Výsledky vyhledávání - "Yu Hua Dean Fang"

Akademický článek

Associations between femoral 3D curvature and sagittal imbalance of spine

Autor: Chien‐Hsiung Lo, Yu‐Hua Dean Fang, Jing‐Yao Wang, Tzu‐Ping Yu, Hao‐Chun Chuang, Yuan‐Fu Liu, Chao‐Jui Chang, Cheng‐Li Lin

Publikováno v: JOR Spine, Vol 7, Iss 1, Pp n/a-n/a (2024)

Abstract Background The sagittal imbalance (SI) of spine triggers compensatory mechanisms (CMs) of lower extremity (LE) to restore trunk balance. These CMs can cause long‐period stress on the femur and may possibly alter the femoral morphology. Thi

Externí odkaz: https://doaj.org/article/3ad0a16db4324982a0f56ed5cdf3456e

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Akademický článek

Understanding post-surgical decline in left ventricular function in primary mitral regurgitation using regression and machine learning models

Autor: Jingyi Zheng, Yuexin Li, Nedret Billor, Mustafa I. Ahmed, Yu-Hua Dean Fang, Betty Pat, Thomas S. Denney, Louis J. Dell’Italia

Publikováno v: Frontiers in Cardiovascular Medicine, Vol 10 (2023)

BackgroundClass I echocardiographic guidelines in primary mitral regurgitation (PMR) risks left ventricular ejection fraction (LVEF) 60%. There are no models predicting LVEF

Externí odkaz: https://doaj.org/article/43cc44bd949545e598bc4a8ab8a0cbb8

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Akademický článek

Development and validation of a prognostic model incorporating [18F]FDG PET/CT radiomics for patients with minor salivary gland carcinoma

Autor: Nai-Ming Cheng, Cheng-En Hsieh, Yu-Hua Dean Fang, Chun-Ta Liao, Shu-Hang Ng, Hung-Ming Wang, Wen-Chi Chou, Chien-Yu Lin, Tzu-Chen Yen

Publikováno v: EJNMMI Research, Vol 10, Iss 1, Pp 1-12 (2020)

Abstract Objectives The aim of this study was to develop and validate a prognostic model incorporating [18F]FDG PET/CT radiomics for patients of minor salivary gland carcinoma (MSGC). Methods We retrospectively reviewed the pretreatment [18F]FDG PET/

Externí odkaz: https://doaj.org/article/55238e3517e240dd82ae090dfb7132d4

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Akademický článek

Image Quantification for TSPO PET with a Novel Image-Derived Input Function Method

Autor: Yu-Hua Dean Fang, Jonathan E. McConathy, Talene A. Yacoubian, Yue Zhang, Richard E. Kennedy, David G. Standaert

Publikováno v: Diagnostics, Vol 12, Iss 5, p 1161 (2022)

There is a growing interest in using 18F-DPA-714 PET to study neuroinflammation and microglial activation through imaging the 18-kDa translocator protein (TSPO). Although quantification of 18F-DPA-714 binding can be achieved through kinetic modeling

Externí odkaz: https://doaj.org/article/1e49b13d2f894359a74239f988c388b0

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Akademický článek

Clinical application of mask region-based convolutional neural network for the automatic detection and segmentation of abnormal liver density based on hepatocellular carcinoma computed tomography datasets

Autor: Ching-Juei Yang, Chien-Kuo Wang, Yu-Hua Dean Fang, Jing-Yao Wang, Fong-Chin Su, Hong-Ming Tsai, Yih-Jyh Lin, Hung-Wen Tsai, Lee-Ren Yeh

Publikováno v: PLoS ONE, Vol 16, Iss 8 (2021)

The aim of the study was to use a previously proposed mask region–based convolutional neural network (Mask R-CNN) for automatic abnormal liver density detection and segmentation based on hepatocellular carcinoma (HCC) computed tomography (CT) datas

Externí odkaz: https://doaj.org/article/f87d1a64747940818bc7bcd4596f5563

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Akademický článek

An MRI-Based Clinical-Perfusion Model Predicts Pathological Subtypes of Prevascular Mediastinal Tumors

Autor: Chia-Ying Lin, Yi-Ting Yen, Li-Ting Huang, Tsai-Yun Chen, Yi-Sheng Liu, Shih-Yao Tang, Wei-Li Huang, Ying-Yuan Chen, Chao-Han Lai, Yu-Hua Dean Fang, Chao-Chun Chang, Yau-Lin Tseng

Publikováno v: Diagnostics, Vol 12, Iss 4, p 889 (2022)

This study aimed to build machine learning prediction models for predicting pathological subtypes of prevascular mediastinal tumors (PMTs). The candidate predictors were clinical variables and dynamic contrast–enhanced MRI (DCE-MRI)–derived perfu

Externí odkaz: https://doaj.org/article/a757b6cb0ce14c37aa00518bb532b00a

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Akademický článek

Generative Adversarial Network (GAN) for Automatic Reconstruction of the 3D Spine Structure by Using Simulated Bi-Planar X-ray Images

Autor: Ching-Juei Yang, Cheng-Li Lin, Chien-Kuo Wang, Jing-Yao Wang, Chih-Chia Chen, Fong-Chin Su, Yin-Ju Lee, Chun-Chung Lui, Lee-Ren Yeh, Yu-Hua Dean Fang

Publikováno v: Diagnostics, Vol 12, Iss 5, p 1121 (2022)

In this study, we modified the previously proposed X2CT-GAN to build a 2Dto3D-GAN of the spine. This study also incorporated the radiologist’s perspective in the adjustment of input signals to prove the feasibility of the automatic production of th

Externí odkaz: https://doaj.org/article/05776ddebed34428992573c7abff0bb5

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