Self-gated 3D stack-of-spirals UTE pulmonary imaging at 0.55T.

PURPOSE: To develop an isotropic high-resolution stack-of-spirals UTE sequence for pulmonary imaging at 0.55 Tesla by leveraging a combination of robust respiratory-binning, trajectory correction, and concomitant-field corrections. METHODS: A stack-of-spirals golden-angle UTE sequence was used to continuously acquire data for 15.5 minutes. The data was binned to a stable respiratory phase based on superoinferior readout self-navigator signals. Corrections for trajectory errors and concomitant field artifacts, along with image reconstruction with conjugate gradient SENSE, were performed inline within the Gadgetron framework. Finally, data were retrospectively reconstructed to simulate scan times of 5, 8.5, and 12 minutes. Image quality was assessed using signal-to-noise, image sharpness, and qualitative reader scores. The technique was evaluated in healthy volunteers, patients with coronavirus disease 2019 infection, and patients with lung nodules. RESULTS: The technique provided diagnostic quality images with parenchymal lung SNR of 3.18 ± 0.0.60, 4.57 ± 0.87, 5.45 ± 1.02, and 5.89 ± 1.28 for scan times of 5, 8.5, 12, and 15.5 minutes, respectively. The respiratory binning technique resulted in significantly sharper images (p < 0.001) as measured with relative maximum derivative at the diaphragm. Concomitant field corrections visibly improved sharpness of anatomical structures away from iso-center. The image quality was maintained with a slight loss in SNR for simulated scan times down to 8.5 minutes. Inline image reconstruction and artifact correction were achieved in <5 minutes. CONCLUSION: The proposed pulmonary imaging technique combined efficient stack-of-spirals imaging with robust respiratory binning, concomitant field correction, and trajectory correction to generate diagnostic quality images with 1.75 mm isotropic resolution in 8.5 minutes on a high-performance 0.55 Tesla system.

Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care.

Deep Learning has a large impact on medical image analysis and lately has been adopted for clinical use at the point of care. However, there is only a small number of reports of long-term studies that show the performance of deep neural networks (DNNs) in such an environment. In this study, we measured the long-term performance of a clinically optimized DNN for laryngeal glottis segmentation. We have collected the video footage for two years from an AI-powered laryngeal high-speed videoendoscopy imaging system and found that the footage image quality is stable across time. Next, we determined the DNN segmentation performance on lossy and lossless compressed data revealing that only 9% of recordings contain segmentation artifacts. We found that lossy and lossless compression is on par for glottis segmentation, however, lossless compression provides significantly superior image quality. Lastly, we employed continual learning strategies to continuously incorporate new data into the DNN to remove the aforementioned segmentation artifacts. With modest manual intervention, we were able to largely alleviate these segmentation artifacts by up to 81%. We believe that our suggested deep learning-enhanced laryngeal imaging platform consistently provides clinically sound results, and together with our proposed continual learning scheme will have a long-lasting impact on the future of laryngeal imaging.

A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity.

The complex feature characteristics and low contrast of cancer lesions, a high degree of inter-class resemblance between malignant and benign lesions, and the presence of various artifacts including hairs make automated melanoma recognition in dermoscopy images quite challenging. To date, various computer-aided solutions have been proposed to identify and classify skin cancer. In this paper, a deep learning model with a shallow architecture is proposed to classify the lesions into benign and malignant. To achieve effective training while limiting overfitting problems due to limited training data, image preprocessing and data augmentation processes are introduced. After this, the 'box blur' down-scaling method is employed, which adds efficiency to our study by reducing the overall training time and space complexity significantly. Our proposed shallow convolutional neural network (SCNN_12) model is trained and evaluated on the Kaggle skin cancer data ISIC archive which was augmented to 16485 images by implementing different augmentation techniques. The model was able to achieve an accuracy of 98.87% with optimizer Adam and a learning rate of 0.001. In this regard, parameter and hyper-parameters of the model are determined by performing ablation studies. To assert no occurrence of overfitting, experiments are carried out exploring k-fold cross-validation and different dataset split ratios. Furthermore, to affirm the robustness the model is evaluated on noisy data to examine the performance when the image quality gets corrupted.This research corroborates that effective training for medical image analysis, addressing training time and space complexity, is possible even with a lightweighted network using a limited amount of training data.

Prediction of COVID-19 diagnosis based on openEHR artefacts.

Nowadays, we are facing the worldwide pandemic caused by COVID-19. The complexity and momentum of monitoring patients infected with this virus calls for the usage of agile and scalable data structure methodologies. OpenEHR is a healthcare standard that is attracting a lot of attention in recent years due to its comprehensive and robust architecture. The importance of an open, standardized and adaptable approach to clinical data lies in extracting value to generate useful knowledge that really can help healthcare professionals make an assertive decision. This importance is even more accentuated when facing a pandemic context. Thus, in this study, a system for tracking symptoms and health conditions of suspected or confirmed SARS-CoV-2 patients from a Portuguese hospital was developed using openEHR. All data on the evolutionary status of patients in home care as well as the results of their COVID-19 test were used to train different ML algorithms, with the aim of developing a predictive model capable of identifying COVID-19 infections according to the severity of symptoms identified by patients. The CRISP-DM methodology was used to conduct this research. The results obtained were promising, with the best model achieving an accuracy of 96.25%, a precision of 99.91%, a sensitivity of 92.58%, a specificity of 99.92%, and an AUC of 0.963, using the Decision Tree algorithm and the Split Validation method. Hence, in the future, after further testing, the predictive model could be implemented in clinical decision support systems.

Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals.

OBJECTIVE: At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. METHODS: In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. MAJOR RESULTS: We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. CONCLUSION: ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. SIGNIFICANCE: We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.

MRI safety, imaging artefacts, and grid distortion evaluated for FFP3 respiratory masks worn throughout the COVID-19 pandemic.

AIM: To determine which filtering face piece (FFP3) respirators worn throughout the COVID-19 pandemic are safe for magnetic resonance imaging (MRI). MATERIALS AND METHODS: Three clinical MRI sequences were performed to assess imaging artefacts, grid distortion, and local heating for eight commercially available FFP3 respirators. All examinations were performed at Cardiff University Brain Research Imaging Centre using a 3 T Siemens Magnetom Prisma with a 64-channel head and neck coil. Each FFP3 mask was positioned on a custom-developed three-dimensional (3D) head phantom for testing. RESULTS: Five of the eight FFP3 masks contained ferromagnetic components and were regarded as "MRI unsafe". One mask was considered "MRI conditional" and only two masks were deemed "MRI safe" for both MRI staff and patients. Temperature strips positioned at the nasal bridge of the phantom did not exhibit local heating. A maximum grid distortion of 5 mm was seen in the anterior portion of the head of the ferromagnetic FFP3 masks. CONCLUSION: This study has demonstrated the importance of assessing respiratory FFP3 masks for use in and around MRI machines. Future research involving FFP3 masks can be conducted safely by following the procedures laid out in this study.

The Social Meanings of Artifacts: Face Masks in the COVID-19 Pandemic.

Since the beginning of the COVID-19 pandemic, research has explored various aspects of face mask use. While most of the research explores their effectiveness to prevent the spread of the virus, a growing body of literature has found that using face masks also has social meaning. But what social meaning does it have, and how does this meaning express itself in people's practice? Based on 413 qualitative interviews with residents in five European countries (Austria, Belgium, Germany, Ireland, and Switzerland), we found that the meanings of face masks have changed drastically during the first months of the pandemic. While in spring 2020 people wearing them had to fear stigmatization, in autumn of 2020 not wearing masks was more likely to be stigmatized. Throughout the first year of the pandemic, we found that mask wearing had multiple and partly seemingly contradictory meanings for people. They were perceived as obstacles for non-verbal communication, but also a way to affirm friendships and maintain social contacts. They also signaled specific moral or political stances on the side of face mask wearers and non-wearers alike, expressed their belonging to certain communities, or articulated concern. In sum, our findings show how face masks serve as scripts for people to navigate their lives during the COVID-19 pandemic. We conclude that public and political discussions concerning face masks should include not only evidence on the epidemiological and infectiological effects of face masks, but also on their social meanings and their social effects.

OCT-Angiography Face Mask-Associated Artifacts During the COVID-19 Pandemic.

PRCIS: Face mask wearing has no significant effects on artifacts or vessel density measurements in optic nerve head (ONH) and macular optical coherence tomography-angiography (OCT-A) scans. PURPOSE: The aim was to assess the difference in area of artifacts observed in optical OCT-A scans with and without face mask wear and to verify if mask wear interferes with OCT-A vessel density measurements. SUBJECTS AND CONTROLS: A total of 64 eyes of 10 healthy subjects, 4 ocular hypertensive, 8 glaucoma suspects, and 17 glaucoma patients were included. MATERIALS AND METHODS: High-density ONH and macula OCT-A scans were obtained in patients with and without surgical masks. Seven different artifacts (motion, decentration, defocus, shadow, segmentation failure, blink, and Z-offset) were quantitatively evaluated by 2 trained graders. The changes in the area (% of scan area) of artifacts, without and with mask wearing, and differences of vessel density were evaluated. RESULTS: Trends of increasing motion artifact area for the ONH scans [4.23 (-0.52, 8.98) %, P=0.08] and defocus artifact area for the macular scans [1.06 (-0.14, 2.26) %, P=0.08] were found with face mask wear. However, there were no significant differences in the mean % area of any artifacts (P>0.05 for all). Further, the estimated mean difference in vessel density in images acquired without and with masks was not significant for any type of artifact. CONCLUSION: Face mask wearing had no significant effect on area of artifacts or vessel density measurements. OCT-A vessel density measurements can be acquired reliably with face mask wear during the pandemic.

Hypochlorous acid and white vinegar: Artifact-free COVID-19 disinfection options for photostimulable storage phosphor plates.

OBJECTIVES: To examine the artifacts on intraoral photostimulable storage phosphor (PSP) plate images caused by 3 different disinfectants that are effective against pathogens including SARS-CoV-2. STUDY DESIGN: Nine new PSP plates, to be wiped with hypochlorous acid (HOCl) in group A, alcohol in group B, and white vinegar in group C, were distributed in 3 groups. Twelve images of each PSP plate with increasing numbers of wipes were examined for artifacts. The comparisons were evaluated by Kruskal-Wallis and post hoc tests. The reliability of the measurements was evaluated using the intraclass correlation coefficient (ICC) and Cohen kappa statistic. RESULTS: Artifacts were observed only on group B images. In terms of artifact scores, the difference between group A and group C was not statistically significant (P > .05), whereas group B artifact scores were significantly higher than group A and group C (P < .05). Intraobserver reliability was perfect (ICC and kappa of 1.0) and interobserver reliability was considered excellent (ICC = 0.985) or almost perfect (kappa = 0.956). CONCLUSIONS: HOCl and white vinegar can be alternative disinfection options for PSP plates tested in this study. Unlike alcohol, they did not produce artifacts. Additional research evaluating their effects on image quality is needed to determine if they are appropriate for disinfection.

Amplification Artifact in SARS-CoV-2 Omicron Sequences Carrying P681R Mutation, New York, USA.

Of 379 severe acute respiratory syndrome coronavirus 2 samples collected in New York, USA, we detected 86 Omicron variant sequences containing Delta variant mutation P681R. Probable explanations were co-infection with 2 viruses or contamination/amplification artifact. Repeated library preparation with fewer cycles showed the P681R calls were artifactual. Unusual mutations should be interpreted with caution.

Artifact from 131I-Contaminated Mask in Postradioiodine Therapy Scintigraphy.

A patient wearing the mandatory face mask because of the ongoing coronavirus disease 2019 pandemic underwent postradioiodine therapy scintigraphy. The spot view of the neck showed an area of uptake that was later demonstrated to be caused by contamination of the mask. This finding has led to updating the scan procedure for posttherapy scintigraphy by replacing the patients' masks before the scan acquisition.

FLIRT: A feature generation toolkit for wearable data.

BACKGROUND AND OBJECTIVE: Researchers use wearable sensing data and machine learning (ML) models to predict various health and behavioral outcomes. However, sensor data from commercial wearables are prone to noise, missing, or artifacts. Even with the recent interest in deploying commercial wearables for long-term studies, there does not exist a standardized way to process the raw sensor data and researchers often use highly specific functions to preprocess, clean, normalize, and compute features. This leads to a lack of uniformity and reproducibility across different studies, making it difficult to compare results. To overcome these issues, we present FLIRT: A Feature Generation Toolkit for Wearable Data; it is an open-source Python package that focuses on processing physiological data specifically from commercial wearables with all its challenges from data cleaning to feature extraction. METHODS: FLIRT leverages a variety of state-of-the-art algorithms (e.g., particle filters, ML-based artifact detection) to ensure a robust preprocessing of physiological data from wearables. In a subsequent step, FLIRT utilizes a sliding-window approach and calculates a feature vector of more than 100 dimensions - a basis for a wide variety of ML algorithms. RESULTS: We evaluated FLIRT on the publicly available WESAD dataset, which focuses on stress detection with an Empatica E4 wearable. Preprocessing the data with FLIRT ensures that unintended noise and artifacts are appropriately filtered. In the classification task, FLIRT outperforms the preprocessing baseline of the original WESAD paper. CONCLUSION: FLIRT provides functionalities beyond existing packages that can address unmet needs in physiological data processing and feature generation: (a) integrated handling of common wearable file formats (e.g., Empatica E4 archives), (b) robust preprocessing, and (c) standardized feature generation that ensures reproducibility of results. Nevertheless, while FLIRT comes with a default configuration to accommodate most situations, it offers a highly configurable interface for all of its implemented algorithms to account for specific needs.

The impact of the COVID-19 pandemic on Sexually Transmitted Infections surveillance data: incidence drop or artefact?

BACKGROUND: Before the COVID-19 pandemic, Sexually transmitted infections (STIs) were increasing in Europe, and Spain and Catalonia were not an exception. Catalonia has been one of the regions with the highest number of COVID-19 confirmed cases in Spain. The objective of this study was to estimate the magnitude of the decline, due to the COVID-19 pandemic, in the number of STI confirmed cases in Catalonia during the lockdown and de-escalation phases. METHODS: Interrupted time series analysis was performed to estimate the magnitude of decline in the number of STI reported confirmed cases - chlamydia, gonorrhoea, syphilis, and lymphogranuloma venereum- in Catalonia since lockdown with historical data, from March 13th to August 1st 2020, comparing the observed with the expected values. RESULTS: We found that since the start of COVID-19 pandemic the number of STI reported cases was 51% less than expected, reaching an average of 56% during lockdown (50% and 45% during de-escalation and new normality) with a maximum decrease of 72% for chlamydia and minimum of 22% for syphilis. Our results indicate that fewer STIs were reported in females, people living in more deprived areas, people with no previous STI episodes during the last three years, and in the HIV negative. CONCLUSIONS: The STI notification sharp decline was maintained almost five months after lockdown started, well into the new normality. This fact can hardly be explained without significant underdiagnosis and underreporting. There is an urgent need to strengthen STI/HIV diagnostic programs and services, as well as surveillance, as the pandemic could be concealing the real size of the already described re-emergence of STIs in most of the European countries.

Condensation Artifact.

During the ongoing COVID-19 pandemic, an artifact with hyperintense signal was observed on the brain images of a number of patients or research subjects, particularly those with heavy body weight and/or increased respiratory rate. The artifact was primarily seen on 3D or 2D sagittal or coronal T2-weighted images, although it occasionally also appeared in the axial plane. It manifested as a bright spot or a cluster of bright spots at similar locations, superior or lateral superior to the skull. This artifact was found to be caused by condensed water droplet(s) in the head coil as a consequence of the altered moisture flow pattern associated with each exhalation due to the mask on the patient. We call this artifact condensation artifact. Several strategies have been proposed to prevent or resolve the artifact. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Extracorporeal membrane oxygenation (ECMO): Radiographic appearances, complications and imaging artefacts for radiologists.

Extracorporeal membrane oxygenation (ECMO) is a form of cardiopulmonary support primarily used in cardiothoracic and intensive care unit (ICU) settings. The purpose of this review is to familiarise radiologists with the imaging features of ECMO devices, their associated complications and appropriate imaging protocols for contrast-enhanced CT imaging of ECMO patients. This paper will provide a brief introduction to ECMO and the imaging modalities utilised in ECMO patients, followed by a description of the types of ECMO available and cannula positioning. Indications and contraindications for ECMO will be outlined followed by a description of the complications associated with ECMO, which radiologists should recognise. Finally, the imaging protocol and interpretation of contrast-enhanced CT imaging in ECMO patients will be discussed. In the current clinical climate with millions of COVID-19 cases around the world and tens of thousands of critically ill patients, many requiring cardiopulmonary support in intensive care units, the use of ECMO in adults has increased, and thus so has the volume of imaging. Radiologists need to be familiar with the types of ECMO available, the correct positioning of the catheters depending on the type of ECMO being utilised, and the associated complications and imaging artefacts.

[Rapid diagnostics of novel coronavirus infection by loop-mediated isothermal amplification].

This review presents the basic principles of application of the loop-mediated isothermal amplification (LAMP) reaction for the rapid diagnosis of coronavirus infection caused by SARS-CoV-2. The basic technical details of the method, and the most popular approaches of specific and non-specific detection of amplification products are briefly described. We also discuss the first published works on the use of the method for the detection of the nucleic acid of the SARS-CoV-2 virus, including those being developed in the Russian Federation. For commercially available and published LAMP-based assays, the main analytical characteristics of the tests are listed, which are often comparable to those based on the method of reverse transcription polymerase chain reaction (RT-PCR), and in some cases are even superior. The advantages and limitations of this promising methodology in comparison to other methods of molecular diagnostics, primarily RT-PCR, are discussed, as well as the prospects for the development of technology for the detection of other infectious agents.

False ground-glass opacity and suspicion of COVID-19, beware of the technique for performing the CT.

Ground-glass opacity is a CT sign that is currently experiencing renewed interest since it is very common in patients with COVID-19. However, this sign is not specific to any disease. Besides, the possibility of false positive ground-glass opacity related to insufficient inspiration during the acquisition of the chest CT should be known. We report the case of a 36-year-old patient suspected of COVID-19 and in whom a second acquisition of chest CT was necessary to remove false ground-glass opacities that erroneously supported the diagnosis of COVID-19.