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Akademický článek

ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection

Autor: Zeyang Yao, Wen Xie, Jiawei Zhang, Yuhao Dong, Hailong Qiu, Haiyun Yuan, Qianjun Jia, Tianchen Wang, Yiyi Shi, Jian Zhuang, Lifeng Que, Xiaowei Xu, Meiping Huang

Publikováno v: Frontiers in Physiology, Vol 12 (2021)

Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence, and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diag

Externí odkaz: https://doaj.org/article/85b3ba4a623b4915a6fe50769fdc07e9

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Predicting reintervention after thoracic endovascular aortic repair of Stanford type B aortic dissection using machine learning

Autor: Lifeng Que, Hui Liu, Weiqi Chen, Qianjun Jia, Jian Zhuang, Jinglei Li, Yue Xi, Yuhao Dong, Meiping Huang

Publikováno v: European Radiology. 32:355-367

To construct models for predicting reintervention after thoracic endovascular aortic repair (TEVAR) of Stanford type B aortic dissection (TBAD). A total of 192 TBAD patients who underwent TEVAR were included; 68 (35.4%) had indications for reinterven

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::af33c5a88f1290259bf0afa8188fb81c
https://doi.org/10.1007/s00330-021-07849-2

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Using Machine Learning to Predict Five-Year Reintervention Risk in Type B Aortic Dissection Patients After Thoracic Endovascular Aortic Repair

Autor: Weiyuan Lin, Guisen Lin, Meiping Huang, Lifeng Que, M. D. Zhicheng Du, Zhu Liang Yu, M. D. Hui Liu, Rui Chen, Qiyang Lu

Publikováno v: Journal of Medical Imaging and Health Informatics. 11:1560-1567

Purpose: Type B aortic dissection (TBAD) is a high-risk disease, commonly treated with thoracic endovascular aortic repair (TEVAR). However, for the long-term follow-up, it is associated with a high 5-year reintervention rate for patients after TEVAR

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f560196275051359ce2f080fc7e43431
https://doi.org/10.1166/jmihi.2021.3813

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Myocardial Mechanics Parameters That Predict Left Ventricular Outflow Tract Obstruction in Patients With Hypertrophic Cardiomyopathy: A Cardiovascular Magnetic Resonance Feature Tracking Analysis

Autor: Jian Zhuang, Jinglei Li, Haiyun Yuan, Lei Huang, Jijin Lin, Lifeng Que, Hui Liu, Qianjun Jia, Yue Xi, Yuhao Dong

Publikováno v: Journal of Computer Assisted Tomography. 45:65-72

OBJECTIVE To identify left ventricular (LV) myocardial mechanics predictors of LV outflow tract obstruction (LVOTO) in patients with hypertrophic cardiomyopathy (HCM). METHODS Thirty-nine adults with HCM and 21 controls underwent cardiovascular magne

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e12b893cfb2466d2c6896d532c8f04d6
https://doi.org/10.1097/rct.0000000000000977

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ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection

Autor: Qianjun Jia, Yuhao Dong, Jiawei Zhang, Yiyi Shi, Tianchen Wang, Lifeng Que, Wen Xie, Jian Zhuang, Meiping Huang, Xiaowei Xu, Haiyun Yuan, Zeyang Yao, Hailong Qiu

Publikováno v: Frontiers in Physiology, Vol 12 (2021)
Frontiers in Physiology

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4474d4d48500acfce8b42240b7b9028e
https://doi.org/10.3389/fphys.2021.732711

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Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

Autor: Qianjun Jia, Jiahua Li, Jijin Lin, Lifeng Que, Dewen Zeng, Meiping Huang, Jian Zhuang, Lei Huang, Haiyun Yuan, Yue Xi, Yuhao Dong

Publikováno v: European Radiology. 30:1369-1377

This study aimed to develop non-invasive machine learning classifiers for predicting post–Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure (mPAP) > 15 mmHg based on preoperative cardiac computed tomography (CT). Th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fbb25426834ada0ada3e399ecd7f97c7
https://doi.org/10.1007/s00330-019-06502-3

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Predicting reintervention after thoracic endovascular aortic repair of Stanford type B aortic dissection using machine learning

Autor: Yuhao, Dong, Lifeng, Que, Qianjun, Jia, Yue, Xi, Jian, Zhuang, Jinglei, Li, Hui, Liu, Weiqi, Chen, Meiping, Huang

Publikováno v: European radiology. 32(1)

To construct models for predicting reintervention after thoracic endovascular aortic repair (TEVAR) of Stanford type B aortic dissection (TBAD).A total of 192 TBAD patients who underwent TEVAR were included; 68 (35.4%) had indications for reintervent

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=pmid________::9bec6bcd339f646bc3f9cea24f6215b0
https://pubmed.ncbi.nlm.nih.gov/34156553

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