Xiao-Zhou Lu

ORCID: 0000-0002-3013-5737
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About
Contact & Profiles
Research Areas
  • Liver Disease Diagnosis and Treatment
  • Cancer-related molecular mechanisms research
  • Radiomics and Machine Learning in Medical Imaging
  • Artificial Intelligence in Healthcare and Education
  • Thyroid Cancer Diagnosis and Treatment
  • Traditional Chinese Medicine Studies
  • Liver Disease and Transplantation
  • Healthcare and Venom Research
  • Hepatitis C virus research
  • Ovarian function and disorders

The First Affiliated Hospital, Sun Yat-sen University
2024

Sun Yat-sen University
2024

Women's Hospital, School of Medicine, Zhejiang University
2007

Background 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 diagnosis. Purpose To investigate the viability leveraging three publicly available LLMs to enhance consistency and diagnostic accuracy based standardized reporting, pathology as reference standard. Materials Methods US images thyroid nodules pathologic results were retrospectively...

10.1148/radiol.232255 article EN Radiology 2024-03-01

Tongue inspection, an essential diagnostic method in Traditional Chinese Medicine (TCM), has the potential for early-stage disease screening. This study aimed to evaluate effectiveness of deep learning-based analysis tongue images hepatic fibrosis

10.1016/j.jtcme.2024.03.010 article EN cc-by-nc-nd Journal of Traditional and Complementary Medicine 2024-03-06

Background Noninvasive tests can be used to screen patients with chronic liver disease for advanced fibrosis; however, the use of single may not adequate. Purpose To construct sequential clinical algorithms that include a US deep learning (DL) model and compare their ability predict fibrosis other noninvasive tests. Materials Methods This retrospective study included adult history or unexplained abnormal function test results who underwent B-mode between January 2014 September 2022 at three...

10.1148/radiol.231461 article EN Radiology 2024-04-01
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