AI Blog

Deep learning detection and quantification of pneumothorax in heterogeneous routine chest computed tomography

European Radiology Experimental October 2020

In this study, the authors proposed a deep learning method for the detection and quantification of pneumothorax in heterogeneous routine clinical data, which may facilitate the automated triage of urgent examinations and enable support in the treatment decision. Key points Pneumothorax is an important pathology to be included in applications that are designed to triage urgent imaging examinations. Heterogeneity in

MRI radiomics features predict immuno-oncological characteristics of hepatocellular carcinoma

Stefanie Hectors, Bachir Taouli September 2020

Advanced hepatocellular carcinoma (HCC) carries a dismal prognosis. For a decade, sorafenib, a multi-kinase inhibitor, was the only approved systemic therapy for HCC. However, its response rate in advanced HCC is only about 2%. The last few years have seen rapid approval of additional systemic therapies for HCC, including immunotherapy strategies. Immune checkpoint inhibitor nivolumab has a promising reported response

Radiomics based on brain MRI is expected to be a useful tool for early identification and prediction of WMH progression

Yuyun Xu, Xiangyang Gong September 2020

As a research hotspot in recent years, radiomics provides a new perspective for image diagnosis or evaluation by mining a large number of non-traditional visual information in medical images and adds new indicators (image markers). Radiomics may become a non-invasive, low-cost and easy to popularize imaging tool. White matter hyperintensity (WMH) refers to the high signal of white matter area

AI in the clinical field of lung disease and COVID-19

ESR September 2020

Artificial intelligence (AI) tools are becoming a common occurrence in everyday healthcare, especially in the areas of early detection and diagnosis. As lung diseases and the current COVID-19 pandemic become more prevalent around the world, early detection and diagnosis can be literal lifesavers. AI, along with collaborative efforts of the radiological, healthcare industry, and academic communities, are helping to make

Machine learning and radiomic phenotyping of lower grade gliomas: improving survival prediction

European Radiology September 2020

In this study, the authors aimed to evaluate whether MRI-based radiomic features were able to improve the accuracy of survival predictions for lower grade gliomas over clinical isocitrate dehydrogenase (IDH) status. The authors extracted radiomic features from the preoperative MRI data of 296 lower grade glioma patients from their institution as well as The Cancer Genome Atlas (TCGA) and The

Artificial intelligence and radiomics enhance the positive predictive value of digital chest tomosynthesis for lung cancer detection within SOS clinical trial

Stéphane Chauvie August 2020

Digital tomosynthesis (DTS) could be a realistic alternative to low dose CT for lung cancer screening, in particular for detecting nodules larger than 5 mm. In the past years, within SOS clinical trial we found that the probability of detecting lung cancer with the DTS in a population of heavy smokers older than 45 years was around 1%, a value

Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials

European Radiology August 2020

In this observational cohort study, the authors aimed to determine the potential impact of machine learning (ML) CT-derived fractional flow reserve (CT-FFR) on the diagnostic efficiency and effectiveness of coronary CT angiography (CCTA) in patients with obstructive coronary artery disease (CAD). It was found that the implementation of on-site CT-FFR may change management and help to improve diagnostic efficiency and

AI abstracts from ECR 2019: analysis of topics and compliance with the STARD for abstracts checklist

Daniel Pinto dos Santos August 2020

New machine learning techniques, especially deep neural networks, hold the promise of revolutionizing many aspects of radiology and have gained immense public and professional attention over the last few years. This has led to a sharp increase in publications, the founding of new journals, and FDA approval for new diagnostic algorithms. With this increased scientific output, we wanted to take

Diagnostic accuracy and potential covariates for machine learning to identify IDH mutations in glioma patients: evidence from a meta-analysis

European Radiology August 2020

The goal of this study was to assess the diagnostic accuracy of machine learning in the prediction of isocitrate dehydrogenase (IDH) mutations, particularly in patients with glioma, as well as to identify potential covariates that may have an influence on the diagnostic performance of machine learning. The authors were able to show that machine learning demonstrated excellent diagnostic performance in

AI for reading screening mammograms: the need for circumspection

Philippe Autier July 2020

AI is viewed as an emerging technology for reading screening mammograms. However, most studies done so far have adopted retrospective designs that cannot fully appreciate the added value and limitations of AI technologies (Autier et al, Eur Radiol 2020, Apr 21). For instance, these studies cannot inform on numbers and results of biopsies that would have been done following a

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