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Provision of Radiology Reports Simplified With Large Language Models to Patients With Cancer: Impact on Patient Satisfaction.
JCO Clin Cancer Inform
January 2025
Department of Radiology, Dr BRAIRCH, All India Institute of Medical Sciences, New Delhi, India.
Purpose: To explore the perceived utility and effect of simplified radiology reports on oncology patients' knowledge and feasibility of large language models (LLMs) to generate such reports.
Materials And Methods: This study was approved by the Institute Ethics Committee. In phase I, five state-of-the-art LLMs (Generative Pre-Trained Transformer-4o [GPT-4o], Google Gemini, Claude Opus, Llama-3.
Inferring the genetic relationships between unsupervised deep learning-derived imaging phenotypes and glioblastoma through multi-omics approaches.
Brief Bioinform
November 2024
Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China.
This study aimed to investigate the genetic association between glioblastoma (GBM) and unsupervised deep learning-derived imaging phenotypes (UDIPs). We employed a combination of genome-wide association study (GWAS) data, single-nucleus RNA sequencing (snRNA-seq), and scPagwas (pathway-based polygenic regression framework) methods to explore the genetic links between UDIPs and GBM. Two-sample Mendelian randomization analyses were conducted to identify causal relationships between UDIPs and GBM.
View Article and Find Full Text PDFTemperature-Dependent Magnetic Resonance Relaxation Behaviors in Porous Materials.
Phys Rev Lett
December 2024
University of New Brunswick, UNB MRI Centre, Department of Physics, Fredericton, New Brunswick, E3B 5A3, Canada.
We observe divergent temperature-dependent magnetic resonance relaxation behaviors across various brine-saturated porous materials. The paramagnetic and diamagnetic nature of the samples underlies these divergent behaviors. The temperature-dependent trends of the longitudinal T_{1} and transverse T_{2} relaxation times are systematically explained via distinct relaxation-diffusion regimes of Brownstein-Tarr theory.
View Article and Find Full Text PDFGenerative artificial intelligence enables the generation of bone scintigraphy images and improves generalization of deep learning models in data-constrained environments.
Eur J Nucl Med Mol Imaging
January 2025
Department of Biomedical Imaging and Image-guided Therapy, Division of Nuclear Medicine, Medical University of Vienna, Spitalgasse 23, Vienna, 1090, Austria.
Purpose: Advancements of deep learning in medical imaging are often constrained by the limited availability of large, annotated datasets, resulting in underperforming models when deployed under real-world conditions. This study investigated a generative artificial intelligence (AI) approach to create synthetic medical images taking the example of bone scintigraphy scans, to increase the data diversity of small-scale datasets for more effective model training and improved generalization.
Methods: We trained a generative model on Tc-bone scintigraphy scans from 9,170 patients in one center to generate high-quality and fully anonymized annotated scans of patients representing two distinct disease patterns: abnormal uptake indicative of (i) bone metastases and (ii) cardiac uptake indicative of cardiac amyloidosis.
Insights and Opportunities from Multimarker Evaluation of Heart Failure: Lessons from BIOSTAT-HF.
Curr Heart Fail Rep
January 2025
Division of Cardiovascular Medicine, Department of Medicine, University of California, 9394 Medical Center Drive, La Jolla, San Diego, CA, USA.
Purpose Of Review: Heart failure is a complex and heterogenous disease state that affects millions worldwide. Over recent decades, advancements in medical therapy and device implementation have significantly transformed the landscape of heart failure outcomes, while improvements in imaging modalities and greater accessibility to genome sequencing have led to increasing recognition of distinct heart failure endotypes. There is rising evidence to suggest all patients do not benefit equally from intensification of guideline directed medical therapy (GDMT).
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