Category Ranking
98%
Total Visits
921
Avg Visit Duration
2 minutes
Citations
20
Article Abstract
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12031886 | PMC |
| http://dx.doi.org/10.1002/ctm2.70241 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Machine learning for myocarditis diagnosis using cardiovascular magnetic resonance: a systematic review, diagnostic test accuracy meta-analysis, and comparison with human physicians.
Int J Cardiovasc Imaging
September 2025
Klinikum Fürth, Friedrich-Alexander-University Erlangen- Nürnberg, Fürth, Germany.
Myocarditis is an inflammation of heart tissue. Cardiovascular magnetic resonance imaging (CMR) has emerged as an important non-invasive imaging tool for diagnosing myocarditis, however, interpretation remains a challenge for novice physicians. Advancements in machine learning (ML) models have further improved diagnostic accuracy, demonstrating good performance.
View Article and Find Full Text PDFPrediction of double expression status of primary CNS lymphoma using multiparametric MRI radiomics combined with habitat radiomics: a double-center study.
J Neurooncol
September 2025
Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, 266003, Shandong, China.
Rationale And Objectives: Double expression lymphoma (DEL) is an independent high-risk prognostic factor for primary CNS lymphoma (PCNSL), and its diagnosis currently relies on invasive methods. This study first integrates radiomics and habitat radiomics features to enhance preoperative DEL status prediction models via intratumoral heterogeneity analysis.
Materials And Methods: Clinical, pathological, and MRI imaging data of 139 PCNSL patients from two independent centers were collected.
Robotic surgery in healthcare: current challenges, technological advances, and global implementation prospects.
J Robot Surg
September 2025
Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham, ME7 5NY, UK.
Robotic surgery has transformed the field of surgery, offering enhanced precision, minimal invasiveness, and improved patient outcomes. This narrative review explores the multifaceted aspects of robotic surgery, examining the challenges, recent advances, and future prospects for its integration into healthcare. Our comprehensive analysis of 48 studies reveals significant geographic disparities in robotic surgery research and implementation, with 68.
View Article and Find Full Text PDFRadiomics nomogram from multiparametric magnetic resonance imaging for preoperative prediction of substantial lymphovascular space invasion in endometrial cancer.
Abdom Radiol (NY)
September 2025
Department of Radiology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics and Gynecology and Pediatrics, Fujian Medical University, Fuzhou, China.
Background: We aimed to develop and validate a radiomics-based machine learning nomogram using multiparametric magnetic resonance imaging to preoperatively predict substantial lymphovascular space invasion in patients with endometrial cancer.
Methods: This retrospective dual-center study included patients with histologically confirmed endometrial cancer who underwent preoperative magnetic resonance imaging (MRI). The patients were divided into training and test sets.
Machine Learning Insights Into Social Determinants Driving Child Abuse in Pediatric Traumatic Brain Injury.
Neurosurgery
September 2025
Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Background And Objectives: Social determinants of health (SDOH) are key drivers of health inequities, shaping disparities in patient outcomes that must be addressed. This study examines the association between SDOH and suspected child abuse (SCA) in pediatric patients sustaining traumatic brain injury (TBI), leveraging newly proposed Centers for Disease Control and Prevention (CDC)/PLACES measures to identify the most contributing measure to SCA.
Methods: A retrospective review of our institutional database (2016-2023) identified pediatric TBI cases (18 years and younger) using International Classification of Diseases, 10th Revision codes based on a modified CDC framework.