AI Blog

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Data control with AI: who’s in charge?

With the increased use of artificial intelligence (AI) in every segment of life including healthcare, patient data is being collected massively and shared by different stakeholders. But who has control over it? The hospital, the patient, the equipment provider, the software developer, the state’s authorities? If no one knows who controls the data, what can happen? Peter van Ooijen, a

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Automatic segmentation and classification of breast lesions through identification of informative multiparametric PET/MRI features

The authors of this study recognized the potential of multiparametric positron emission tomography/magnetic resonance imaging (mpPET/MRI) for detecting and classifying breast lesions. To better understand computer-aided segmentation and diagnosis (CAD) features, the authors introduced a data-driven machine learning approach for a CAD system that enables the assessment of the relevance of mpPET/MRI features on segmentation and classification accuracy. Key points:

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Publications on artificial intelligence: an interview with Professor Zhi-Cheng Li

The topic of Artificial Intelligence (AI) is on the tip of everyone’s tongue in healthcare, especially those involved in radiology. When one thinks of AI in radiology, initial thoughts may consist of new imaging algorithms or futuristic machines assisting doctors. However, one area that may not immediately be considered is that of publishing all the new research. Radiological journals must

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Using radiomics for the preoperative estimation of pathological grade in bladder cancer tumors

The goal of this study was to develop and authenticate an MRI-based radiomics strategy for the preoperative estimation of pathological grade in bladder cancer (BCa) tumors. The obtained radiomic features were evaluated for diagnostic abilities using receiver operating characteristic (ROC) curve analysis and compared using the DeLong test. The authors then performed validation using 30 consecutive patients and were able

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AI guru gives a glimpse into the future of radiology with AI

With the advent of Artificial Intelligence, new opportunities will arise for radiologists if they remain focused and critical, ‘rock star of the digital revolution’ Toby Walsh told delegates at the ESR AI Premium event. A new continent Artificial intelligence (AI) is going to transform every aspect of human life and could generate 15.7 trillion USD – almost China and India’s

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Application of a deep learning algorithm for detection and visualization of hip fractures on plain pelvic radiographs

In this study, we developed the DCNN not only for the automated detection of hip fractures on frontal pelvic radiographs but also to offer visualization of the fracture site by Grad-CAM, which enables the rapid integration of this tool into the current medical system. The age of artificial intelligence (AI) offers new opportunities but also poses challenges, for physicians. Deep

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Artificial Intelligence: From Buzzword into Clinical Routine

Artificial intelligence (AI) shows strong promise in helping to address global challenges faced by healthcare. Through improving patient outcomes or expanding precision medicine, AI offers the opportunity to manage staff shortages and enable cost-effectiveness. According to a study conducted by the management consulting firm PWC, the use of AI helps with early detection of serious diseases and leads to better

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MRI radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study

This study aimed to assess whether MRI radiomics can categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer patients. The authors evaluated the diagnostic performance of the signatures derived from MRI radiomics in 286 patients with proven adnexal tumor. The study results suggest a correlation between radiomics features extracted from MRI and

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AI can reduce reading times and improve reader performance in breast and chest

Tools using artificial intelligence (AI) for medical imaging can help detect and classify lesions in an increasing number of clinical applications. In breast and chest imaging, algorithms can now significantly expedite workflow and improve cancer risk prediction, two experts explained during the ESR AI Premium meeting earlier this month in Barcelona, Spain. Breast cancer screening Breast cancer (BC) screening with

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Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT

Deep learning reconstruction (DLR) is a novel method of reconstruction that introduces deep convolutional neural networks into the reconstruction flow. The authors of this research examined the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR and to compare it to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR). Radiologists analysed and graded results from 46 patients

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