AI Blog

On Artificial Intelligence: An interview with Tugba Akinci D’Antonoli

Alex Vogel, Tugba Akinci D’Antonoli November 2024

In our latest interview, we spoke to Tugba Akinci D’Antonoli, a radiology resident at Cantonal Hospital Baselland and a researcher at the University of Basel, Switzerland. D’Antonoli is a member of the 2023–2025 trainee editorial board for Radiology: Artificial Intelligence, a scientific editorial board member for European Radiology and Diagnostic and Interventional Radiology and is also a member of the

Using AI analytics as imaging biomarkers to predict invasive upgrade of ductal carcinoma in situ

Insights into Imaging November 2024

This study aimed to evaluate the effectiveness of AI-based computer-aided detection/diagnosis (AI-CAD) scores in predicting invasive upgrade in ductal carcinoma in situ (DCIS) diagnosed on percutaneous biopsy. The authors included 440 DCIS cases from 420 women diagnosed via percutaneous biopsy between 2015 and 2019. The results of the study showed that 26.6% of DCIS cases were upgraded to invasive cancer.

On Artificial Intelligence: An interview with Thomas Dratsch

Alex Vogel, Thomas Dratsch November 2024

This week, we spoke with Thomas Dratsch, a radiologist in the Department of Diagnostic and Interventional Radiology at the University of Cologne in Germany. Dratsch’s excitement for artificial intelligence stems from a desire for efficiency and optimizing workflows. As a psychologist, he has also developed an interest in the collaboration of radiologists and AI, as well as AI’s influence on

Public data homogenization for AI model development in breast cancer

European Radiology Experimental November 2024

Developing reliable AI models for clinical applications, especially in breast cancer, requires access to both clinical and imaging data. While The Cancer Imaging Archive (TCIA) offers a large collection of publicly available medical images and clinical data, these existing datasets are often too heterogeneous for direct use in AI model development. This study aimed to harmonize these datasets to create

Detecting type 2 diabetes in women using AI

Insights into Imaging November 2024

This retrospective study explored whether artificial intelligence (AI) had the ability to detect type 2 diabetes mellitus through the evaluation of the pectoral muscle on digital breast tomosynthesis (DBT). Through an analysis of over 11,500 DBT images from 300 female patients, the authors found that applying deep learning for the detection of diabetes mellitus in women found an accuracy of

On Artificial Intelligence: An interview with Merel Huisman

Alex Vogel, Merel Huisman October 2024

In our latest interview, we spoke with Merel Huisman, a cardiovascular and musculoskeletal radiologist at Radboud University Medical Center in Nijmegen, Netherlands and an EuSoMII Board Member. Huisman has a passion for artificial intelligence, spanning almost a decade, even appearing on television in the Netherlands to discuss AI in the healthcare space. Furthermore, Huisman is also an Associate Editor for

Marginal Contribution of Current AI Application in Radiology in the Western World

Merel Huisman, European Radiology October 2024

Huisman et al. highlight the context-dependent value of AI in radiology. In their article, “The Emperor Has Few Clothes: A Realistic Appraisal of Current AI in Radiology,” they present a critical perspective on AI’s actual impact in the Western setting. Merel Huisman, a radiologist and clinical epidemiologist with a special focus on AI, sees it as her mission to caution

Deep learning-based algorithm vs. Virtual monoenergetic imaging and orthopedic metal artifact reduction

European Radiology Experimental October 2024

This study compared the image quality, metal artifacts, and diagnostic confidence of conventional CT images of unilateral total hip arthroplasty patients (THA) with deep learning-based metal artifact reduction (DL-MAR) to conventional CT and 130-keV monoenergetic images with and without orthopedic metal artifact reduction (O-MAR). They found that DL-MAR showed not only higher image quality but also diagnostic confidence and superior

On Artificial Intelligence: An interview with Brendan Kelly

Alex Vogel, Brendan Kelly October 2024

This week, we were extremely delighted to dive into the topic of artificial intelligence in radiology with Brendan Kelly. With a passion for AI, Kelly is currently an AI and Paediatric Radiology Fellow at Great Ormond Street Hospital for Children NHS Foundation Trust, a 2024 NDTP Dr. Richard Steevens Fellow, and an ESOR Fellow in AI and Paediatric Radiology. In

Predictive potential of intratumoral and multiregion peritumoral radiomics

Insights into Imaging October 2024

This study published in Insights into Imaging highlighted the potential of a multiparametric MRI-based radiomic model that integrated intratumoral and peritumoral features as a tool to predict differentiation in hepatocellular carcinoma (HCC). The authors found that the IntraPeri model showed an outstanding ability to predict individualized HCC differentiation through integrating intratumoral and optimal peritumoral features. Key points: Both the intratumoral

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