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

Machine learning–based radiomics classifies parotid tumors using morphological MRI

This comparative study aimed to evaluate the effectiveness of machine learning models based on morphological MRI radiomics in the classification of parotid tumors. The authors developed three-step machine learning models with extreme gradient boosting (XGBoost), support vector machine (SVM), and decision tree (DT) algorithms in order to classify the parotid neoplasms into four subtypes. The study was able to demonstrate

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Communicating with patients in the age of online portals

Future generations of patients are very likely to get more involved in decisions regarding their healthcare according to the motto “nothing about me without me”. The deployment of electronic patient portals increasingly allows patients throughout Europe to consult and share their medical data, including radiology reports and images, securely and timely online. Technical solutions and rules for releasing reports and

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Considerations for AI clinical impact in oncologic imaging

Artificial Intelligence for Health Imaging (AI4HI) is a network of 5 large EU-funded research projects (Chaimeleon, EuCanImage, INCISIVE, ProCancer-I, PRIMAGE), consisting of more than 120 institutions from 20 countries. This network is currently collecting images from more than 91,000 patients with different types of cancer and is working on artificial intelligence (AI) solutions that will be trained and validated on

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Deep Learning–driven classification of external DICOM studies for PACS archiving

The authors of this study used a deep learning-based approach, MOdality Mapping and Orchestration (MOMO), to deal with potential issues that are caused when patients switch hospitals throughout the course of their treatment. These changes result in the staff at the new hospital, consisting of dedicated medical-technical personnel, being tasked with the processing and archiving of external DICOM studies. This

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AI for radiological paediatric fracture assessment

Fractures in children are common, sometimes subtle and can signify underlying child abuse. They present unique challenges, given the different appearances of the growing skeleton at different ages. In this systematic review, the authors reviewed the available literature on the use of AI for paediatric fracture detection. Additional Key points: Few articles (n=9) were available for review regarding paediatric fracture

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The role of generative adversarial networks in brain MRI

Magnetic Resonance Imaging (MRI) is a widely used medical imaging technology that is non-intrusive and considered safe for humans and can generate different modalities of an image, as well as provide valuable insights into a specific disease. The frequent sequences of MRI are T1-weighted and T2- weighted scans. The popularity of artificial intelligence (AI) for brain MRI is on the

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Survey of ESR members looks at radiologists’ practical experience with AI

To obtain an impression of the current practical clinical experience of radiologists from different European countries with artificial intelligence (AI)-powered tools, the European Society of Radiology (ESR) conducted a survey among its members. Out of a total of 690 respondents, there were 276 radiologists from 229 institutions in 32 countries who responded that they had practical clinical experience with an

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AI system for detecting COVID-19 on chest radiographs in symptomatic patients

Due to the challenges associated with differentiating COVID-19 from the number of respiratory infections that can appear on chest radiographs (CXR), the authors of this study developed and validated an AI system for COVID-19 detection on presenting CXR. This was achieved by training a deep learning model on nearly 170,000 CXRs, and was subsequently validated on a large international test

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MRI-based radiomics to predict response in locally advanced rectal cancer

The study aimed to implement and externally validate an MRI-based radiomics pipeline in order to predict the response to treatment of locally advanced rectal cancer (LARC), while also investigating the impact of manual and automatic segmentations on said radiomics models. The authors were able to show that radiomics models can help clinicians in the prediction of tumor response to chemoradiotherapy

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Radiologists with and without deep learning–based computer-aided diagnosis

When radiologists encounter pulmonary nodules/masses in computed tomography (CT) images, they diagnose malignancy based on lesion characteristics (e.g., spiculation and calcification). However, accurate characterization requires careful observation and can be difficult, especially for inexperienced radiologists. In addition, the assessments may vary among radiologists, resulting in the low reproductivity of findings. We investigated if commercially-available deep learning (DL)-based computer-aided diagnosis (CAD)

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