42 papers
Radiology and imaging serve as the eyes of modern medicine, allowing doctors to peer inside the human body without making a single incision. This rapidly evolving field uses technologies like X-rays, MRI scans, and ultrasound to detect diseases, guide treatments, and monitor patient recovery. As new research emerges, these visual tools become increasingly sophisticated, offering deeper insights into conditions ranging from broken bones to complex neurological disorders.
At Gist.Science, we bridge the gap between raw scientific data and public understanding by processing every new preprint in this category from medRxiv. Our team translates these complex studies into both plain-language overviews and detailed technical summaries, ensuring that breakthroughs in medical imaging are accessible to everyone, from students to specialists. Below are the latest papers in radiology and imaging, ready for you to explore.
This study demonstrates that ensemble regression models trained on automated image quality metrics can effectively and transparently predict expert visual ratings for medical image translation, achieving high agreement with human consensus while offering a scalable solution for clinical quality control.
This multi-dataset validation study demonstrates that technical acquisition parameters, specifically radiograph view type, are the primary drivers of performance disparities in chest X-ray AI systems, significantly outweighing the contributions of demographic factors like age and sex, thereby necessitating a shift in regulatory frameworks to prioritize acquisition parameter auditing alongside demographic subgroup analysis.
This study provides multimodal MRI evidence that active second language immersion enhances glymphatic system function—improving brain-CSF coordination and optimizing choroid plexus structure—thereby suggesting a neuroprotective mechanism through improved brain waste clearance.
This study demonstrates that Standard Model Imaging (SMI) and Diffusion Tensor Imaging (DTI) both effectively characterize microstructural alterations across various white matter tissue classes in multiple sclerosis, with a combined multi-model approach yielding the highest classification performance for distinguishing lesion types and subtle tissue changes.
This study demonstrates that multi-b shell diffusion MRI combined with multiple advanced diffusion models provides superior microstructural characterization and discriminative performance for multiple sclerosis lesions compared to conventional single-shell approaches, despite the continued challenge of distinguishing subtle normal-appearing tissue changes.
This clinical pilot study demonstrates that incorporating polygenic risk scores into breast cancer screening for women aged 40–49 can effectively stratify risk and personalize screening recommendations without inducing significant anxiety among participants.
This paper outlines the protocol for the AIMS Norway randomized controlled trial, which aims to determine if an AI-stratified mammography reading strategy—where low-risk cases are read by a single radiologist and high-risk cases by two—is non-inferior to standard double reading in terms of screen-detected breast cancer rates.
This exploratory study demonstrates that photoacoustic imaging can non-invasively detect significant differences in muscle water, lipid, and hemoglobin ratios between patients with m.3243A>G mitochondrial myopathy and healthy controls, highlighting its potential as a novel biomarker for monitoring disease progression.
This study reveals that increased functional connectivity within white matter networks, but not between white and gray matter, is significantly associated with social impairment severity in individuals with Autism Spectrum Disorder, offering new insights into the neural mechanisms underlying the disorder.
This study demonstrates that a transfer learning-based nnU-Net model, pretrained on TOF-MRA and fine-tuned on 7T 4D Flow MRI data, outperforms existing deep learning architectures in intracranial artery segmentation and provides the most accurate, automated hemodynamic quantification, thereby confirming that segmentation precision directly impacts the reliability of derived flow metrics.