomes. Its integration of dynamic risk prediction and explainable AI offers a paradigm for aesthetic surgical decision-making.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fsurg.2025.1594514.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Development_of_a_prognostic_prediction_model_and_visualization_system_for_autologous_costal_cartilage_rhinoplasty_an_automated_machine_learning_approach_docx/30263092）。