AI Blog

Avoiding Radiology’s Boeing 737 Max moment

Raymond Geis March 2025

Recently, Kemper et al. published an insightful paper on the challenges and opportunities of health technology assessment (HTA) models to evaluate the value, safety, and trustworthiness of radiology computer vision AI (RCVAI) tools used in daily practice. Formal HTA frameworks are theoretically accurate and help define complex issues, but they are ideal approaches. The real world is messier. Ideal descriptions

Image biomarkers and explainable AI

European Radiology Experimental February 2025

Feature extraction and selection in medical data are crucial for radiomics and image biomarker discovery, particularly using convolutional neural networks (CNNs). The process involves feature extraction, dimensionality reduction, and addressing the curse of dimensionality. While deep learning (DL) techniques perform well, handcrafted features are important for certain studies and need to be considered. Dataset size and diversity are also key

On Artificial Intelligence: An interview with Daniel Truhn

Alex Vogel, Daniel Truhn February 2025

Following a fellowship in artificial intelligence (AI) at the Institute of Imaging and Computer Vision at RWTH Aachen, which only solidified his belief in AI’s great potential in medicine, Daniel Truhn, a Diagnostic and Interventional radiologist at University Hospital Aachen in Germany, started working on AI’s application in medicine, even creating his own working group in the area. Join us

CNNs perform automated staging of cardiac iron overload from multiecho MR sequences

European Radiology February 2025

This new European Radiology study aimed to develop a deep-learning model for classifying myocardial iron overload (MIO) using magnitude T2* multi-echo MR images from 496 thalassemia major patients. Two 2D convolutional neural networks (CNN), MS-HippoNet (multi-slice) and SS-HippoNet (single-slice), were trained using 5-fold cross-validation. The model showed strong performance with a multi-class accuracy of 0.885 and 0.836 on test sets,

Deep learning in diagnostic imaging of spondyloarthropathies

Mahmud Omar February 2025

Our recent systematic review observed that deep learning models for spondyloarthropathies (SpA) hold remarkable promise—not just for identifying subtle radiographic or MRI findings, but also for harnessing massive datasets to detect early, even preclinical, disease features. These techniques can parse millions of pixel-level patterns, potentially flagging nuanced changes that may precede clinical symptoms. Such a capability could fundamentally shift SpA

Multi-scene model shows success for precise preoperative and intraoperative segmentation of acute VCFs

Insights into Imaging February 2025

This study aimed to develop a multi-scene model, Positioning and Focus Network (PFNet), for automatically segmenting acute vertebral compression fractures (VCFs) from spine radiographs. Conducted across five hospitals from 2016 to 2019, the study included both acute VCF patients and healthy controls. PFNet was trained with radiographs from Hospitals A and B, and tested on datasets from Hospitals A-E. The

An AI deep learning algorithm for detecting pulmonary nodules on ultra-low-dose CT in an emergency setting

Inge van den Berk January 2025

The OPTIMACT trial investigated the added value of ultra-low-dose CT (ULDCT) compared to chest x-ray in patients suspected of pulmonary disease at the emergency department (ED). As expected, more incidental pulmonary nodules required follow-up in the ULDCT-arm (4.5%) compared to the CXR-arm (0.6%) of the trial. The number of these nodules in the ULDCT-arm was lower than reported in other

Using commercial AI-based mammography analysis software to improve breast US interpretations

European Radiology January 2025

This study evaluated the use of commercial AI-based mammography software to improve breast ultrasound (US) lesion interpretations. The AI software, which provides an AI malignancy score ranging from 0 to 100, was tested on 1,109 breasts that underwent both mammography and US-guided breast biopsy. The AI software showed an area under the curve (AUROC) of 0.79 for distinguishing benign from

Deep learning and compressed sensing for reconstruction of 3D knee MRI

European Radiology Experimental December 2024

This study explores combining compressed sensing (CS) and artificial intelligence (AI), particularly deep learning (DL), to accelerate three-dimensional (3D) magnetic resonance imaging (MRI) of the knee. Twenty healthy volunteers were examined using a 3-T scanner with a fat-saturated 3D proton density sequence at various acceleration levels. Two reconstruction methods were compared: conventional CS and a new DL-based algorithm (CS-AI). Two

On Artificial Intelligence: An interview with Christian Blüthgen

Alex Vogel, Christian Blüthgen December 2024

We were delighted to speak with Christian Blüthgen, a radiologist at the Institute for Diagnostic and Interventional Radiology at University Hospital Zurich. Blüthgen was previously a postdoctoral research fellow at the Center for Artificial Intelligence in Medicine and Imaging (AIMI) at Stanford University in California, USA, where he gained practical experience in the design and implementation of deep learning pipelines

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