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

Radiomics in the evaluation of ovarian masses

This systematic review looked at the literature reporting the application of radiomics to imaging techniques in ovarian lesion patients. The authors found that radiomics showed promising results and great potential as a clinical diagnostic tool in patients with ovarian masses when it comes to improving lesion stratification, treatment selection, and outcome prediction. However, much larger and more diverse patient cohorts

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Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes

With the help of radiomics, standard medical images can be transformed into detailed, high-dimensional data sets that go beyond what the eye can see. The typical workflow of radiomic projects involves a series of sequential processes, including image registration, intensity normalization, and segmentation of the region of interest. While there is a general agreement on the essential steps, consensus on

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Radiomics-based prediction of FIGO grade for placenta accreta spectrum

Placenta Accreta Spectrum (PAS) is a serious, life-threatening pregnancy complication. Although rare, as more women are giving birth by Caesarean section, PAS is becoming more common. The most important factor in improving outcomes for mothers and babies is the detection of PAS during pregnancy to ensure the appropriate multi-disciplinary team care is implemented for the pregnancy and birth. However, up

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Can radiomics push the limits of current IPMN malignancy assessment and help avoid unnecessary resection?

This commentary dives into an article published in August 2023 in European Radiology entitled “Radiomics model versus 2017 revised international consensus guidelines for predicting malignant intraductal papillary mucinous neoplasms” (Doo Young Lee et al.), which compared the diagnostic performance of the CT radiomics model with the 2017 international consensus Fukuoka guideline for predicting malignant intraductal papillary mucinous neoplasms (IMPNs). The

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Self-reporting with checklists in AI research on medical imaging

This study aimed to evaluate the usage of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM), a well-known and widely adopted checklist in the radiological community, for self-reporting through a systematic analysis of its citations. The authors used three databases (Google Scholar, Web of Science, and Scopus) and identified nearly 400 unique citations across 118 papers, of which only

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Ovarian cancer beyond imaging: integration of AI and multiomics biomarkers

Ovarian cancer, often referred to as the “silent killer”, tends to show inconspicuous symptoms in the early stages, making timely diagnosis difficult. Detecting ovarian cancer at an early stage significantly increases the chances of effective treatment and therefore the chances of survival. This review study has shown that AI-based tools that rely on the integration of multi-omics data perform better

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Label-set impact on deep learning-based prostate segmentation on MRI

This study delves into a less-explored territory in automatic prostate segmentation: label-set selection. Recognizing the emphasis on dataset selection in segmentation model training, we thought it crucial to investigate the impact of the labels, i.e. the manual segmentations, on model performance. Although label sets are often considered the gold standard, as they are provided by highly trained professionals, disparities emerge

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External validation, radiological evaluation, and development of DL lung segmentation in chest CT

The authors of this study developed a 3D nnU-Net-based model for automatic lung segmentation in computed tomography pulmonary angiography (CTPA) imaging that was found to be highly accurate, clinically evaluated, and externally tested in patient cohorts with a spread of lung disease. Key points Article: External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT

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ChatGPT makes medicine easy to swallow

Just over a year ago, the release of ChatGPT marked a turning point in AI and language processing, sparking widespread excitement for Large Language Models (LLMs) for diverse use cases across various domains. Back then, we noticed friends using ChatGPT for medical text simplification, yet, as medical laypersons, they couldn’t verify the accuracy of the simplified text. Anticipating its imminent adoption by patients,

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Core and penumbra estimation using deep learning-based AIF in association with clinical measures in computed tomography perfusion (CTP)

In this study, the utilization of a convolutional neural network (CNN)-based arterial input function (AIF) and whether it improves the volumetric estimation of core penumbra was investigated. The authors included 160 acute ischemic stroke patients for this study, who underwent CTP imaging, National Institutes of Health Stroke Scale (NIHSS), and Alberta Stroke Programme Early CT Score (APSECTS) grading. They were

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