62 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 utilizes 3D mesh-based shape analysis of gluteus maximus MRI data from the UK Biobank to reveal that spatially localized muscle remodelling, rather than global volume or fat fraction alone, provides sex-specific biomarkers for metabolic dysfunction and type 2 diabetes risk.
This study presents and evaluates a deployable, explainable deep learning system based on DenseNet121 and Grad-CAM that achieves accurate tuberculosis detection from chest X-rays on both desktop and mobile platforms, demonstrating its potential as an offline decision support tool for resource-constrained healthcare settings.
This study analyzes FDA-authorized radiology AI devices to demonstrate how low disease prevalence creates a false positive paradox that undermines positive predictive value, arguing for the mandatory disclosure of false discovery and omission rates to guide clinically and ethically appropriate AI selection.
This study demonstrates that the cross-scanner reliability of brain MRI foundation model embeddings varies significantly based on pretraining strategy, with models incorporating biological metadata achieving scanner-robust performance comparable to traditional morphometric baselines, while purely self-supervised models exhibit substantial scanner-induced variance.
This study audits 543 pediatric CT scans at a South African tertiary hospital, finding that radiation doses generally align with international safety standards but are slightly higher during after-hours shifts, highlighting the need for consistent staff training and standardized protocols.
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.