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 longitudinal study utilizing advanced MAP-MRI techniques found no significant microstructural alterations in acute mild traumatic brain injury (mTBI) patients compared to controls, suggesting that such injuries may not be detectable with current diffusion MRI methods despite the presence of clinical symptoms.
This study demonstrates that a soft voting ensemble of MedGemma-4B-it large language models significantly improves diagnostic accuracy and discriminatory performance for paediatric pneumonia detection in chest X-rays compared to individual agents, offering a promising privacy-preserving tool for clinical triage and decision support.
This study presents MoEMIL, a multi-task deep learning model that integrates pretreatment MRI and whole slide images to outperform traditional staging and single-modality approaches in predicting induction chemotherapy response and overall survival for patients with locally advanced nasopharyngeal carcinoma, thereby offering a promising tool for personalized treatment decision-making.
This paper demonstrates that a self-supervised Video Diffusion Encoder can effectively detect Myofascial Pain Syndrome in the upper trapezius muscle using B-mode ultrasound videos from a small prospective cohort, offering a data-efficient alternative to conventional deep learning methods that require large annotated datasets.
This paper introduces Spectral Normative Modeling (SNM), a computationally efficient framework that leverages brain eigenmodes to generate high-resolution, adaptable normative growth charts for brain structure, enabling precise characterization of individual neurodevelopmental trajectories and neurodegenerative patterns like Alzheimer's disease.
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.