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Quantitative texture analysis using machine learning for predicting interpretable pulmonary perfusion from non-contrast computed tomography in pulmonary embolism patients

Crossref DOI link: https://doi.org/10.1186/s12931-024-03004-9

Published Online: 2024-10-28

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Li, Zihan

Zhao, Meixin

Li, Zhichun

Huang, Yu-Hua

Chen, Zhi

Pu, Yao

Zhao, Mayang

Liu, Xi

Wang, Meng

Wang, Kun

Yeung, Martin Ho Yin

Geng, Lisheng

Cai, Jing

Zhang, Weifang

Yang, Ruijie

Ren, Ge
Funding

Funding for this research was provided by:

the Health and Medical Research Fund (09200576, 09200576, 09200576, 09200576, 09200576, 09200576, 09200576, 09200576, 09200576, 09200576, 09200576)

Shenzhen Science and Technology Program (JCYJ20230807140403007, JCYJ20230807140403007, JCYJ20230807140403007, JCYJ20230807140403007, JCYJ20230807140403007, JCYJ20230807140403007, JCYJ20230807140403007)
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Text and Data Mining valid from 2024-10-28

Version of Record valid from 2024-10-28
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Article History

Received: 6 August 2024

Accepted: 4 October 2024

First Online: 28 October 2024

Declarations

:

: The internal dataset was approved by the Institutional Review Board (IRB) of the University of Hong Kong/Hospital Authority Hong Kong West Cluster. Written informed consent was not required because of the retrospective nature of the study. The external dataset was approved by the Peking University Third Hospital Medical Science Research Ethics Committee (No.IRB00006761-M2024673). Informed written consent was obtained from all participants who agreed to take part in the study.

: Not applicable.

: The authors declare no competing interests.

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