Deep learning-enabled pelvic ultrasound images for accurate diagnosis of ovarian cancer in China: a retrospective, multicentre, diagnostic study

Autor: Yue Gao, MD, Shaoqing Zeng, MD, Xiaoyan Xu, ProfMD, Huayi Li, Shuzhong Yao, ProfPhD, Kun Song, ProfPhD, Xiao Li, BS, Lingxi Chen, PhD, Junying Tang, ProfPhD, Hui Xing, ProfPhD, Zhiying Yu, ProfPhD, Qinghua Zhang, ProfPhD, Shue Zeng, ProfPhD, Cunjian Yi, ProfPhD, Hongning Xie, ProfPhD, Xiaoming Xiong, BS, Guangyao Cai, MD, Zhi Wang, MS, Yuan Wu, MS, Jianhua Chi, BS, Xiaofei Jiao, BS, Yan Qin, MS, Xiaogang Mao, MD, Yu Chen, MD, Xin Jin, MD, Qingqing Mo, MD, Pingbo Chen, ProfMD, Yi Huang, ProfMD, Yushuang Shi, MD, Junmei Wang, ProfPhD, Yimin Zhou, MD, Shuping Ding, MD, Shan Zhu, MD, Xin Liu, ProfMD, Xiangyi Dong, MD, Lin Cheng, MD, Linlin Zhu, MD, Huanhuan Cheng, MD, Li Cha, MD, Yanli Hao, MS, Chunchun Jin, MD, Ludan Zhang, MD, Peng Zhou, BS, Meng Sun, MS, Qin Xu, MS, Kehua Chen, BS, Zeyan Gao, MS, Xu Zhang, BS, Yuanyuan Ma, MS, Yan Liu, MD, Liling Xiao, MS, Li Xu, MD, Lin Peng, BS, Zheyu Hao, MD, Mi Yang, MD, Yane Wang, MD, Hongping Ou, MD, Yongmei Jia, MD, Lihua Tian, MD, Wei Zhang, MD, Ping Jin, MS, Xun Tian, ProfMD, Lei Huang, MD, Zhen Wang, MD, Jiahao Liu, BS, Tian Fang, MS, Danmei Yan, BS, Heng Cao, BS, Jingjing Ma, MD, Xiaoting Li, MD, Xu Zheng, BS, Hua Lou, BS, Chunyan Song, BS, Ruyuan Li, BS, Siyuan Wang, BS, Wenqian Li, MD, Xulei Zheng, MD, Jing Chen, MD, Guannan Li, BS, Ruqi Chen, MS, Cheng Xu, MS, Ruidi Yu, MS, Ji Wang, MS, Sen Xu, MD, Beihua Kong, ProfPhD, Xing Xie, ProfPhD, Ding Ma, ProfPhD, Qinglei Gao, ProfPhD

Publikováno v: The Lancet: Digital Health, Vol 4, Iss 3, Pp e179-e187 (2022)

Summary: Background: Ultrasound is a critical non-invasive test for preoperative diagnosis of ovarian cancer. Deep learning is making advances in image-recognition tasks; therefore, we aimed to develop a deep convolutional neural network (DCNN) model

Externí odkaz: https://doaj.org/article/557389c903e04fe284a592be5931df32

The Added Value of Whole-Exome Sequencing for Anomalous Fetuses With Detailed Prenatal Ultrasound and Postnatal Phenotype

Autor: Miao He, Liu Du, Hongning Xie, Lihe Zhang, Yujun Gu, Ting Lei, Ju Zheng, Dan Chen

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

ObjectivesThe objective of the study was to explore the added value of whole-exome sequencing (WES) in abnormal fetuses with detailed prenatal ultrasound and postnatal phenotype with normal karyotype and chromosomal microarray analysis (CMA).MethodsP

Externí odkaz: https://doaj.org/article/451e6e1a6fb54171908b8c7044f5cef6

Identification of compound heterozygous variants in the noncoding RNU4ATAC gene in a Chinese family with two successive foetuses with severe microcephaly

Autor: Ye Wang, Xueli Wu, Liu Du, Ju Zheng, Songqing Deng, Xin Bi, Qiuyan Chen, Hongning Xie, Claude Férec, David N. Cooper, Yanmin Luo, Qun Fang, Jian-Min Chen

Publikováno v: Human Genomics, Vol 12, Iss 1, Pp 1-8 (2018)

Abstract Background Whole-exome sequencing (WES) over the last few years has been increasingly employed for clinical diagnosis. However, one caveat with its use is that it inevitably fails to detect disease-causative variants that occur within noncod

Externí odkaz: https://doaj.org/article/20163f49eb25449e914778b536efc332

DeepGA for automatically estimating fetal gestational age through ultrasound imaging

Autor: Tingting Dan, Xijie Chen, Miao He, Hongmei Guo, Xiaoqin He, Jiazhou Chen, Jianbo Xian, Yu Hu, Bin Zhang, Nan Wang, Hongning Xie, Hongmin Cai

Publikováno v: Artificial Intelligence in Medicine. 135:102453

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::db8c7aad0ac5b33dd2304ece3b99a454
https://doi.org/10.1016/j.artmed.2022.102453

Ultrasonic Image Segmentation Method based on Improved Fully Convolution Network

Autor: Kaiping Feng, Hai Ye, Hongning Xie, Lihong Luo

Publikováno v: Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::df9ba6515c10f7bb562aed0ba2710214
https://doi.org/10.2991/icmeit-19.2019.93