Electrical impedance tomography reveals pathophysiology of neonatal pneumothorax during NAVA

Pneumothorax is a potentially life‐threatening complication of neonatal respiratory distress syndrome (RDS). We describe a case of a tension pneumothorax that occurred during neurally adjusted ventilatory assist (NAVA) in a preterm infant suffering from RDS. The infant was included in a multicenter study examining the role of electrical impedance tomography (EIT) in intensive care and therefore continuously monitored with this imaging method. The attending physicians were blinded for EIT findings but offline analysis revealed the potential of EIT to clarify the underlying cause of this complication, which in this case was heterogeneous lung disease resulting in uneven ventilation distribution. Instantaneous increase in end‐expiratory lung impedance on the affected side was observed at time of the air leak. Real‐time bedside availability of EIT data could have modified the treatment decisions made

Multimodal Machine Learning-based Knee Osteoarthritis Progression Prediction from Plain Radiographs and Clinical Data

Knee osteoarthritis (OA) is the most common musculoskeletal disease without a cure, and current treatment options are limited to symptomatic relief. Prediction of OA progression is a very challenging and timely issue, and it could, if resolved, accelerate the disease modifying drug development and ultimately help to prevent millions of total joint replacement surgeries performed annually. Here, we present a multi-modal machine learning-based OA progression prediction model that utilizes raw radiographic data, clinical examination results and previous medical history of the patient. We validated this approach on an independent test set of 3,918 knee images from 2,129 subjects. Our method yielded area under the ROC curve (AUC) of 0.79 (0.78-0.81) and Average Precision (AP) of 0.68 (0.66-0.70). In contrast, a reference approach, based on logistic regression, yielded AUC of 0.75 (0.74-0.77) and AP of 0.62 (0.60-0.64). The proposed method could significantly improve the subject selection process for OA drug-development trials and help the development of personalized therapeutic plans

Thoracic shape changes in newborns due to their position

The highly compliant nature of the neonatal chest wall is known to clinicians. However, its morphological changes have never been characterized and are especially important for a customised monitoring of respiratory diseases. Here, we show that a device applied on newborns can trace their chest boundary without the use of radiation. Such technology, which is easy to sanitise between patients, works like a smart measurement tape drawing also a digital cross section of the chest. We also show that in neonates the supine position generates a significantly different cross section compared to the lateral ones. Lastly, an unprecedented comparison between a premature neonate and a child is reported

Performance over Random

This paper proposes a new evaluation approach for video summarization algorithms. We start by studying the currently established evaluation protocol; this protocol, defined over the ground-truth annotations of the SumMe and TVSum datasets, quantifies the agreement between the user-defined and the automatically-created summaries with F-Score, and reports the average performance on a few different training/testing splits of the used dataset. We evaluate five publicly-available summarization algorithms under a large-scale experimental setting with 50 randomly-created data splits. We show that the results reported in the papers are not always congruent with their performance on the large-scale experiment, and that the F-Score cannot be used for comparing algorithms evaluated on different splits. We also show that the above shortcomings of the established evaluation protocol are due to the significantly varying levels of difficulty among the utilized splits, that affect the outcomes of the evaluations. Further analysis of these findings indicates a noticeable performance correlation among all algorithms and a random summarizer. To mitigate these shortcomings we propose an evaluation protocol that makes estimates about the difficulty of each used data split and utilizes this information during the evaluation process. Experiments involving different evaluation settings demonstrate the increased representativeness of performance results when using the proposed evaluation approach, and the increased reliability of comparisons when the examined methods have been evaluated on different data splits

Learned Enhancement Filters for Image Coding for Machines

Machine-To-Machine (M2M) communication applications and use cases, such as object detection and instance segmentation, are becoming mainstream nowadays. As a consequence, majority of multimedia content is likely to be consumed by machines in the coming years. This opens up new challenges on efficient compression of this type of data. Two main directions are being explored in the literature, one being based on existing traditional codecs, such as the Versatile Video Coding (VVC) standard, that are optimized for human-Targeted use cases, and another based on end-To-end trained neural networks. However, traditional codecs have significant benefits in terms of interoperability, real-Time decoding, and availability of hardware implementations over end-To-end learned codecs. Therefore, in this paper, we propose learned post-processing filters that are targeted for enhancing the performance of machine vision tasks for images reconstructed by the VVC codec. The proposed enhancement filters provide significant improvements on the target tasks compared to VVC coded images. The conducted experiments show that the proposed post-processing filters provide about 45% and 49% Bjontegaard Delta Rate gains over VVC in instance segmentation and object detection tasks, respectively.acceptedVersionPeer reviewe

MiR-185-5p regulates the development of myocardial fibrosis

Background: Cardiac fibrosis stiffens the ventricular wall, predisposes to cardiac arrhythmias and contributes to the development of heart failure. In the present study, our aim was to identify novel miRNAs that regulate the development of cardiac fibrosis and could serve as potential therapeutic targets for myocardial fibrosis. Methods and results: Analysis for cardiac samples from sudden cardiac death victims with extensive myocardial fibrosis as the primary cause of death identified dysregulation of miR-185-5p. Analysis of resident cardiac cells from mice subjected to experimental cardiac fibrosis model showed induction of miR-185-5p expression specifically in cardiac fibroblasts. In vitro, augmenting miR-185-5p induced collagen production and profibrotic activation in cardiac fibroblasts, whereas inhibition of miR-185-5p attenuated collagen production. In vivo, targeting miR-185-5p in mice abolished pressure overload induced cardiac interstitial fibrosis. Mechanistically, miR-185-5p targets apelin receptor and inhibits the anti-fibrotic effects of apelin. Finally, analysis of left ventricular tissue from patients with severe cardiomyopathy showed an increase in miR-185-5p expression together with pro-fibrotic TGF-beta 1 and collagen I. Conclusions: Our data show that miR-185-5p targets apelin receptor and promotes myocardial fibrosis.Peer reviewe