Liver involvement in patients with COVID-19 infection: A comprehensive overview of diagnostic imaging features.

During the first wave of the pandemic, coronavirus disease 2019 (COVID-19) infection has been considered mainly as a pulmonary infection. However, different clinical and radiological manifestations were observed over time, including involvement of abdominal organs. Nowadays, the liver is considered one of the main affected abdominal organs. Hepatic involvement may be caused by either a direct damage by the virus or an indirect damage related to COVID-19 induced thrombosis or to the use of different drugs. After clinical assessment, radiology plays a key role in the evaluation of liver involvement. Ultrasonography (US), computed tomography (CT) and magnetic resonance imaging (MRI) may be used to evaluate liver involvement. US is widely available and it is considered the first-line technique to assess liver involvement in COVID-19 infection, in particular liver steatosis and portal-vein thrombosis. CT and MRI are used as second- and third-line techniques, respectively, considering their higher sensitivity and specificity compared to US for assessment of both parenchyma and vascularization. This review aims to the spectrum of COVID-19 liver involvement and the most common imaging features of COVID-19 liver damage.

Benchmark methodological approach for the application of artificial intelligence to lung ultrasound data from COVID-19 patients: From frame to prognostic-level.

Automated ultrasound imaging assessment of the effect of CoronaVirus disease 2019 (COVID-19) on lungs has been investigated in various studies using artificial intelligence-based (AI) methods. However, an extensive analysis of state-of-the-art Convolutional Neural Network-based (CNN) models for frame-level scoring, a comparative analysis of aggregation techniques for video-level scoring, together with a thorough evaluation of the capability of these methodologies to provide a clinically valuable prognostic-level score is yet missing within the literature. In addition to that, the impact on the analysis of the posterior probability assigned by the network to the predicted frames as well as the impact of temporal downsampling of LUS data are topics not yet extensively investigated. This paper takes on these challenges by providing a benchmark analysis of methods from frame to prognostic level. For frame-level scoring, state-of-the-art deep learning models are evaluated with additional analysis of best performing model in transfer-learning settings. A novel cross-correlation based aggregation technique is proposed for video and exam-level scoring. Results showed that ResNet-18, when trained from scratch, outperformed the existing methods with an F1-Score of 0.659. The proposed aggregation method resulted in 59.51%, 63.29%, and 84.90% agreement with clinicians at the video, exam, and prognostic levels, respectively; thus, demonstrating improved performances over the state of the art. It was also found that filtering frames based on the posterior probability shows higher impact on the LUS analysis in comparison to temporal downsampling. All of these analysis were conducted over the largest standardized and clinically validated LUS dataset from COVID-19 patients.

Computed tomography fluoroscopy-guided percutaneous biopsy of pulmonary nodules ≤ 10 mm: retrospective analysis of procedures performed during the COVID-19 pandemic.

Objective: To evaluate the diagnostic performance of computed tomography (CT) fluoroscopy-guided percutaneous transthoracic needle biopsy (PTNB) in pulmonary nodules ≤ 10 mm during the coronavirus disease 2019 pandemic. Materials and Methods: Between January 1, 2020 and April 30, 2022, a total of 359 CT fluoroscopy-guided PTNBs were performed at an interventional radiology center. Lung lesions measured between 2 mm and 108 mm. Of the 359 PTNBs, 27 (7.5%) were performed with an 18G core needle on nodules ≤ 10 mm in diameter. Results: Among the 27 biopsies performed on nodules ≤ 10 mm, the lesions measured < 5 mm in four and 5-10 mm in 23. The sensitivity and overall diagnostic accuracy of PTNB were 100% and 92.3%, respectively. The mean dose of ionizing radiation during PTNB was 581.33 mGy*cm (range, 303-1,129 mGy*cm), and the mean biopsy procedure time was 6.6 min (range, 2-12 min). There were no major postprocedural complications. Conclusion: CT fluoroscopy-guided PTNB appears to provide a high diagnostic yield with low complication rates.

Lung Ultrasound in COVID-19 and Post-COVID-19 Patients, an Evidence-Based Approach.

OBJECTIVES: Worldwide, lung ultrasound (LUS) was utilized to assess coronavirus disease 2019 (COVID-19) patients. Often, imaging protocols were however defined arbitrarily and not following an evidence-based approach. Moreover, extensive studies on LUS in post-COVID-19 patients are currently lacking. This study analyses the impact of different LUS imaging protocols on the evaluation of COVID-19 and post-COVID-19 LUS data. METHODS: LUS data from 220 patients were collected, 100 COVID-19 positive and 120 post-COVID-19. A validated and standardized imaging protocol based on 14 scanning areas and a 4-level scoring system was implemented. We utilized this dataset to compare the capability of 5 imaging protocols, respectively based on 4, 8, 10, 12, and 14 scanning areas, to intercept the most important LUS findings. This to evaluate the optimal trade-off between a time-efficient imaging protocol and an accurate LUS examination. We also performed a longitudinal study, aimed at investigating how to eventually simplify the protocol during follow-up. Additionally, we present results on the agreement between AI models and LUS experts with respect to LUS data evaluation. RESULTS: A 12-areas protocol emerges as the optimal trade-off, for both COVID-19 and post-COVID-19 patients. For what concerns follow-up studies, it appears not to be possible to reduce the number of scanning areas. Finally, COVID-19 and post-COVID-19 LUS data seem to show differences capable to confuse AI models that were not trained on post-COVID-19 data, supporting the hypothesis of the existence of LUS patterns specific to post-COVID-19 patients. CONCLUSIONS: A 12-areas acquisition protocol is recommended for both COVID-19 and post-COVID-19 patients, also during follow-up.

New International Guidelines and Consensus on the Use of Lung Ultrasound.

Following the innovations and new discoveries of the last 10 years in the field of lung ultrasound (LUS), a multidisciplinary panel of international LUS experts from six countries and from different fields (clinical and technical) reviewed and updated the original international consensus for point-of-care LUS, dated 2012. As a result, a total of 20 statements have been produced. Each statement is complemented by guidelines and future developments proposals. The statements are furthermore classified based on their nature as technical (5), clinical (11), educational (3), and safety (1) statements.

Multi-objective automatic analysis of lung ultrasound data from COVID-19 patients by means of deep learning and decision trees.

COVID-19 raised the need for automatic medical diagnosis, to increase the physicians' efficiency in managing the pandemic. Among all the techniques for evaluating the status of the lungs of a patient with COVID-19, lung ultrasound (LUS) offers several advantages: portability, cost-effectiveness, safety. Several works approached the automatic detection of LUS imaging patterns related COVID-19 by using deep neural networks (DNNs). However, the decision processes based on DNNs are not fully explainable, which generally results in a lack of trust from physicians. This, in turn, slows down the adoption of such systems. In this work, we use two previously built DNNs as feature extractors at the frame level, and automatically synthesize, by means of an evolutionary algorithm, a decision tree (DT) that aggregates in an interpretable way the predictions made by the DNNs, returning the severity of the patients' conditions according to a LUS score of prognostic value. Our results show that our approach performs comparably or better than previously reported aggregation techniques based on an empiric combination of frame-level predictions made by DNNs. Furthermore, when we analyze the evolved DTs, we discover properties about the DNNs used as feature extractors. We make our data publicly available for further development and reproducibility.

State of the Art in Lung Ultrasound, Shifting from Qualitative to Quantitative Analyses.

Lung ultrasound (LUS) has been increasingly expanding since the 1990s, when the clinical relevance of vertical artifacts was first reported. However, the massive spread of LUS is only recent and is associated with the coronavirus disease 2019 (COVID-19) pandemic, during which semi-quantitative computer-aided techniques were proposed to automatically classify LUS data. In this review, we discuss the state of the art in LUS, from semi-quantitative image analysis approaches to quantitative techniques involving the analysis of radiofrequency data. We also discuss recent in vitro and in silico studies, as well as research on LUS safety. Finally, conclusions are drawn highlighting the potential future of LUS.

Are Lung Ultrasound Features More Severe in Children Diagnosed with Bronchiolitis after the COVID-19 Lockdown Period?

The non-pharmacological measures implemented during the SARS-CoV-2 pandemic disrupted the usual bronchiolitis seasonality. Some authors have speculated that, after the lock down period, there would be an increase in the number and severity of respiratory infections due to the re-introduction of respiratory viruses. We collected clinical, microbiological and lung ultrasound data using the classification of the Italian Society of Thoracic Ultrasound (ADET) in children with bronchiolitis during the pandemic compared to the pre-pandemic period, with the aim of assessing whether the epidemic of bronchiolitis during the pandemic was characterized by a more severe lung involvement documented by lung ultrasound. We enrolled 108 children with bronchiolitis (52 pre-pandemic and 56 COVID-19 period), with a median age of 1.74 months (interquartile range, IQR 1-3.68) and 39.8% were females. Rhinovirus detection and high-flow nasal cannula usage were both increased during the COVID-19 period, although overall need of hospitalization and pediatric intensive care unit admissions did not change during the two periods. Lung ultrasound scores were similar in the two cohorts evaluated. Conclusions: our study suggests that, despite changes in microbiology and treatments performed, lung ultrasound severity scores were similar, suggesting that that bronchiolitis during the pandemic period was no more severe than pre-pandemic period, despite children diagnosed during the pandemic had a higher, but it was not statistically significant, probably, due to small sample size, probability of being admitted.

Lung Function Tests, Quality of Life and Telemedicine: Three Windows on the Multifaceted World of Asthma in Adolescents.

Asthma is a heterogeneous disease usually characterized by chronic airway inflammation and recognized as the most prevalent chronic illness among children. Despite this, the knowledge as to how asthma affects adolescents is still scarce. One of the main management problems of asthmatic adolescents is the poor adherence to pharmacological and non-pharmacological treatments. The assessment of respiratory function and the impact on quality of life are still two crucial challenges in the management of asthmatic adolescents. Additionally, the COVID-19 pandemic has prompted physicians to explore complementary management strategies including telemedicine technologies. This review aims to provide an update on the contribution of respiratory functional tests, how asthma affects quality of life of adolescents and, finally, how telemedicine contributes to the management of adolescent asthmatics during the COVID-19 pandemic.

Lung Function Tests, Quality of Life and Telemedicine: Three Windows on the Multifaceted World of Asthma in Adolescents

Asthma is a heterogeneous disease usually characterized by chronic airway inflammation and recognized as the most prevalent chronic illness among children. Despite this, the knowledge as to how asthma affects adolescents is still scarce. One of the main management problems of asthmatic adolescents is the poor adherence to pharmacological and non-pharmacological treatments. The assessment of respiratory function and the impact on quality of life are still two crucial challenges in the management of asthmatic adolescents. Additionally, the COVID-19 pandemic has prompted physicians to explore complementary management strategies including telemedicine technologies. This review aims to provide an update on the contribution of respiratory functional tests, how asthma affects quality of life of adolescents and, finally, how telemedicine contributes to the management of adolescent asthmatics during the COVID-19 pandemic.

Deep Learning-Based Classification of Reduced Lung Ultrasound Data From COVID-19 Patients.

The application of lung ultrasound (LUS) imaging for the diagnosis of lung diseases has recently captured significant interest within the research community. With the ongoing COVID-19 pandemic, many efforts have been made to evaluate LUS data. A four-level scoring system has been introduced to semiquantitatively assess the state of the lung, classifying the patients. Various deep learning (DL) algorithms supported with clinical validations have been proposed to automate the stratification process. However, no work has been done to evaluate the impact on the automated decision by varying pixel resolution and bit depth, leading to the reduction in size of overall data. This article evaluates the performance of DL algorithm over LUS data with varying pixel and gray-level resolution. The algorithm is evaluated over a dataset of 448 LUS videos captured from 34 examinations of 20 patients. All videos are resampled by a factor of 2, 3, and 4 of original resolution, and quantized to 128, 64, and 32 levels, followed by score prediction. The results indicate that the automated scoring shows negligible variation in accuracy when it comes to the quantization of intensity levels only. Combined effect of intensity quantization with spatial down-sampling resulted in a prognostic agreement ranging from 73.5% to 82.3%.These results also suggest that such level of prognostic agreement can be achieved over evaluation of data reduced to 32 times of its original size. Thus, laying foundation to efficient processing of data in resource constrained environments.

Investigating training-test data splitting strategies for automated segmentation and scoring of COVID-19 lung ultrasound images.

Ultrasound in point-of-care lung assessment is becoming increasingly relevant. This is further reinforced in the context of the COVID-19 pandemic, where rapid decisions on the lung state must be made for staging and monitoring purposes. The lung structural changes due to severe COVID-19 modify the way ultrasound propagates in the parenchyma. This is reflected by changes in the appearance of the lung ultrasound images. In abnormal lungs, vertical artifacts known as B-lines appear and can evolve into white lung patterns in the more severe cases. Currently, these artifacts are assessed by trained physicians, and the diagnosis is qualitative and operator dependent. In this article, an automatic segmentation method using a convolutional neural network is proposed to automatically stage the progression of the disease. 1863 B-mode images from 203 videos obtained from 14 asymptomatic individual,14 confirmed COVID-19 cases, and 4 suspected COVID-19 cases were used. Signs of lung damage, such as the presence and extent of B-lines and white lung areas, are manually segmented and scored from zero to three (most severe). These manually scored images are considered as ground truth. Different test-training strategies are evaluated in this study. The results shed light on the efficient approaches and common challenges associated with automatic segmentation methods.

Integrating Domain Knowledge Into Deep Networks for Lung Ultrasound With Applications to COVID-19.

Lung ultrasound (LUS) is a cheap, safe and non-invasive imaging modality that can be performed at patient bed-side. However, to date LUS is not widely adopted due to lack of trained personnel required for interpreting the acquired LUS frames. In this work we propose a framework for training deep artificial neural networks for interpreting LUS, which may promote broader use of LUS. When using LUS to evaluate a patient's condition, both anatomical phenomena (e.g., the pleural line, presence of consolidations), as well as sonographic artifacts (such as A- and B-lines) are of importance. In our framework, we integrate domain knowledge into deep neural networks by inputting anatomical features and LUS artifacts in the form of additional channels containing pleural and vertical artifacts masks along with the raw LUS frames. By explicitly supplying this domain knowledge, standard off-the-shelf neural networks can be rapidly and efficiently finetuned to accomplish various tasks on LUS data, such as frame classification or semantic segmentation. Our framework allows for a unified treatment of LUS frames captured by either convex or linear probes. We evaluated our proposed framework on the task of COVID-19 severity assessment using the ICLUS dataset. In particular, we finetuned simple image classification models to predict per-frame COVID-19 severity score. We also trained a semantic segmentation model to predict per-pixel COVID-19 severity annotations. Using the combined raw LUS frames and the detected lines for both tasks, our off-the-shelf models performed better than complicated models specifically designed for these tasks, exemplifying the efficacy of our framework.

Occupational hand dermatitis web survey in a university hospital during COVID-19 pandemic: the SHIELD study.

BACKGROUND: Occupational hand dermatitis (OHD) is a skin disease occurring on employees' hands in certain jobs. Little is known about prevalence, incidence and characteristics of this adverse skin reaction and its associated risk factors during COVID-19 pandemic. To evaluate both prevalence and incidence of OHD and associated risk factors in Italian clinicians. METHODS: A cross-sectional study was performed using a self-report questionnaire. RESULTS: Two hundred and thirty clinicians responded to the survey and 82% of responders did not report previous OHD history before the COVID-19 pandemic. Daily use of gloves was reported by 80% of responders. OHD prevalence was 18%, while incidence was 80%. We found a protective effect on symptom occurrence for vinyl/nitrile gloves if the time with gloves was ≥ 6 hours per day. CONCLUSIONS: This survey reveals a high OHD incidence in an Italian population of clinicians. Furthermore, wearing vinyl/nitrile gloves for at least 6 hours a day had a protective effect on symptom onset.

Diet Intervention Study through Telemedicine Assistance for Systemic Nickel Allergy Syndrome Patients during the COVID-19 Pandemic.

BACKGROUND: Restrictions due to the COVID-19 pandemic limited patients' access to hospital care. The aims of this study were to assess dietary nutritional status, quality of life (QoL), and adherence to dietary therapy before and after 30-day personalized diet therapy through telenutrition tools in patients with systemic nickel allergic syndrome (SNAS). METHODS: Each SNAS patient underwent the following allergological procedures: (a) face-to-face visit (nutritional visit and QoL evaluation) with prescription of one out of five personalized and balanced dietary plans different for calorie intake, (b) video call visit for dietary evaluation and assessment of adherence to diet after 15 days, and (c) video call visit for dietary and QoL evaluation and assessment of adherence to diet therapy after 30 days (end of study). RESULTS: We enrolled 20 SNAS patients. After 15 and 30 days, we found a statistically significant improvement in anthropometric findings after diet therapy, a significant adherence rate to low-nickel diet (60% and 80%, respectively), and an improvement in QoL with an increase in almost all psychometric indices. CONCLUSIONS: Our study demonstrates that telenutrition can be a valid tool to monitor nutritional status and adherence to balanced low-Ni diet positively affecting QoL in SNAS patients during the COVID-19 pandemic.

Worldwide management of hepatocellular carcinoma during the COVID-19 pandemic.

The coronavirus disease 2019 (COVID-19) pandemic has impacted hospital organization, with the necessity to quickly react to face the pandemic. The management of the oncological patient has been modified by necessity due to different allocation of nurses and doctors, requiring new strategies to guarantee the correct assistance to the patients. Hepatocellular carcinoma, considered as one of the most aggressive types of liver cancer, has also required a different management during this period in order to optimize the management of patients at risk for and with this cancer. The aim of this document is to review recommendations on hepatocellular carcinoma surveillance and management, including surgery, liver transplantation, interventional radiology, oncology, and radiotherapy. Publications and guidelines from the main scientific societies worldwide regarding the management of hepatocellular carcinoma during the COVID-19 pandemic were reviewed.

Deep learning applied to lung ultrasound videos for scoring COVID-19 patients: A multicenter study.

In the current pandemic, lung ultrasound (LUS) played a useful role in evaluating patients affected by COVID-19. However, LUS remains limited to the visual inspection of ultrasound data, thus negatively affecting the reliability and reproducibility of the findings. Moreover, many different imaging protocols have been proposed, most of which lacked proper clinical validation. To address these problems, we were the first to propose a standardized imaging protocol and scoring system. Next, we developed the first deep learning (DL) algorithms capable of evaluating LUS videos providing, for each video-frame, the score as well as semantic segmentation. Moreover, we have analyzed the impact of different imaging protocols and demonstrated the prognostic value of our approach. In this work, we report on the level of agreement between the DL and LUS experts, when evaluating LUS data. The results show a percentage of agreement between DL and LUS experts of 85.96% in the stratification between patients at high risk of clinical worsening and patients at low risk. These encouraging results demonstrate the potential of DL models for the automatic scoring of LUS data, when applied to high quality data acquired accordingly to a standardized imaging protocol.