Publications by authors named "Ze-Rong Huang"
Article Synopsis
- Advanced liver fibrosis is a crucial stage in chronic liver disease (CLD) assessment, influencing treatment strategies and disease progression evaluation.
- This study introduces an innovative diagnostic method called GLCV-Net, which combines global and local features from ultrasound images using three main components: a feature extractor, a local feature selector, and a fusion module.
- The model demonstrated strong predictive performance, achieving over 86% accuracy in internal validations and confirming its effectiveness on external datasets, highlighting its potential for accurate, non-invasive diagnosis of advanced liver fibrosis.
Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis; however, the use of single tests may not be adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability to predict advanced liver fibrosis with that of other noninvasive tests. Materials and Methods This retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode US of the liver between January 2014 and September 2022 at three health care facilities.
View Article and Find Full Text PDFBackground Large language models (LLMs) hold substantial promise for medical imaging interpretation. However, there is a lack of studies on their feasibility in handling reasoning questions associated with medical diagnosis. Purpose To investigate the viability of leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy in medical imaging based on standardized reporting, with pathology as the reference standard.
View Article and Find Full Text PDFArticle Synopsis
- Lesion localization and tracking in medical imaging are enhanced by Contrast-Enhanced Ultrasound (CEUS), which provides better images of blood flow compared to traditional methods, but current tracking systems struggle to fully utilize this data and handle respiratory motion.
- The proposed adaptive-weighted dual mapping (ADMNet) includes a new Multimodal Atrous Attention Fusion (MAAF) module to adjust the focus between B-mode and CEUS images, and a Respiratory Motion Compensation (RMC) module to correct movement errors caused by breathing.
- Testing on two large CEUS datasets shows that ADMNet surpasses existing tracking systems, highlighting the effectiveness of its components and opening up new opportunities for research in
Objective: The objective of the work described here was to assess the value of the combination of pre-operative multimodal data-including clinical data, contrast-enhanced ultrasound (CEUS) information and liver stiffness measurement (LSM) based on 2-D shear wave elastography (SWE)-in predicting early (within 1 y) and late (after 1 y) recurrence of hepatocellular carcinoma (HCC) after curative treatment.
Methods: We retrospectively included 101 patients with HCC who met the Milan criteria and received curative treatment. The multimodel data from clinical parameters, LSM by 2-D SWE and CEUS enhancement patterns were collected.
Objective: The main aim of this study was to analyze the performance of different artificial intelligence (AI) models in endoscopic colonic polyp detection and classification and compare them with doctors with different experience.
Methods: We searched the studies on Colonoscopy, Colonic Polyps, Artificial Intelligence, Machine Learning, and Deep Learning published before May 2020 in PubMed, EMBASE, Cochrane, and the citation index of the conference proceedings. The quality of studies was assessed using the QUADAS-2 table of diagnostic test quality evaluation criteria.
[Neuropsychological development of human immunodeficiency virus-exposed uninfected infants/young children].
Zhongguo Dang Dai Er Ke Za Zhi
September 2022
Objectives: To investigate the level of neuropsychological development in human immunodeficiency virus (HIV)-exposed uninfected (HEU) infants/young children and the influence of maternal HIV infection on the neuropsychological development of HEU infants/young children.
Methods: A total of 141 HEU infants/young children, aged 0-18 months and born to HIV-infected mothers, who were managed in four maternal and child health care hospitals in Yunnan Province of China from June 2019 to December 2020 and met the inclusion criteria were enrolled as the HEU group. A total of 141 HIV-unexposed uninfected (HUU) infants/young children who were born to healthy mothers and managed in the same hospitals, matched at a ratio of 1:1 based on sex, age, method of birth, birth weight, and gestational age, were enrolled as controls.
Background: Heterogeneity within the tumor may cause large heterogeneity in quantitative perfusion parameters. Three-dimensional contrast-enhanced ultrasound (3D-CEUS) can show the spatial relationship of vascular structure after post-acquisition reconstruction and monodisperse bubbles can resonate the ultrasound pulse, resulting in the increase in sensitivity of CEUS imaging.
Objectives: To evaluate whether the combination of 3D-CEUS and monodisperse microbubbles could reduce the heterogeneity of quantitative CEUS.