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

Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

We investigated a saliency-based 3D convolutional neural network (CNN) to classify seven categories of common focal liver lesions and validated the model performance. This retrospective study included 557 lesions examined by multisequence MRI. We found that this interpretable deep learning model showed high diagnostic performance in the differentiation of common liver masses on multisequence MRI. A few important notes on

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Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Prostate MRI can be a game-changer for many men with elevated prostate-specific antigen (PSA). For decades these many men underwent biopsies while never developing prostate cancer. Expert prostate MRI can help avoid these unnecessary biopsies and better target any biopsies. Unfortunately, reading prostate MRI is challenging and time-consuming. Like other medical imaging modalities, AI is explored for helping read prostate

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Artificial intelligence on MRI for molecular subtyping of diffuse gliomas: feature comparison, visualization, and correlation between radiomics and deep learning

This editorial comment discusses the study by Li et al., entitled “Molecular subtyping of diffuse gliomas using magnetic resonance imaging: comparison and correlation between radiomics and deep learning”. The original article to which the editorial comment refers aimed to establish predictive models based on preoperative multiparametric MRI, related to molecular subtyping of diffuse gliomas. Article: Artificial intelligence on MRI for

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Commercial AI solutions in detecting COVID‐19 pneumonia in chest CT: not yet ready for clinical implementation?

Thinking back on the last two years, what were the dominant topics of discussion in radiology? Certainly, artificial intelligence (AI) in radiology has sparked a lot of interest and enthusiasm in radiology, and COVID-19, which was a topic nobody could avoid. So, it comes as no surprise that the combination of both topics – i.e. using AI to detect COVID

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A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

The authors of this study aimed to develop an artificial intelligence (AI)-based fully automated CT image analysis system in order to detect and diagnose pulmonary tuberculosis (TB). This was achieved through the retrospective use of 892 chest CT scans from pathogen-confirmed TB patients. It was found that the end-to-end AI system based on chest CT is able to achieve human-level

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An international survey on AI in radiology in 1041 radiologists and radiology residents part 2: expectations, hurdles to implementation, and education

As a follow-up to part one of this international survey on artificial intelligence (AI), which surveyed over 1,000 radiologists and radiology residents and explored early adoption of AI, as well as radiologists’ perspectives on and fear of being replaced by these technologies, part 2 discusses the expectations of AI and the hurdles associated with implementation and education. Regardless of the

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Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke

The authors of this study aimed to determine the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from computed tomography angiography (CTA), subsequently comparing the results to a CT perfusion (CTP)-based commercially available software. The stroke cases treated with thrombolytic therapy or receiving supportive care were retrospectively selected by the authors. The study found that a

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CT and MRI radiomics of bone and soft-tissue sarcomas: a systematic review of reproducibility and validation strategies

The authors of this study aimed to systematically review radiomic feature reproducibility and predictive model validation strategies in studies that deal with CT and MRI radiomics of bone and soft-tissue sarcomas. The review consisted of 278 papers, forty-nine of which were published between 2008 and 2020. The authors found that the issues of radiomic feature reproducibility and model validation varied

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Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

This retrospective, multi-institutional study investigated machine learning classifiers and interpretable models using chest CT for the detection of COVID-19 and to differentiate this from types of pneumonia, interstitial lung disease (ILD), and normal CTs. The study included 2,446 chest CTs from across 16 different institutions and the authors’ method was found to accurately differentiate COVID-19 from other types of pneumonia,

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Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

The aim of this study was to develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE) on chest radiographs. The authors determined that the DLCE-LAE was able to outperform and improve the performance of cardiothoracic radiologists in the detection of LAE, while also showing promise in screening individuals with moderate-to-severe LAE in a healthcare

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