Beyond the Artificial Intelligence Hype What Lies Behind the Algorithms and What We Can Achieve

The field of artificial intelligence (AI) is currently experiencing a period of extensive growth in a wide variety of fields, medicine not being the exception. The base of AI is mathematics and computer science, and the current fame of AI in industry and research stands on 3 pillars: big data, high performance computing infrastructure, and algorithms. In the current digital era, increased storage capabilities and data collection systems, lead to a massive influx of data for AI algorithm. The size and quality of data are 2 major factors influencing performance of AI applications. However, it is highly dependent on the type of task at hand and algorithm chosen to perform this task. AI may potentially automate several tedious tasks in radiology, particularly in cardiothoracic imaging, by pre-readings for the detection of abnormalities, accurate quantifications, for example, oncologic volume lesion tracking and cardiac volume and image optimization. Although AI-based applications offer great opportunity to improve radiology workflow, several challenges need to be addressed starting from image standardization, sophisticated algorithm development, and large-scale evaluation. Integration of AI into the clinical workflow also needs to address legal barriers related to security and protection of patient-sensitive data and liability before AI will reach its full potential in cardiothoracic imaging

Fast Mapping bij jongeren met het syndroom van Down

Het onderzoek omvat een studie naar Fast Mapping (FM) bij 14 jongeren met downsyndroom (12;2 – 18;6 jaar) in vergelijking met 14 op mentale leeftijd gematchte kinderen (4;8 – 8;1 jaar). De jongeren met downsyndroom presteren minder goed op een FM-begripstaak en een FM-productietaak, maar blijken even goed te presteren op een taak waarbij semantische kenmerken vergeleken moeten worden. Bij het leren van nieuwe woorden blijkt dat zij de vaardigheid missen om woorden te leren waaraan zij slechts kort blootgesteld zijn. Hiermee wordt een mogelijke verklaring gegeven voor de geringere woordenschat van deze kinderen

Evaluation of pericoronary adipose tissue attenuation on CT

Pericoronary adipose tissue (PCAT) is the fat deposit surrounding coronary arteries. Although PCAT is part of the larger epicardial adipose tissue (EAT) depot, it has different pathophysiological features and roles in the atherosclerosis process. While EAT evaluation has been studied for years, PCAT evaluation is a relatively new concept. PCAT, especially the mean attenuation derived from CT images may be used to evaluate the inflammatory status of coronary arteries non-invasively. The most commonly used measure, PCATMA, is the mean attenuation of adipose tissue of 3 mm thickness around the proximal right coronary artery with a length of 40 mm. PCATMA can be analyzed on a per-lesion, per-vessel or per-patient basis. Apart from PCATMA, other measures for PCAT have been studied, such as thickness, and volume. Studies have shown associations between PCATMA and anatomical and functional severity of coronary artery disease. PCATMA is associated with plaque components and high-risk plaque features, and can discriminate patients with flow obstructing stenosis and myocardial infarction. Whether PCATMA has value on an individual patient basis remains to be determined. Furthermore, CT imaging settings, such as kV levels and clinical factors such as age and sex affect PCATMA measurements, which complicate implementation in clinical practice. For PCATMA to be widely implemented, a standardized methodology is needed. This review gives an overview of reported PCAT methodologies used in current literature and the potential use cases in clinical practice.</p

An integrated stewardship model: antimicrobial, infection prevention and diagnostic (AID)

Considering the threat of antimicrobial resistance and the difficulties it entails in treating infections, it is necessary to cross borders and approach infection management in an integrated, multidisciplinary manner. We propose the antimicrobial, infection prevention and diagnostic stewardship model comprising three intertwined programs: antimicrobial, infection prevention and diagnostic stewardship, involving all stakeholders. The focus is a so-called ‘theragnostics’ approach. This leads to a personalized infection management plan, improving patient care and minimizing resistance development. Furthermore, it is important that healthcare regions nationally and internationally work together, ensuring that the patient (and microorganism) transfers will not cause problems in a neighboring institution. This antimicrobial, infection prevention and diagnostic stewardship model can serve as a blue print to implement innovative, integrative infection management

Automatic coronary calcium scoring in chest CT using a deep neural network in direct comparison with non-contrast cardiac CT:A validation study

Purpose: To evaluate deep-learning based calcium quantification on Chest CT scans compared with manual evaluation, and to enable interpretation in terms of the traditional Agatston score on dedicated Cardiac CT. Methods: Automated calcium quantification was performed using a combination of deep-learning convolution neural networks with a ResNet-architecture for image features and a fully connected neural network for spatial coordinate features. Calcifications were identified automatically, after which the algorithm automatically excluded all non-coronary calcifications using coronary probability maps and aortic segmentation. The algorithm was first trained on cardiac-CTs and refined on non-triggered chest-CTs. This study used on 95 patients (cohort 1), who underwent both dedicated calcium scoring and chest-CT acquisitions using the Agatston score as reference standard and 168 patients (cohort 2) who underwent chest-CT only using qualitative expert assessment for external validation. Results from the deep-learning model were compared to Agatston-scores(cardiac-CTs) and manually determined calcium volumes(chest-CTs) and risk classifications. Results: In cohort 1, the Agatston score and AI determined calcium volume shows high correlation with a correlation coefficient of 0.921(p < 0.001) and R-2 of 0.91. According to the Agatston categories, a total of 67(70 %) were correctly classified with a sensitivity of 91 % and specificity of 92 % in detecting presence of coronary calcifications. Manual determined calcium volume on chest-CT showed excellent correlation with the AI volumes with a correlation coefficient of 0.923(p < 0.001) and R-2 of 0.96, no significant difference was found (p = 0.247). According to qualitative risk classifications in cohort 2, 138(82 %) cases were correctly classified with a k-coefficient of 0.74, representing good agreement. All wrongly classified scans (30(18 %)) were attributed to an adjacent category. Conclusion: Artificial intelligence based calcium quantification on chest-CTs shows good correlation compared to reference standards. Fully automating this process may reduce evaluation time and potentially optimize clinical calcium scoring without additional acquisitions

Quantitative analysis of dynamic computed tomography angiography for the detection of endoleaks after abdominal aorta aneurysm endovascular repair:A feasibility study

ObjectivesTo assess the feasibility of quantitative analysis of dynamic computed tomography angiography (dCTA) for the detection of endoleaks in patients who underwent endovascular repair of abdominal aortic aneurysms (EVAR).Material and methodsTwenty patients scheduled for contrast-enhanced CT angiography (CTA) of the abdominal aorta post-EVAR were prospectively enrolled. All patients received a standard triphasic CTA protocol, followed by an additional dCTA. The dCTA acquisition enabled reconstruction of color-coded maps depicting blood perfusion and a dCTA dataset of the aneurysm sac. Observers assessed the dCTA and dynamic CT perfusion (dCTP) images for the detection of endoleaks, establishing diagnostic confidence based on a modified 5-point Likert scale. An index was calculated for the ratio between the endoleak and aneurysm sac using blood flow for dCTP and Hounsfield units (HU) for dCTA. The Wilcoxon test compared the endoleak index and the diagnostic confidence of the observers.ResultsIn total, 19 patients (18 males, median age 74 years [70.5-75.7]) were included for analysis. Nine endoleaks were detected in 7 patients using triphasic CTA as the reference standard. There was complete agreement for endoleak detection between the two techniques on a per-patient basis. Both dCTA and dCTP identified an additional endoleak in one patient. The diagnostic confidence using dCTP for detection of endoleaks was not significantly superior to dCTA (5.0 [5-5] vs. 4.5 [4-5], respectively; p = 0.11); however, dCTP demonstrated superior diagnostic confidence for endoleak exclusion compared to dCTA (1.0 [1-1] vs 1.5 [1.5-1.5], respectively; p ConclusionsQuantitative analysis of dCTP imaging can aid in the detection of endoleaks and demonstrates a higher endoleak detection rate than triphasic CTA, as well as a strong correlation with visual assessment of dCTA images