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

Digital breast tomosynthesis versus digital mammography: integration of image modalities enhances deep learning-based breast mass classification

The authors of this study evaluated the impact of utilizing digital breast tomosynthesis (DBT) and/or full-field digital mammography (FFDM), and different transfer learning strategies, on deep convolutional neural network (DCNN)-based mass classification for breast cancer. The authors found that integrating DBT and FFDM in DCNN training helps to enhance breast mass classification accuracy. Key points Transfer learning facilitates mass classification

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Radiogenomics of lower-grade gliomas: machine learning–based MRI texture analysis for predicting 1p/19q codeletion status

In this work, we aimed to evaluate the potential value of the machine learning (ML)–based MRI texture analysis for predicting 1p/19q codeletion status of lower-grade gliomas (LGG). This retrospective study was totally based on public data. We reduced the high-dimensionality of the radiomic data with collinearity analysis and ReliefF algorithm. Then, we used seven ML classifiers for the development of

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Fully Interlinked for Rapid Data Exchange in Mammography

In the region of southern Denmark, a broad network of hospitals provides medical services for the 1.2 million residents of South Jutland and the island of Funen. A mammography screening program for women ranging in age from 50 to 69 has been running there for 25 years. More than 100,000 women are called in for an examination every year, and

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Reading recommendation – Reasons to do AI with Friends

The literature below has been hand-selected as recommended reading to complement the topics covered during Season 2 of ‘AI-Use Cases: Reasons to do AI with Friends‘, brought to you by ESR Connect and the European Society of Radiology. General AI papers: European Society of Radiology (2019) What the radiologist should know about artificial intelligence – an ESR white paper. Insights

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Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire

In this study, the authors aimed to develop and validate a patient questionnaire on the patients’ view on the implementation of artificial intelligence (AI) in radiology. Six domains from a previous study were used to develop the patient questionnaire. The questionnaire explored accountability, distrust, personal interaction, and efficiency, among other areas. Key points Although AI systems are increasingly developed, not

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Prediction of pulmonary pressure after Glenn shunts by computed tomography–based machine learning models

The aim of this retrospective study was to develop non-invasive machine learning (ML) classifiers for predicting post-Glenn shunt patients with low and high risks of a mean pulmonary arterial pressure above 15 mmHg, which was based on pre-operative cardiac CT. The study included 96 patients who underwent a bidirectional Glenn procedure. Key points Twenty-three candidate descriptors were manually extracted from

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Update: Now featuring a podcast – Taking a look at Digital Twin Technology: a new frontier in personalised healthcare

Update: Listen to two experts discuss the technological progress of artificial intelligence* in healthcare: Dr. Dorin Comaniciu, PhD, Senior Vice President of Artificial Intelligence at Siemens Healthineers, and Dr. Christoph Zindel, Member of the Managing Board of Siemens Healthineers with responsibility for the Imaging and Advanced Therapies business segments, talk about the expectations of physicians, their challenges, and new opportunities

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Evaluation of an AI-based, automatic coronary artery calcium scoring software

In this study, using a semi-automatic software as a reference, the authors aimed to evaluate an artificial intelligence (AI)-based, automatic coronary artery calcium (CAC) scoring software. This observational study included 315 non-contrast-enhanced calcium scoring computed tomography (CSCT) scans. The authors determined that there was a strong correlation between the automatic software and the semi-automatic software for three CAC scores and

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Deep learning-based detection system for multiclass lesions on chest radiographs: comparison with observer readings

In this study, the authors sought to investigate the feasibility of a deep learning-based detection (DLD) system for multiclass legions on chest radiograph. The study included almost 16,000 chest radiographs collected from two tertiary hospitals, which were then used to develop a DLD system. The authors found that the DLD system showed potential for detections of lesions and pattern classification.

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Combining DWI radiomics features with transurethral resection promotes the differentiation between muscle-invasive bladder cancer and non-muscle-invasive bladder cancer

The purpose of this retrospective study was to investigate the value of radiomics features from diffusion-weighted imaging (DWI) in differentiating muscle-invasive bladder cancer (MIBC) from non-muscle-invasive bladder cancer (NMIBC). The study included 218 bladder cancer patients who underwent DWI prior to biopsy between July 2014 and December 2018. The authors discovered that combining DWI radiomics features with transurethral resection (TUR)

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Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.