Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1097/NCQ.0b013e31821e08ee | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
MRI-based deep learning radiomics to differentiate dual-phenotype hepatocellular carcinoma from HCC and intrahepatic cholangiocarcinoma: a multicenter study.
Insights Imaging
January 2025
Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, China.
Objectives: To develop and validate radiomics and deep learning models based on contrast-enhanced MRI (CE-MRI) for differentiating dual-phenotype hepatocellular carcinoma (DPHCC) from HCC and intrahepatic cholangiocarcinoma (ICC).
Methods: Our study consisted of 381 patients from four centers with 138 HCCs, 122 DPHCCs, and 121 ICCs (244 for training and 62 for internal tests, centers 1 and 2; 75 for external tests, centers 3 and 4). Radiomics, deep transfer learning (DTL), and fusion models based on CE-MRI were established for differential diagnosis, respectively, and their diagnostic performances were compared using the confusion matrix and area under the receiver operating characteristic (ROC) curve (AUC).
The enlarged perivascular spaces in the hippocampus is associated with memory function in patients with type 2 diabetes mellitus.
Sci Rep
January 2025
Department of MRI, Shaanxi Provincial People's Hospital, Xi'an, China.
Early detection of cognitive dysfunction in patients with type 2 diabetes mellitus (T2DM) is important for preventive measures due to the lack of effective treatments. The purpose of this study is to investigate the relationship between enlarged perivascular space in the hippocampus (H-EPVS) and cognitive performance in patients with T2DM, and to determine whether it can serve as an imaging marker for cognitive dysfunction. 66 T2DM patients with cognitive impairment (T2DM-CI) and 71 T2DM patients with normal cognitive function (T2DM-NC) underwent cranial MRI scans and comprehensive neuropsychological assessments.
View Article and Find Full Text PDFDeep Learning for Lumbar Disc Herniation Diagnosis and Treatment Decision Making Using MRI Images: A Retrospective Study.
World Neurosurg
January 2025
Department of Spine Surgery, The Affiliated Taizhou People's Hospital of Nanjing Medical University.
Background: Lumbar disc herniation (LDH) is a common cause of back and leg pain. Diagnosis relies on clinical history, physical exam, and imaging, with magnetic resonance imaging (MRI) being an important reference standard. While artificial intelligence (AI) has been explored for MRI image recognition in LDH, existing methods often focus solely on disc herniation presence.
View Article and Find Full Text PDFDeep learning reconstruction of zero-echo time sequences to improve visualization of osseous structures and associated pathologies in MRI of cervical spine.
Insights Imaging
January 2025
Institute of Diagnostic and Interventional Radiology, University Hospital Zurich (USZ), Zurich, Switzerland.
Objectives: To determine whether deep learning-based reconstructions of zero-echo-time (ZTE-DL) sequences enhance image quality and bone visualization in cervical spine MRI compared to traditional zero-echo-time (ZTE) techniques, and to assess the added value of ZTE-DL sequences alongside standard cervical spine MRI for comprehensive pathology evaluation.
Methods: In this retrospective study, 52 patients underwent cervical spine MRI using ZTE, ZTE-DL, and T2-weighted 3D sequences on a 1.5-Tesla scanner.
Artificial intelligence for brain neuroanatomical segmentation in magnetic resonance imaging: A literature review.
J Clin Neurosci
January 2025
Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, NSW, Australia; Computational NeuroSurgery (CNS) Lab, Macquarie University, NSW, Australia.
Purpose: This literature review aims to synthesise current research on the application of artificial intelligence (AI) for the segmentation of brain neuroanatomical structures in magnetic resonance imaging (MRI).
Methods: A literature search was conducted using the databases Embase, Medline, Scopus, and Web of Science, and captured articles were assessed for inclusion in the review. Data extraction was performed for the summary of the AI model used, and key findings of each article, advantages and disadvantages were identified.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!