Some psychometric properties of the Ghent Parental Behavior Scale

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Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications

Artificial intelligence in radiology: 100 commercially available products and their scientific evidence

OBJECTIVES: Map the current landscape of commercially available artificial intelligence (AI) software for radiology and review the availability of their scientific evidence. METHODS: We created an online overview of CE-marked AI software products for clinical radiology based on vendor-supplied product specifications ( www.aiforradiology.com ). Characteristics such as modality, subspeciality, main task, regulatory information, deployment, and pricing model were retrieved. We conducted an extensive literature search on the available scientific evidence of these products. Articles were classified according to a hierarchical model of efficacy. RESULTS: The overview included 100 CE-marked AI products from 54 different vendors. For 64/100 products, there was no peer-reviewed evidence of its efficacy. We observed a large heterogeneity in deployment methods, pricing models, and regulatory classes. The evidence of the remaining 36/100 products comprised 237 papers that predominantly (65%) focused on diagnostic accuracy (efficacy level 2). From the 100 products, 18 had evidence that regarded level 3 or higher, validating the (potential) impact on diagnostic thinking, patient outcome, or costs. Half of the available evidence (116/237) were independent and not (co-)funded or (co-)authored by the vendor. CONCLUSIONS: Even though the commercial supply of AI software in radiology already holds 100 CE-marked products, we conclude that the sector is still in its infancy. For 64/100 products, peer-reviewed evidence on its efficacy is lacking. Only 18/100 AI products have demonstrated (potential) clinical impact. KEY POINTS: * Artificial intelligence in radiology is still in its infancy even though already 100 CE-marked AI products are commercially available. * Only 36 out of 100 products have peer-reviewed evidence of which most studies demonstrate lower levels of efficacy. * There is a wide variety in deployment strategies, pricing models, and CE marking class of AI products for radiology

How does artificial intelligence in radiology improve efficiency and health outcomes?

Since the introduction of artificial intelligence (AI) in radiology, the promise has been that it will improve health care and reduce costs. Has AI been able to fulfill that promise? We describe six clinical objectives that can be supported by AI: a more efficient workflow, shortened reading time, a reduction of dose and contrast agents, earlier detection of disease, improved diagnostic accuracy and more personalized diagnostics. We provide examples of use cases including the available scientific evidence for its impact based on a hierarchical model of efficacy. We conclude that the market is still maturing and little is known about the contribution of AI to clinical practice. More real-world monitoring of AI in clinical practice is expected to aid in determining the value of AI and making informed decisions on development, procurement and reimbursement

CNN-based phoneme classifier from vocal tract MRI learns embedding consistent with articulatory topology

Recent advances in real-time magnetic resonance imaging (rtMRI) of the vocal tract provides opportunities for studying human speech. This modality together with acquired speech may enable the mapping of articulatory configurations to acoustic features. In this study, we take the first step by training a deep learning model to classify 27 different phonemes from midsagittal MR images of the vocal tract.An American English database was used to train a convolutional neural network for classifying vowels (13 classes), consonants (14 classes) and all phonemes (27 classes) of 17 subjects. Classification top-1 accuracy of the test set for all phonemes was 57%. Erroranalysis showedvoiced and unvoiced sounds often being confused. Moreover, we performed principal component analysis on the network’s embedding and observed topological similarities between thenetwork learned representation and the vowel diagram.Saliency maps gaveinsight intothe anatomical regions most important for classification and show congruence with knownregions of articulatory importance.We demonstrate the feasibility for deep learning to distinguish between phonemes from MRI. Network analysis can be used to improve understanding of normal articulation and speech and, in the future, impaired speech. This study brings us a step closer to the articulatory-to-acoustic mapping from rtMRI

Cost-effectiveness of artificial intelligence aided vessel occlusion detection in acute stroke: an early health technology assessment

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AI-support for the detection of intracranial large vessel occlusions: One-year prospective evaluation

Purpose: Few studies have evaluated real-world performance of radiological AI-tools in clinical practice. Over one-year, we prospectively evaluated the use of AI software to support the detection of intracranial large vessel occlusions (LVO) on CT angiography (CTA). Method: Quantitative measures (user log-in attempts, AI standalone performance) and qualitative data (user surveys) were reviewed by a key-user group at three timepoints. A total of 491 CTA studies of 460 patients were included for analysis. Results: The overall accuracy of the AI-tool for LVO detection and localization was 87.6%, sensitivity 69.1% and specificity 91.2%. Out of 81 LVOs, 31 of 34 (91%) M1 occlusions were detected correctly, 19 of 38 (50%) M2 occlusions, and 6 of 9 (67%) ICA occlusions. The product was considered user-friendly. The diagnostic confidence of the users for LVO detection remained the same over the year. The last measured net promotor score was −56%. The use of the AI-tool fluctuated over the year with a declining trend. Conclusions: Our pragmatic approach of evaluating the AI-tool used in clinical practice, helped us to monitor the usage, to estimate the perceived added value by the users of the AI-tool, and to make an informed decision about the continuation of the use of the AI-tool

Safety and immunogenicity of co-administered hookworm vaccine candidates Na-GST-1 and Na-APR-1 in Gabonese adults: a randomised, controlled, double-blind, phase 1 dose-escalation trial

Background Hookworms cause substantial morbidity in children and women of reproductive age. The control strategy of mass drug administration is suboptimal, hence the need for a vaccine. Necator americanus aspartic protease-1 (Na-APR-1) and N americanus glutathione S-transferase-1 (Na-GST-1) are involved in the digestion and detoxification of haemoglobin in the hookworm digestive tract. In animal models, vaccination against these antigens resulted in protection from challenge infection. Both vaccine candidates were shown to be safe and well tolerated when administered separately to healthy adults. We assessed the safety and immunogenicity of co-administered Na-GST-1 and Na-APR-1 (M74) vaccines in healthy Gabonese adults.Methods This randomised, controlled, double-blind, phase 1, dose-escalation trial was done at the Centre de Recherches Medicales de Lambarene, in a region of Gabon where N americanus and other helminths are prevalent. Healthy adults aged 18-50 years and living in Lambarene or the surrounding areas were recruited to the study. Participants were enrolled consecutively into two dose cohorts (30 mu g or 100 mu g of the experimental vaccines) and randomly assigned in blocks (block size four) to receive three doses of either co-administered Na-GST-1 plus Na-APR-1 (M74; 30 mu g or 100 mu g of each), adjuvanted with Alhydrogel (aluminium hydroxide gel suspension) together with an aqueous formulation of glucopyranosyl lipid A, or hepatitis B vaccine plus saline (control group). Vaccines were administered intramuscularly on days 0, 28, and 180. The primary endpoint was safety, with immunogenicity a secondary endpoint. The intention-to-treat population was used for safety analyses, whereas for immunogenicity analyses, the per-protocol population was used (participants who received all scheduled vaccinations). Control vaccine recipients for both dose cohorts were combined for the analyses. The trial is registered with ClinicalTrials.gov, NCT02126462.Findings Between Oct 27, 2014, and Jan 31, 2015, 56 individuals were screened for eligibility, of whom 32 were enrolled and randomly assigned to one of the three study groups (12 each in the 30 mu g and 100 mu g experimental vaccine groups and eight in the control group). Both study vaccines were well tolerated in both dose groups. The most common adverse events were mild-to-moderate injection-site pain, headache, myalgia, and nausea. No severe or serious adverse events related to the vaccines were recorded. 52 unsolicited vaccine-related adverse events occurred during the study, but there was no difference in frequency between vaccine groups. IgG antibodies were induced to each of the vaccine antigens, with mean IgG levels increasing after each vaccination. Vaccination with 100 mu g of each vaccine antigen consistently induced IgG seroconversion (IgG levels above the reactivity threshold). Peak IgG responses were observed 2 weeks after the third vaccine dose for both antigens, with all participants who received the 100 mu g doses seroconverting at that timepoint. IgG levels steadily declined until the final study visit 6 months after the third vaccination, although they remained significantly higher than baseline in the 100 mu g dose group.Interpretation Vaccination with recombinant Na-GST-1 and Na-APR-1 (M74) in healthy adults living in N americanusendemic areas of Gabon was safe and induced IgG to each antigen. To our knowledge, this study is the first to report results of Na-APR-1 (M74) co-administered with Alhydrogel in participants from an N americanus-endemic area. Further clinical development of these vaccines should involve efficacy studies. Funding European Union Seventh Framework Programme. Copyright (C) 2020 Elsevier Ltd. All rights reserved.Host-parasite interactio

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

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