Stay Ahead of the Curve in Radiology AI.
RadAI Slice is your weekly intelligence briefing on the most critical developments at the intersection of radiology and artificial intelligence. Stop searching. Start leading.
Lastest Issues
The latest developments in Radiology & AI.
Your Weekly Slice of Innovation
Each issue is precisely structured to give you exactly what you need. No fluff, just facts and forward-looking insights.
Recent Industry News
The Latest Research
FDA Approvals Database
Latest News & Trends
AI Model Accurately Flags Brain MRI Abnormalities, Eases Radiologist Burden
A new AI model can accurately flag brain abnormalities in MRI scans, potentially streamlining triage and diagnosis for radiologists.
Most Radiology AI Users Lack Clear Evidence of Financial ROI
Survey finds over 75% of radiology organizations using AI lack clear, quantified ROI data.
AI Ultrasound Tool May Cut Unnecessary Breast Biopsies by 60%
An FDA-cleared AI tool for breast ultrasound may reduce unnecessary biopsies of benign breast lesions by about 60%.
From the Research Hub
QENNA: A quantum-enhanced neural network for early Alzheimer's detection using magnetic resonance imaging.
Early detection of Alzheimer's disease (AD) is essential for effective clinical intervention and disease management. However, conventional Deep Learning (DL) methods face limitations in analyzing complex brain magnetic resonance imaging (MRI), especially when training data are scarce. In this study, we propose a Quantum-Enhanced Neural Network Architecture (QENNA) that integrates quantum convolutional layers with classical deep learning to improve diagnostic accuracy in early AD detection. The model also incorporates quantum data augmentation strategies, including Quantum Generative Adversarial Networks (QGANs) and quantum random walks, to generate high-fidelity synthetic MRI scans and address training data limitations. Experiments on two public MRI datasets demonstrate that QENNA achieves up to 93.0 % accuracy and 96.0 % Area Under the Curve (AUC), outperforming state-of-the-art classical models. Ablation studies confirm that the quantum components substantially enhance performance. These results suggest that quantum-enhanced learning frameworks can significantly advance Artificial Intelligence (AI)-driven diagnostic tools for neurodegenerative disorders and support scalable, early-stage AD screening in clinical practice.
Multimodal framework for TACE treatment response prediction in patients with hepatocellular carcinoma.
Transarterial chemoembolization (TACE) is a first-line treatment for intermediate-stage hepatocellular carcinoma (HCC) that can cause side effects. An accurate prediction of TACE response is important to improve clinical outcomes and avoid unnecessary toxicity. This study pursues a dual objective: to propose a standardized evaluation pipeline that enables reproducible benchmarking of state-of-the-art approaches on publicly available datasets, including both internal and external validation with public dataset, and to introduce a novel multimodal framework that integrates clinical variables, radiomic and deep features extracted from CT scans using the Vision Transformer MedViT to predict treatment response. Experiments were conducted using two publicly available datasets, the HCC-TACE-Seg, used as training and internal validation sets, and the WAW-TACE cohort, used as external validation set. The results demonstrated that the proposed method outperforms existing approaches. Independent validation on the external WAW-TACE dataset achieved promising results, confirming the robustness of the model and its potential to support treatment planning.
Supervoxel-based image-to-biomarker conversions - An initial study on morphological age prediction from whole-body MRI and its clinical relevance in the UK Biobank
Biological aging remains a central focus of research, from the scale of sub-cellular processes to whole-organism tissue morphology and function. In this work, we developed a novel and quantitatively interpretable method for the prediction of variables, such as age, from tomographic medical images. The method uses supervoxels (whose granularity is selected by the user), standardized through inter-subject image registration, and tissue-specific feature extractions from each supervoxel to convert all image data collected into a set of well-defined imaging biomarkers. We applied the method to age prediction, using linear modelling and whole-body water-fat MRI data of 38,235 subjects in the multicentre UK Biobank study, resulting in predictions representing morphological age (MA). We observed state-of-the-art whole-body age prediction performance on a held-out test set with a mean absolute error of 1.951/2.057 years, and R2 of 0.884/0.892 for females/males, respectively. The method was observed to outperform both previously reported CNN-based results from the UK Biobank and predictions from explicit biomarkers from a multi-organ/tissue segmentation approach, in a direct comparison. The interpretability of the method enabled a detailed analysis of the body-wide associations with age. Volumes of the aorta, regional muscle, bone marrow, and adipose tissue depots, and lean tissue fat content were of particular importance. The predicted MAs were of clinical relevance as they were significantly related to both type 2 diabetes and all-cause mortality. A key finding was an accuracy/utility trade-off where the more parsimonious models showed lower chronological age (CA) predictive performance but higher clinical relevance and interpretability. The proposed method facilitates automated image-to-biomarker conversions and predictions based on subsets of anatomies, tissues, and image features, for potential application in numerous future medical studies. This study was funded by the Swedish Heart-Lung Foundation, the Swedish Research Council, EXODIAB, and Uppsala Diabetes Center. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and Google Scholar with search terms: "uk biobank age prediction" and "whole-body mr age prediction". Two studies involving age prediction from whole-body MR images were found, primarily using black-box AI methods, and both achieved good performance on chronological age (CA) prediction. For other imaging sequences, such as brain images, high-performance AI models have been developed. For other imaging sequences and other cohorts, age prediction using features derived from image segmentation has also been explored, commonly exhibiting lower CA prediction performance but higher interpretability. Added value of this studyThe study introduces a novel general-purpose image-to-biomarker method: tissue-specific standardized supervoxel-based prediction (TS-SSP), relying on the definition of well-defined imaging-biomarkers through inter-subject image registration, tissue-specific supervoxel-based feature extraction, and using linear models to derive an interpretable morphological age (MA) prediction from a large dataset of whole-body MR images. The study evaluates the proposed method in comparison to segmentation-based methods, and indirectly with deep learning-based methods, and explores the interplay between model complexity, CA prediction performance, and clinical relevance. Interpretability analysis reveals spatially-resolved tissue- and organ-level associations with age and both fat content and tissue volume. Implications of all the available evidenceThe study shows that it is feasible to achieve age prediction with the interpretability of segmentation-based approaches with higher spatial resolution and the high CA prediction performance of deep learning-based approaches (or even higher performance) simultaneously through the use of tissue-specific standardized supervoxel-based prediction relying on image registration and linear models. The study uncovers a trade-off between the CA prediction performance and relevance to diabetes and mortality (and interpretability), underscoring the need for rigorous evaluation of age prediction methods against clinically relevant outcomes. Previously known associations with age were found, such as aorta volume and muscle volume, in addition to detailed tissue-level associations.
From the RadAI FDA Approvals Database
Archy Dental, Inc.
Archy Dental Imaging
Archy Dental Imaging is a radiological image processing system designed for dental applications. It assists clinicians by enhancing and analyzing dental radiographs to improve diagnostic accuracy and patient care.
Siemens Shenzhen Magnetic Resonance , Ltd.
MAGNETOM Free.Max; MAGNETOM Free.Star
MAGNETOM Free.Max and MAGNETOM Free.Star are MRI systems provided by Siemens Shenzhen Magnetic Resonance that help clinicians capture detailed images of the body's internal structures. These advanced MRI systems support medical diagnosis and treatment planning by providing high-quality nuclear magnetic resonance images.
Siemens Healthineers Ag
syngo.MR Applications (VB80)
syngo.MR Applications (VB80) by Siemens Healthineers is an advanced image processing system designed for radiology. It helps clinicians by processing and enhancing MRI images, facilitating better diagnosis and analysis of medical conditions using magnetic resonance imaging.
The Sharpest Insights, Effortlessly
Save Time
We scour dozens of sources so you don't have to. Get all the essential information in a 5-minute read.
Stay Informed
Never miss a critical update. Understand the trends shaping the future of your practice and research.
Gain an Edge
Be the first to know about the tools and technologies that matter, from clinical practice to academic research.
Ready to Sharpen Your Edge?
Join hundreds of your peers who rely on RadAI Slice. Get the essential weekly briefing that empowers you to navigate the future of radiology.
We respect your privacy. Unsubscribe at any time.