Prediction of the mean transit time using machine learning models based on radiomics features from digital subtraction angiography in moyamoya disease or moyamoya syndrome-a development and validation model study.
Journal Information
Full Title: Cardiovasc Diagn Ther
Abbreviation: Cardiovasc Diagn Ther
Country: Unknown
Publisher: Unknown
Language: N/A
Publication Details
Subject Category: Cardiac & Cardiovascular Systems
Available in Europe PMC: Yes
Available in PMC: Yes
PDF Available: No
Related Papers from Same Journal
Transparency Score
Transparency Indicators
Click on green indicators to view evidence textCore Indicators
"Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cdt.amegroups.com/article/view/10.21037/cdt-23-151/coif). The authors have no conflicts of interest to declare."
"Funding: This study was supported by Guangzhou Science and Technology Key Research and Development Program ( No. 202206010130 ), Medical Simulation Education Research Project of China Medical Education Development Center ( No. 2021MNZC37 ), and Science and Technology Program of Guangzhou ( No. 202102020650 )."
Additional Indicators
Assessment Info
Tool: rtransparent
OST Version: N/A
Last Updated: Aug 05, 2025