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

Unlocking the Potential of Radiomics: Ongoing Challenges Are Revolving Around Methodology and Reproducibility

Over a decade in the making, the novel concept of radiomics has been silently brewing, promising to reshape the landscape of personalised and precision medicine. So, why has radiomics not made its clinical debut yet, despite its innovative and logical approach? The answer lies in the intricate world of advanced computation that forms the basis of radiomics, which in essence

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Evaluation of techniques to improve a deep learning algorithm for the automatic detection of intracranial haemorrhage on CT head imaging

This study evaluates deep learning (DL) algorithms that are playing an increasingly important role in automatic medical image analysis. The DL algorithm used was trained and externally evaluated on open-source, multi-centre retrospective data that contained radiologist-annotated non-contrast CT head studies. The authors concluded that the DL model has applications in a triage role with the potential to improve diagnostic yield

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Multi-channel deep learning model diagnoses the cause of LVH

A new study sees the development of a fully automatic framework for the diagnosis of the cause of left ventricular hypertrophy (LVH) via cardiac cine images. The fully automatic myocardium segmentation and spatial-temporal morphology feature-based LVH etiology diagnosis deep learning framework model was able to show a favorable and robust performance in diagnosing the cause of LVH, which could be

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Evaluating a deep learning software for lung parenchyma characterization in COVID-19 pneumonia

The aim of this study was to evaluate the performance of the LungQuant system, which is a deep learning-based software for quantitative analysis of chest CT. LungQuant was evaluated by comparing its results with independent visual evaluations by a group of clinical experts. The results indicated that an automatic quantification tool may be beneficial and contribute to an improved clinical

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Robustness of pulmonary nodule radiomic features on CT as a function of varying radiation dose levels

The aim of this study was to present an in vivo stability analysis of radiomic features for pulmonary nodules against varying radiation dose levels. The authors found that a large majority of pulmonary nodule radiomic features were not inherently robust to radiation dose level variations and determined that a lower radiation dose introduces increasingly random noise and bias to radiomic

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Reproducibility of a combined AI and optimal-surface graph-cut method to automate bronchial parameter extraction

The authors of this study evaluated the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters. A deep-learning model was trained on 24 low-dose chest CT scans. The study demonstrated a comprehensive and fully automatic pipeline for bronchial parameter measurement on low-dose CT using open-source tools. Key points

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Quo vadis Radiomics? Bibliometric analysis of 10-year Radiomics journey

This study conducted a bibliometric analysis of radiomics ten years after the first work became available in March 2012. Throughout the analysis, the authors identified over 5,500 articles from almost 17,000 authors from over 900 different sources, highlighting developments within radiomics, its real-world applications, and tangible and intangible benefits. Key points ML-based bibliometric analysis is fundamental to detect unknown pattern

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A novel AI model to distinguish benign from malignant ovarian tumors

The authors of this study developed a CT-based artificial intelligence model with the ability to differentiate between benign and malignant ovarian tumors, showing high accuracy and specificity. In coordination with less-experienced radiologists, the model helped in the performance of ovarian tumor assessment, with applications to provide better therapeutic strategies for patients with ovarian tumors. Key points CT-based radiomics and deep

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Aging-related volume changes in the brain and cerebrospinal fluid using AI-automated segmentation

Deep learning methods to quantitatively assess disease-specific brain atrophy from CT and MRI images are rapidly gaining popularity, and a new era of clinical neuroimaging will soon arrive. We investigated the effects of aging and gender differences in volumes and volume ratios of regional brain and cerebrospinal fluid (CSF) spaces in healthy volunteers using the Brain Subregion Analysis application working

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Structured reporting using an intelligent dialogue system based on speech recognition and NLP

Although structured reporting (SR) is recommended in the field of radiology compared to free-text reporting (FTR), the use of SR still experiences obstacles due to insufficient integration of speech recognition. Furthermore, SR templates are time-consuming as they have to be completed using a traditional mouse and keyboard. New technologies within the realm of artificial intelligence, such as natural language processing

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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
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04
European Radiology, Insights into Imaging, European Radiology Experimental.