Diagnostic Value of Imaging Modalities for COVID-19: Scoping Review

Fully automated measurement of noise, signal-to-noise ratio, and contrast-to-noise ratio on chest CT images: feasibility and efficiency

BACKGROUND

Rapid and accurate measurement of computed tomography (CT) image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) is a clinical challenge.

PURPOSE

To explore the feasibility of intelligent measurement of chest CT image noise, SNR, and CNR.

MATERIAL AND METHODS

A total of 300 chest CT scans were included in the study, which was divided into research dataset, internal test dataset, and external test dataset. Based on the research dataset, automatically segment and measure the average CT values and standard deviation (SD) of CT values for background air and lung field under different thresholds to obtain noise, SNR, and CNR results. Using the results of manual measurements as the reference standard, we determine the optimal threshold with the highest consistency. Using internal and external test datasets, validate the consistency of automated measurements of noise, SNR, and CNR at the optimal CT threshold with reference standards.

RESULTS

With background air set at -900 HU and lung field at -800 HU as thresholds, the automated measurements of noise, SNR, and CNR demonstrate the highest consistency with the reference standards. At the optimal threshold, the noise, SNR, and CNR measured automatically on both the internal (intraclass correlation coefficient [ICC] = 0.85-0.96) and external (ICC = 0.75-0.85) test datasets exhibit high consistency with their respective reference standards.

CONCLUSION

The method we explored can intelligently measure the noise, SNR, and CNR of chest CT images, exhibits high consistency with radiologists, and offers a novel tool for image quality evaluation and analysis.

Evaluating Acute Pulmonary Changes of Coronavirus 2019: Comparative Analysis of the Pertinent Modalities

This review explores imaging's crucial role in acute Coronavirus Disease 2019 (COVID-19) assessment. High Resolution Computer Tomography is especially effective in detection of lung abnormalities. Chest radiography has limited utility in the initial stages of COVID-19 infection. Lung Ultrasound has emerged as a valuable, radiation-free tool in critical care, and Magnetic Resonance Imaging shows promise as a Computed Tomography alternative. Typical and atypical findings of COVID-19 by each of these modalities are discussed with emphasis on their prognostic value. Considerations for pediatric and immunocompromised cases are outlined. A comprehensive diagnostic approach is recommended, as radiological diagnosis remains challenging in the acute phase.

A Literature Review on the Relative Diagnostic Accuracy of Chest CT Scans versus RT-PCR Testing for COVID-19 Diagnosis

BACKGROUND

Reverse transcription polymerase chain reaction (RT-PCR) is the main technique used to identify COVID-19 from respiratory samples. It has been suggested in several articles that chest CTs could offer a possible alternate diagnostic tool for COVID-19; however, no professional medical body recommends using chest CTs as an early COVID-19 detection modality. This literature review examines the use of CT scans as a diagnostic tool for COVID-19.

METHOD

A comprehensive search of research works published in peer-reviewed journals was carried out utilizing precisely stated criteria. The search was limited to English-language publications, and studies of COVID-19-positive patients diagnosed using both chest CT scans and RT-PCR tests were sought. For this review, four databases were consulted: these were the Cochrane and ScienceDirect catalogs, and the CINAHL and Medline databases made available by EBSCOhost.

FINDINGS

In total, 285 possibly pertinent studies were found during an initial search. After applying inclusion and exclusion criteria, six studies remained for analysis. According to the included studies, chest CT scans were shown to have a 44 to 98% sensitivity and 25 to 96% specificity in terms of COVID-19 diagnosis. However, methodological limitations were identified in all studies included in this review.

CONCLUSION

RT-PCR is still the suggested first-line diagnostic technique for COVID-19; while chest CT is adequate for use in symptomatic patients, it is not a sufficiently robust diagnostic tool for the primary screening of COVID-19.

Lung Involvement Patterns in COVID-19: CT Scan Insights and Prognostic Implications From a Tertiary Care Center in Southern India

Background COVID-19, caused by the SARS-CoV-2 virus, has presented an unparalleled challenge and a profound learning curve globally. Among the myriad of investigative tools, CT scans of the chest have become instrumental in assessing the magnitude of lung involvement in the pathogenesis of this disease. Objectives This study aimed to evaluate the distribution and patterns of lung involvement depicted in the CT chest scans of COVID-19 patients admitted to a specialized tertiary care center located in a southern state of India. Methods With clearance secured from the Institutional Ethics Committee, an analytical cross-sectional study was conducted. It encompassed CT chest images from all symptomatic COVID-19 patients within the designated study center during the specified study timeline. Subsequent data analysis ensued. Results Among the 1066 COVID-19 patients evaluated, ground-glass opacities (GGO) were the predominant lung involvement pattern. Distinct patterns, such as GGOs combined with solid consolidation or atelectasis, were noted, with the highest mortality linked to GGOs paired with pneumomediastinum (PM). Data underscored a direct correlation between the extent of lung involvement and patient prognosis, with specific lung regions, namely the right apical, right posterior, right superior basal, left superior lingular, and left inferior lingular segments, showing frequent involvement. Conclusion Amidst the pandemic, our study emphasizes that ground-glass opacities on CT scans are robust indicators of COVID-19 in RT-PCR-positive patients. Early identification can enhance patient management, with findings highlighting a strong link between lung involvement and prognosis. This insight aids in refining patient triage, while further research is warranted to delve deeper into variations in lung involvement and guide treatment advancements.

Computed tomographic diaphragmatic thickness: a promising method for the evaluation of diaphragmatic muscle in cardiopulmonary diseased cats

Diaphragmatic dysfunction (DD) is defined as a weakening of the diaphragmatic muscle and can be an undetected cause of dyspnea. The objectives of this study were to explore the appropriate diaphragmatic location, measure diaphragmatic thickness (DT), evaluate the effect of intrinsic factors on DT, and compare DT between healthy and diseased cats, using 33 healthy cats and 15 diseased cats. A retrospective, analytical, case-control study using thoraco-abdominal feline computed tomography (CT) was performed. Two radiologists independently reviewed all images to verify inter- and intra-observer reliabilities and the best position for measuring DT. The effects of sex, age, and body weight were also studied, and cutoff values for detecting DT abnormalities were established. The results showed that the appropriate location for DT measurement was at the ventral border of the cranial endplate of the first lumbar vertebral body (L1) due to its highest intra- and inter-observer reliabilities. At this location, a significant difference in DT between the right and left hemidiaphragms (p = 0.01) was observed. Only sex had an impact on DT values. Interestingly, the DTs of cardiorespiratory-affected cats, both on the right and left sides, were significantly thinner than those of healthy cats. In conclusion, CT imaging is a reliable imaging method for determining diaphragmatic muscular atrophy. The ventral border of the cranial endplate of L1 is recommended for measuring the DT, and sex was the only factor affecting the DT measurement.

Customizable Scintillator of Cs3 Cu2 I5 :2% In+ @Paper for Large-Area X-Ray Imaging

High-resolution X-ray imaging is increasingly required for medical diagnosis and large-area detection. However, the issues of scattering and optical crosstalk are limiting the spatial resolution of the indirect X-ray imaging. In this study, a feasible and efficient strategy is proposed to in situ synthesize flexible Cs3 Cu2 I5 :2%In+ @paper as a superior scintillator film, which can be scaled up to an ultra-large area of 4800 cm2 . The as-obtained Cs3 Cu2 I5 :2%In+ @paper performs a fascinating photoluminescence quantum efficiency up to 88.14%, a steady state light yield of 70169 photons/MeV, and spatial resolution of 15 lp mm-1 . Moreover, the suppressed physical scattering and optical crosstalk of the corresponding film are demonstrated. Accordingly, this work explores a feasible fabrication of customizable scintillation films with large area for high-resolution X-ray detection.

COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests

Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.

Lessons learnt from the first wave of COVID-19 in Damascus, Syria: a multicentre retrospective cohort study

OBJECTIVES

The decade-long Syrian war led to fragile health infrastructures lacking in personal and physical resources. The public health of the Syrian population was, therefore, vulnerable to the COVID-19 pandemic, which devastated even well-resourced healthcare systems. Nevertheless, the officially reported incidence and fatality rates were significantly lower than the forecasted numbers.

DESIGN

A retrospective cohort study.

SETTING

The four main responding hospitals in Damascus, which received most of the cases during the first pandemic wave in Syria (i.e., June-August 2020).

PARTICIPANTS

One thousand one hundred eighty-four patients who were managed as inpatient COVID-19 cases.

PRIMARY AND SECONDARY OUTCOME MEASURES

The records of hospitalised patients were screened for clinical history, vital signs, diagnosis modality, major interventions and status at discharge.

RESULTS

The diagnostic and therapeutic preparedness for COVID-19 was significantly heterogeneous among the different centres and depleted rapidly after the arrival of the first wave. Only 32% of the patients were diagnosed based on positive reverse transcription-PCR tests. Five hundred twenty-six patients had an indication for intensive care unit admission, but only 82% of them received it. Two hundred fifty-seven patients needed mechanical ventilation, but ventilators were not available to 14% of them, all of whom died. Overall mortality during hospitalisation reached 46% and no significant difference was found in fatality between those who received and did not receive these care options.

CONCLUSIONS

The Syrian healthcare system expressed minor resilience in facing the COVID-19 pandemic, as its assets vanished swiftly with a limited number of cases. This forced physicians to reserve resources (e.g., ventilators) for the most severe cases, which led to poor outcomes of in-hospital management and limited the admission capacity for milder cases. The overwhelmed system additionally suffered from constrained coordination, suboptimal allocation of the accessible resources and a severe inability to informatively report on the catastrophic pandemic course in Syria.

[18F]FDG-PET/CT in mechanically ventilated critically ill patients with COVID-19 ARDS and persistent inflammation

Purpose

We report the findings of four critically ill patients who underwent an [18F]FDG-PET/CT because of persistent inflammation during the late phase of their COVID-19.

Methods

Four mechanically ventilated patients with COVID-19 were retrospectively discussed in a research group to evaluate the added value of [18F]FDG-PET/CT.

Results

Although pulmonary PET/CT findings differed, bilateral lung anomalies could explain the increased CRP and leukocytes in all patients. This underscores the limited ability of the routine laboratory to discriminate inflammation from secondary infections. Based on PET/CT findings, a secondary infection/inflammatory focus was suspected in two patients (pancreatitis and gastritis). Lymphadenopathy was present in patients with a detectable SARS-CoV-2 viral load. Muscle uptake around the hips or shoulders was observed in all patients, possibly due to the process of heterotopic ossification.

Conclusion

This case series illustrates the diagnostic potential of [18F]FDG-PET/CT imaging in critically ill patients with persistent COVID-19 for the identification of other causes of inflammation and demonstrates that this technique can be performed safely in mechanically ventilated critically ill patients.

COVID-19 chest X-ray image analysis by threshold-based segmentation

COVID-19 is a severe acute respiratory syndrome that has caused a major ongoing pandemic worldwide. Imaging systems such as conventional chest X-ray (CXR) and computed tomography (CT) were proven essential for patients due to the lack of information about the complications that could result from this disease. In this study, the aim was to develop and evaluate a method for automatic diagnosis of COVID-19 using binary segmentation of chest X-ray images. The study used frontal chest X-ray images of 27 infected and 19 uninfected individuals from Kaggle COVID-19 Radiography Database, and applied binary segmentation and quartering in MATLAB to analyze the images. The binary images of the lung were split into four quarters; Q1 = right upper quarter, Q2 = left upper quarter, Q3 = right lower, and Q4 = left lower. The results showed that COVID-19 patients had a higher percentage of attenuation in the lower lobes of the lungs (p-value < 0.00001) compared to healthy individuals, which is likely due to ground-glass opacities and consolidations caused by the infection. The ratios of white pixels in the four quarters of the X-ray images were calculated, and it was found that the left lower quarter had the highest number of white pixels but without a statistical significance compared to right lower quarter (p-value = 0.102792). This supports the theory that COVID-19 primarily affects the lower and lateral fields of the lungs, and suggests that the virus is accumulated mostly in the lower left quarter of the lungs. Overall, this study contributes to the understanding of the impact of COVID-19 on the respiratory system and can help in the development of accurate diagnostic methods.

A Review of Crucial Radiological Investigations in the Management of COVID-19 Cases

Chest X-ray, chest CT, and lung ultrasound are the most common radiological interventions used in the diagnosis and management of coronavirus disease 2019 (COVID-19) patients. The purpose of this literature review, which was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, is to determine which radiological investigation is crucial for that purpose. PubMed, Medline, American Journal of Radiology (AJR), Public Library of Science (PLOS), Elsevier, National Center for Biotechnology Information (NCBI), and ScienceDirect were explored. Seventy-two articles were reviewed for potential inclusion, including 50 discussing chest CT, 15 discussing chest X-ray, five discussing lung ultrasound, and two discussing COVID-19 epidemiology. The reported sensitivities and specificities for chest CT ranged from 64 to 98% and 25 to 88%, respectively. The reported sensitivities and specificities for chest X-rays ranged from 33 to 89% and 11.1 to 88.9%, respectively. The reported sensitivities and specificities for lung ultrasound ranged from 93 to 96.8% and 21.3 to 95%, respectively. The most common findings on chest CT include ground glass opacities and consolidation. The most common findings on chest X-rays include opacities, consolidation, and pleural effusion. The data indicate that chest CT is the most effective radiological tool for the diagnosis and management of COVID-19 patients. The authors support the continued use of reverse transcription polymerase chain reaction (RT-PCR), along with physical examination and contact history, for such diagnosis. Chest CT could be more appropriate in emergency situations when quick triage of patients is necessary before RT-PCR results are available. CT can also be used to visualize the progression of COVID-19 pneumonia and to identify potential false positive RT-PCR results. Chest X-ray and lung ultrasound are acceptable in situations where chest CT is unavailable or contraindicated.

COVID-19 pandemic experience of diagnostic radiographers: A Singapore survey

INTRODUCTION

Diagnostic Radiographers (DR) are the frontline responders during the COVID-19 outbreak, providing essential diagnostic imaging services for screening and monitoring of suspected and confirmed patients. Understanding the experience and perceptions of DR towards the COVID-19 outbreak enables radiography leaders to guide changes in the approach to managing response to future health outbreaks. This study aims to document the experiences of DR in Singapore during the COVID-19 pandemic.

METHODS

All DR practising in Singapore institutions were invited to participate in an online survey, disseminated by the Singapore Society of Radiographers (SSR). The survey assessed the attitudes and perceptions of the respondents on the COVID-19 pandemic. The Holmes and Rahe Stress Scale was used to identify the respondents' life events closely related to the pandemic. Data collection took place from 5 July 2020 to 5 September 2020.

RESULTS

A total of 123 DR responded to the survey, where 89.4% of the respondents had been involved in the imaging of suspected or confirmed COVID-19 patients. Those performing General Radiography had the highest number of cases - 300 cases a month. The fear of transmitting COVID-19 to their family presented as the primary stressor (77.2%), followed by the lack of manpower (73.2%). The global themes that emerged from the study were (1) adapting to change and (2) quality of support.

CONCLUSION

Radiology departments in Singapore were able to cope with the high demands of the pandemic in terms of the provision of information, supplies, and physical equipment. However, they were less prepared to handle human factors such as mental health and staff morale. The safety and well-being of staff should not be compromised to reduce staff anxiety while performing their duties. Strategies to improve their ability to adapt to changes and provision of quality support are necessary measures in future pandemic situations.

Multisystem Imaging Recommendations/Guidelines: In the Pursuit of Precision Oncology

With an increasing rate of cancers in almost all age groups and advanced screening techniques leading to an early diagnosis and longer longevity of patients with cancers, it is of utmost importance that radiologists assigned with cancer imaging should be prepared to deal with specific expected and unexpected circumstances that may arise during the lifetime of these patients. Tailored integration of preventive and curative interventions with current health plans and global escalation of efforts for timely diagnosis of cancers will pave the path for a cancer-free world. The commonly encountered circumstances in the current era, complicating cancer imaging, include coronavirus disease 2019 infection, pregnancy and lactation, immunocompromised states, bone marrow transplant, and screening of cancers in the relevant population. In this article, we discuss the imaging recommendations pertaining to cancer screening and diagnosis in the aforementioned clinical circumstances.

E-GCS: Detection of COVID-19 through classification by attention bottleneck residual network

Background

Recently,  the coronavirus disease 2019 (COVID-19)  has caused mortality of many people globally. Thus, there existed a need to detect this disease to prevent its further spread. Hence, the study aims to predict COVID-19 infected patients based on deep learning (DL) and image processing.

Objectives

The study intends to classify the normal and abnormal cases of COVID-19 by considering three different medical imaging modalities namely ultrasound imaging, X-ray images and CT scan images through introduced attention bottleneck residual network (AB-ResNet). It also aims to segment the abnormal infected area from normal images for localizing localising the disease infected area through the proposed edge based graph cut segmentation (E-GCS).

Methodology

AB-ResNet is used for classifying images whereas E-GCS segment the abnormal images. The study possess various advantages as it rely on DL and possess capability for accelerating the training speed of deep networks. It also enhance the network depth leading to minimum parameters, minimising the impact of vanishing gradient issue and attaining effective network performance with respect to better accuracy.

Results/Conclusion

Performance and comparative analysis is undertaken to evaluate the efficiency of the introduced system and results explores the efficiency of the proposed system in COVID-19 detection with high accuracy (99%).

Towards smart diagnostic methods for COVID-19: Review of deep learning for medical imaging

The infectious disease known as COVID-19 has spread dramatically all over the world since December 2019. The fast diagnosis and isolation of infected patients are key factors in slowing down the spread of this virus and better management of the pandemic. Although the CT and X-ray modalities are commonly used for the diagnosis of COVID-19, identifying COVID-19 patients from medical images is a time-consuming and error-prone task. Artificial intelligence has shown to have great potential to speed up and optimize the prognosis and diagnosis process of COVID-19. Herein, we review publications on the application of deep learning (DL) techniques for diagnostics of patients with COVID-19 using CT and X-ray chest images for a period from January 2020 to October 2021. Our review focuses solely on peer-reviewed, well-documented articles. It provides a comprehensive summary of the technical details of models developed in these articles and discusses the challenges in the smart diagnosis of COVID-19 using DL techniques. Based on these challenges, it seems that the effectiveness of the developed models in clinical use needs to be further investigated. This review provides some recommendations to help researchers develop more accurate prediction models.

Greater central airway luminal area in people with COVID-19: a case-control series

Respiratory epithelium in the conducting airways of the human body is one of the primary targets of SARS-CoV-2 infection, however, there is a paucity of studies describing the association between COVID-19 and physical characteristics of the conducting airways. To better understand the pathophysiology of COVID-19 on the size of larger conducting airways, we determined the luminal area of the central airways in patients with a history of COVID-19 compared to a height-matched cohort of controls using a case-control study design. Using three-dimensional reconstruction from low-dose high-resolution computed tomography, we retrospectively assessed airway luminal cross-sectional area in 114 patients with COVID-19 (66 females, 48 males) and 114 healthy, sex- and height-matched controls (66 females, 48 males). People with a history of smoking, cardiopulmonary disease, or a body mass index greater than 40 kg·m-2 were excluded. Luminal areas of seven conducting airways were analyzed, including trachea, left and right main bronchus, intermediate bronchus, left and right upper lobe, and left lower lobe. For the central conducting airways, luminal area was ~ 15% greater patients with COVID-19 compared to matched controls (p < 0.05). Among patients with COVID-19, there were generally no differences in the luminal areas of the conducting airways between hospitalized patients compared to patients who did not require COVID-19-related hospitalization. Our findings suggest that males and females with COVID-19 have pathologically larger conducting airway luminal areas than healthy, sex- and height-matched controls.

Comparison of perceived general health status between suspected and confirmed cases of COVID-19 and identifying the nursing diagnoses: A cross-sectional study

AIM

This study aimed to examine the differences in health status between patients with confirmed COVID-19 and those suspected (other diagnosis) and to identify nursing diagnoses using a structured checklist from a hospital in China.

DESIGN

Cross-sectional study design was used.

METHODS

One hundred sixty COVID-19 confirmed, and suspected patients were conveniently selected. A structured survey and checklist were utilized. Independent t test and chi-square test were employed to compare the mean between patients with confirmed coronavirus infection and others. A two-sided p-value of .05 or less is considered statistically significant.

RESULTS

The study yielded a response rate of 93.6%. The result indicated that patients with confirmed coronavirus infection have a higher proportion of perceived General Health Status than inpatients with suspected (other) diagnoses. The finding also indicated that ineffective airway clearance, hyperthermia, imbalanced nutrition less than body requirement and sleep pattern disturbance were the main nursing diagnoses identified.

Comparative study of the usefulness of risk score assessment in the early stages of COVID ‐19 affected pregnancies: Omicron variant versus previous variants

Aim

To evaluate the utility of the risk score in assessing the current status and prognosis of COVID‐19 in pregnancy.

Methods

Seventy‐seven cases affected before the Omicron variant epidemic and 50 pregnant cases affected by the Omicron variant were included. The risk score consists of maternal background, current condition, and examination findings. We determined the risk score in the early stages of disease onset.

Results

There were no significant differences in the maternal or gestational ages between the groups. The risk score was significantly lower in the After‐Group patients (those affected during the Omicron epoch), while 14.3% of the Before‐Group patients (those affected during the pre‐Delta and Delta epochs), experienced a worsening of disease after the visit to the center, whereas none of the After‐Group patients did. The Before Group's frequency of risk score items was higher among the two groups for “fever for ≥48 h,” “mild pneumonia image,” and “blood tests,” whereas “disease onset 14 days after the second vaccination” was increased in After Group. The blood test parameters for platelet count, C‐reactive protein, and D‐dimer levels were not significantly different between the groups.

Conclusions

The risk score system appeared superior in detecting deteriorating cases. There were no cases of post‐illness deterioration in the After‐Group, suggesting that cases of the Omicron variant in pregnancy may have had a less severe course compared to that of previous variants. However, there was no significant difference between the groups in terms of a specific blood test evaluation, suggesting the need for a combined evaluation of cases affected during pregnancy.

Genetic algorithm based image reconstruction applying the digital holography process with the Discrete Orthonormal Stockwell Transform technique for diagnosis of COVID-19

World Health Organization has described the real-time reverse transcription-polymerase chain reaction test method for the diagnosis of the novel coronavirus disease (COVID-19). However, the limited number of test kits, the long-term results of the tests, the high probability of the disease spreading during the test and imaging without focused images necessitate the use of alternative diagnostic methods such as chest X-ray (CXR) imaging. The storage of data obtained for the diagnosis of the disease also poses a major problem. This causes misdiagnosis and delays treatment. In this work, we propose a hybrid 3D reconstruction method of CXR images (CXRI) to detect coronavirus pneumonia and prevent misdiagnosis on CXRI. We used the digital holography technique (DHT) for obtaining a priori information of CXRI stored in created digital hologram (CDH). In this way, the elimination of the storage problem that requires high space was revealed. In addition, Discrete Orthonormal S-Transform (DOST) is applied to the reconstructed CDH image obtained by using DHT. This method is called CDH_DHT_DOST. A multiresolution spatial-frequency representation of the lung images that belong to healthy people and diseased people with the COVID-19 virus is obtained by using the CDH_DHT_DOST. Moreover, the genetic algorithm (GA) is adopted for the reconstruction process for optimization of the CDH image and then DOST is applied. This hybrid method is called CDH_GA_DOST. Finally, we compare the results obtained from CDH_DHT_DOST and CDH_GA_DOST. The results show the feasibility of reconstructing CXRI with CDH_GA_DOST. The proposed method holds promises to meet demands for the detection of the COVID-19 virus.

An effective detection of COVID-19 using adaptive dual-stage horse herd bidirectional long short-term memory framework

COVID-19 is a quickly increasing severe viral disease that affects the human beings as well as animals. The increasing amount of infection and death due to COVID-19 needs timely detection. This work presented an innovative deep learning methodology for the prediction of COVID-19 patients with chest x-ray images. Chest x-ray is the most effective imaging technique for predicting the lung associated diseases. An effective approach with adaptive dual-stage horse herd bidirectional LSTM model is presented for the classification of images into normal, lung opacity, viral pneumonia, and COVID-19. Initially, the input images are preprocessed using modified histogram equalization approach. This is utilized to improve the contrast of the images by changing low-resolution images into high-resolution images. Subsequently, an extended dual tree complex wavelet with trigonometric transform is introduced to extract the high-density features to decrease the complexity of features. Moreover, the dimensionality of the features reduced by adaptive beetle antennae search optimization is utilized. This approach enhances the performance of disease classification by reducing the computational complexity. Finally, an adaptive dual-stage horse herd bidirectional LSTM model is utilized for the classification of images into normal, viral pneumonia, lung opacity, and COVID-19. The implementation platform used in the work is PYTHON. The performance of the presented approach is proved by comparing with the existing approaches in accuracy (99.07%), sensitivity (97.6%), F-measure (97.1%), specificity (99.36%), kappa coefficient (97.7%), precision (98.56%), and area under the receiver operating characteristic curve (99%) for COVID-19 chest x-ray database.

COVID-19: A literature review of the impact on diagnostic radiography students

INTRODUCTION

COVID-19 is a highly contagious viral disease declared a global pandemic in March 2020. Throughout the pandemic, radiography students have been working in hospitals on the frontline. The review aimed to search for evidence of the impact COVID-19 has had on diagnostic radiography students and consider whether additional support and learning needs to be implemented.

METHODS

A literature search strategy applied keywords, BOOLEAN search operators, and eligibility criteria on PubMed, Medline, and Google Scholar databases. Cormack's (2000) critique framework was chosen to methodologically appraise the mixed-method studies to evaluate the quality, validity and rigour.

RESULTS

The search decisions were displayed in a PRISMA flowchart to evidence the process to identify the found articles comprised of two surveys, two semi-structured interviews and one case study. The findings identified common and reoccurring themes of personal protective equipment, mental wellbeing, accommodation and travel, assessments and learning, and transitioning to registration.

CONCLUSION

The literature suggests that students felt positive impacts of the pandemic, such as being prepared for registration. However, negative effects included the fear of contracting the virus, anxieties of working with ill patients, impracticalities of accommodation and travel during clinical placement, and the adaption to online learning.

IMPLICATIONS FOR PRACTICE

Clinical staff and universities need to work together to ensure students are mentally and physically supported during the pandemic. Regular meetings and agreed channels of communication with students will allow any issues to be brought to attention and addressed. In addition, employers should recognise that newly qualified radiographers will need extra support.

Review of Machine Learning in Lung Ultrasound in COVID-19 Pandemic

Ultrasound imaging of the lung has played an important role in managing patients with COVID-19-associated pneumonia and acute respiratory distress syndrome (ARDS). During the COVID-19 pandemic, lung ultrasound (LUS) or point-of-care ultrasound (POCUS) has been a popular diagnostic tool due to its unique imaging capability and logistical advantages over chest X-ray and CT. Pneumonia/ARDS is associated with the sonographic appearances of pleural line irregularities and B-line artefacts, which are caused by interstitial thickening and inflammation, and increase in number with severity. Artificial intelligence (AI), particularly machine learning, is increasingly used as a critical tool that assists clinicians in LUS image reading and COVID-19 decision making. We conducted a systematic review from academic databases (PubMed and Google Scholar) and preprints on arXiv or TechRxiv of the state-of-the-art machine learning technologies for LUS images in COVID-19 diagnosis. Openly accessible LUS datasets are listed. Various machine learning architectures have been employed to evaluate LUS and showed high performance. This paper will summarize the current development of AI for COVID-19 management and the outlook for emerging trends of combining AI-based LUS with robotics, telehealth, and other techniques.

Truncating fined-tuned vision-based models to lightweight deployable diagnostic tools for SARS-CoV-2 infected chest X-rays and CT-scans

In such a brief period, the recent coronavirus (COVID-19) already infected large populations worldwide. Diagnosing an infected individual requires a Real-Time Polymerase Chain Reaction (RT-PCR) test, which can become expensive and limited in most developing countries, making them rely on alternatives like Chest X-Rays (CXR) or Computerized Tomography (CT) scans. However, results from these imaging approaches radiated confusion for medical experts due to their similarities with other diseases like pneumonia. Other solutions based on Deep Convolutional Neural Network (DCNN) recently improved and automated the diagnosis of COVID-19 from CXRs and CT scans. However, upon examination, most proposed studies focused primarily on accuracy rather than deployment and reproduction, which may cause them to become difficult to reproduce and implement in locations with inadequate computing resources. Therefore, instead of focusing only on accuracy, this work investigated the effects of parameter reduction through a proposed truncation method and analyzed its effects. Various DCNNs had their architectures truncated, which retained only their initial core block, reducing their parameter sizes to <1 M. Once trained and validated, findings have shown that a DCNN with robust layer aggregations like the InceptionResNetV2 had less vulnerability to the adverse effects of the proposed truncation. The results also showed that from its full-length size of 55 M with 98.67% accuracy, the proposed truncation reduced its parameters to only 441 K and still attained an accuracy of 97.41%, outperforming other studies based on its size to performance ratio.

COVID-19 Identification from Low-Quality Computed Tomography Using a Modified Enhanced Super-Resolution Generative Adversarial Network Plus and Siamese Capsule Network

Computed Tomography has become a vital screening method for the detection of coronavirus 2019 (COVID-19). With the high mortality rate and overload for domain experts, radiologists, and clinicians, there is a need for the application of a computerized diagnostic technique. To this effect, we have taken into consideration improving the performance of COVID-19 identification by tackling the issue of low quality and resolution of computed tomography images by introducing our method. We have reported about a technique named the modified enhanced super resolution generative adversarial network for a better high resolution of computed tomography images. Furthermore, in contrast to the fashion of increasing network depth and complexity to beef up imaging performance, we incorporated a Siamese capsule network that extracts distinct features for COVID-19 identification.The qualitative and quantitative results establish that the proposed model is effective, accurate, and robust for COVID-19 screening. We demonstrate the proposed model for COVID-19 identification on a publicly available dataset COVID-CT, which contains 349 COVID-19 and 463 non-COVID-19 computed tomography images. The proposed method achieves an accuracy of 97.92%, sensitivity of 98.85%, specificity of 97.21%, AUC of 98.03%, precision of 98.44%, and F1 score of 97.52%. Our approach obtained state-of-the-art performance, according to experimental results, which is helpful for COVID-19 screening. This new conceptual framework is proposed to play an influential task in the issue facing COVID-19 and related ailments, with the availability of few datasets.

Evaluation of chest CT scan finding in the patients with acute respiratory symptoms following positive results of RT-PCR-COVID19

BACKGROUND

Reverse transcription-polymerase chain reaction (RT-PCR) is a standard technique for diagnosing coronavirus disease 2019 (COVID-19). The parameters for the diagnosis of COVID-19 included the history of exposure to positive COVID-19 patients, clinical signs and symptoms related to the disease, inflammation factors in the blood test or positive antigen-antibody test, and chest computed tomography (CT) findings. The current study evaluated the chest CT scan findings in patients with respiratory problems following positive RT-PCR of COVID 19.

MATERIALS AND METHODS

This cross-sectional study was performed on 120 patients referred to Ali Ibn-Abi Talib Hospital in Rafsanjan, Kerman Province, Iran, with respiratory symptoms between Dec-2019 to Dec-2020. Two radiologists reviewed the chest CT scans of these patients using the checklist that included parameters such as the types of involvement (consolidation/grand-glass/crazy paving, etc.) and the patterns of involvement (central/peripheral), and the pleural findings.

RESULTS

The CT scan was conducted in 107 patients with a typical condition and 11 patients with an atypical form of the disease. The frequency of the typical CT image of COVID-19 in the male group was significantly higher than that in the female group (P=0.004). The frequency of reverse halo sign, septal thickening, cardiomegaly, and crazy paving was significantly higher in males than in females (P≤0.05). Also, there was a significant difference between age groups based on the number of involved lobes (P=0.04).

CONCLUSION

Chest CT scan is an important diagnostic method for COVID 19 with high sensitivity. The parameters in the CT scan are beneficial for the diagnosis of COVID 19. In addition, some characters in CT scans in the male gender are more specific.

Medical imaging of pulmonary disease in SARS-CoV-2-exposed non-human primates

Chest X-ray (CXR), computed tomography (CT), and positron emission tomography-computed tomography (PET-CT) are noninvasive imaging techniques widely used in human and veterinary pulmonary research and medicine. These techniques have recently been applied in studies of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-exposed non-human primates (NHPs) to complement virological assessments with meaningful translational readouts of lung disease. Our review of the literature indicates that medical imaging of SARS-CoV-2-exposed NHPs enables high-resolution qualitative and quantitative characterization of disease otherwise clinically invisible and potentially provides user-independent and unbiased evaluation of medical countermeasures (MCMs). However, we also found high variability in image acquisition and analysis protocols among studies. These findings uncover an urgent need to improve standardization and ensure direct comparability across studies.

Inter-Observer Agreement between Low-Dose and Standard-Dose CT with Soft and Sharp Convolution Kernels in COVID-19 Pneumonia

Computed tomography (CT) has been an essential diagnostic tool during the COVID-19 pandemic. The study aimed to develop an optimal CT protocol in terms of safety and reliability. For this, we assessed the inter-observer agreement between CT and low-dose CT (LDCT) with soft and sharp kernels using a semi-quantitative severity scale in a prospective study (Moscow, Russia). Two consecutive scans with CT and LDCT were performed in a single visit. Reading was performed by ten radiologists with 3–25 years’ experience. The study included 230 patients, and statistical analysis showed LDCT with a sharp kernel as the most reliable protocol (percentage agreement 74.35 ± 43.77%), but its advantage was marginal. There was no significant correlation between radiologists’ experience and average percentage agreement for all four evaluated protocols. Regarding the radiation exposure, CTDI_vol was 3.6 ± 0.64 times lower for LDCT. In conclusion, CT and LDCT with soft and sharp reconstructions are equally reliable for COVID-19 reporting using the “CT 0-4” scale. The LDCT protocol allows for a significant decrease in radiation exposure but may be restricted by body mass index.

Explainable Machine Learning for COVID-19 Pneumonia Classification With Texture-Based Features Extraction in Chest Radiography

Both reverse transcription-PCR (RT-PCR) and chest X-rays are used for the diagnosis of the coronavirus disease-2019 (COVID-19). However, COVID-19 pneumonia does not have a defined set of radiological findings. Our work aims to investigate radiomic features and classification models to differentiate chest X-ray images of COVID-19-based pneumonia and other types of lung patterns. The goal is to provide grounds for understanding the distinctive COVID-19 radiographic texture features using supervised ensemble machine learning methods based on trees through the interpretable Shapley Additive Explanations (SHAP) approach. We use 2,611 COVID-19 chest X-ray images and 2,611 non-COVID-19 chest X-rays. After segmenting the lung in three zones and laterally, a histogram normalization is applied, and radiomic features are extracted. SHAP recursive feature elimination with cross-validation is used to select features. Hyperparameter optimization of XGBoost and Random Forest ensemble tree models is applied using random search. The best classification model was XGBoost, with an accuracy of 0.82 and a sensitivity of 0.82. The explainable model showed the importance of the middle left and superior right lung zones in classifying COVID-19 pneumonia from other lung patterns.

Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays

The year 2020 was characterized by the COVID-19 pandemic that has caused, by the end of March 2021, more than 2.5 million deaths worldwide. Since the beginning, besides the laboratory test, used as the gold standard, many applications have been applying deep learning algorithms to chest X-ray images to recognize COVID-19 infected patients. In this context, we found out that convolutional neural networks perform well on a single dataset but struggle to generalize to other data sources. To overcome this limitation, we propose a late fusion approach where we combine the outputs of several state-of-the-art CNNs, introducing a novel method that allows us to construct an optimum ensemble determining which and how many base learners should be aggregated. This choice is driven by a two-objective function that maximizes, on a validation set, the accuracy and the diversity of the ensemble itself. A wide set of experiments on several publicly available datasets, accounting for more than 92,000 images, shows that the proposed approach provides average recognition rates up to 93.54% when tested on external datasets.

Clinical and operational impact of rapid point-of-care SARS-CoV-2 detection in an emergency department

Study objective

Rapid point-of-care (POC) SARS-CoV-2 detection with Abbott ID NOW™ COVID-19 test has been implemented in our Emergency Department (ED) for several months. We aimed to evaluate the operational impact and potential benefits of this innovative clinical pathway.

Methods

We conducted a prospective, descriptive, interventional, non-randomized study, before-after trial with the comparison of patient cohorts from two consecutive periods of seven weeks (observational pre-POC period vs interventional POC period).

Results

In 2020, throughout weeks 37 to 50, 3333 patients were assessed for eligibility and among them 331 (9.9%) were positive for SARS-CoV-2 infections. Among the included patients, 136 (9.2%) were positive for SARS-CoV-2 infection in the pre-POC period and 195 (10.5%) in the POC period. Among positive patients for SARS-CoV-2 related infection in-hospital mortality rate was similar between the two groups but the hospitalization rate was higher in the POC group (81.6% vs. 65.4%; p < 0.001). More patients in the POC period were able to leave the ED within 6 h. We examined rates of antibiotic, anticoagulant, and corticosteroid prescriptions among patients tested for SARS-CoV-2 in the ED. Only the rate of prescribed anticoagulants was found to be higher in the POC period (40% vs. 24.2%; p < 0.003).

Conclusion

We demonstrated that COVID-19 point-of-care testing speeds up clinical decision-making, improving use of recommended treatments for COVID-19, such as anticoagulants. Moreover, it improves the boarding time and significantly shortened the length of stay in the ED for patients requiring outpatient care.

Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem

Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

The Role and Challenges of Clinical Imaging During COVID-19 Outbreak

Objective:

The Radiology department played a crucial role in detecting and following up with the COVID-19 disease during the pandemic. The purpose of this review was to highlight and discuss the role of each imaging modality, in the radiology department, that can help in the current pandemic and to determine the challenges faced by staff and how to overcome them.

Materials and Methods:

A literature search was performed using different databases, including PubMed, Google scholar, and the college electronic library to access 2020 published related articles.

Results:

A chest computed tomogram (CT) was found to be superior to a chest radiograph, with regards to the early detection of COVID-19. Utilizing lung point of care ultrasound (POCUS) with pediatric patients, demonstrated excellent sensitivity and specificity, compared to a chest radiography. In addition, lung ultrasound (LUS) showed a high correlation with the disease severity assessed with CT. However, magnetic resonance imaging (MRI) has some limiting factors with regard to its clinical utilization, due to signal loss. The reported challenges that the radiology department faced were mainly related to infection control, staff workload, and the training of students.

Conclusion:

The choice of an imaging modality to provide a COVID-19 diagnosis is debatable. It depends on several factors that should be carefully considered, such as disease stage, mobility of the patient, and ease of applying infection control procedures. The pros and cons of each imaging modality were highlighted, as part of this review. To control the spread of the infection, precautionary measures such as the use of portable radiographic equipment and the use of personal protective equipment (PPE) must be implemented.

Spontaneous Pneumomediastinum in Patients Diagnosed with COVID-19: A Case Series with Review of Literature

Background

Spontaneous pneumomediastinum (SPM) is a rare condition defined by the presence of air in the mediastinum in the absence of traumatic or iatrogenic injury. Although the imaging findings and complications of SARS-CoV-2 infection have been reported many times, there are few reports of the prevalence and outcomes of patients with SPM.

Purpose

In this paper, we aimed to illustrate the different manifestations, management, and outcome of three cases of SPM in COVID-19 patients and provide an extensive review available literature.

Materials and Methods

Detailed report of patients' demographics, clinical presentation, management, and outcome of three cases of COVID-19 induced SPM seen in our institution was provided. Additionally, literature search was employed through March 2021 using Pubmed and Google scholar databases where a total of 22 articles consisting of 35 patients were included.

Results

Statistical analysis of the reviewed articles showed that SPM in COVID-19 occurs in patients with a mean age of 55.6 ± 16.7 years. Furthermore, 80% of the 35 patients are males and almost 60% have comorbidities. Intriguingly, SPM in COVID-19 is associated with a 28.5% mortality rate. These findings are consistent with our case series and are different from previous reports of SPM in non-COVID-19 cases where it most commonly occurs in younger individuals and has a self-limiting course with a good outcome.

Conclusion

Therefore, SPM in COVID-19 patients occurs in older patients and is potentially associated with a higher mortality rate. Further studies are necessary to assess its role as a prognostic marker of poor outcome.

Diagnostic ultrasound imaging of the lung: A simulation approach based on propagation and reverberation in the human body

Although ultrasound cannot penetrate a tissue/air interface, it images the lung with high diagnostic accuracy. Lung ultrasound imaging relies on the interpretation of "artifacts," which arise from the complex reverberation physics occurring at the lung surface but appear deep inside the lung. This physics is more complex and less understood than conventional B-mode imaging in which the signal directly reflected by the target is used to generate an image. Here, to establish a more direct relationship between the underlying acoustics and lung imaging, simulations are used. The simulations model ultrasound propagation and reverberation in the human abdomen and at the tissue/air interfaces of the lung in a way that allows for direct measurements of acoustic pressure inside the human body and various anatomical structures, something that is not feasible clinically or experimentally. It is shown that the B-mode images beamformed from these acoustical simulations reproduce primary clinical features that are used in diagnostic lung imaging, i.e., A-lines and B-lines, with a clear relationship to known underlying anatomical structures. Both the oblique and parasagittal views are successfully modeled with the latter producing the characteristic "bat sign," arising from the ribs and intercostal part of the pleura. These simulations also establish a quantitative link between the percentage of fluid in exudative regions and the appearance of B-lines, suggesting that the B-mode may be used as a quantitative imaging modality.

COVID-19 pneumonia: A review of typical radiological characteristics

Coronavirus disease 2019 (COVID-19) was first discovered after unusual cases of severe pneumonia emerged by the end of 2019 in Wuhan (China) and was declared a global public health emergency by the World Health Organization in January 2020. The new pathogen responsible for the infection, genetically similar to the beta-coronavirus family, is known as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and the current gold standard diagnostic tool for its detection in respiratory samples is the reverse transcription-polymerase chain reaction test. Imaging findings on COVID-19 have been widely described in studies published throughout last year, 2020. In general, ground-glass opacities and consolidations, with a bilateral and peripheral distribution, are the most typical patterns found in COVID-19 pneumonia. Even though much of the literature focuses on chest computed tomography (CT) and X-ray imaging and their findings, other imaging modalities have also been useful in the assessment of COVID-19 patients. Lung ultrasonography is an emerging technique with a high sensitivity, and thus useful in the initial evaluation of SARS-CoV-2 infection. In addition, combined positron emission tomography-CT enables the identification of affected areas and follow-up treatment responses. This review intends to clarify the role of the imaging modalities available and identify the most common radiological manifestations of COVID-19.

Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem

Computer-aided-diagnosis and stratification of COVID-19 based on chest X-ray suffers from weak bias assessment and limited quality-control. Undetected bias induced by inappropriate use of datasets, and improper consideration of confounders prevents the translation of prediction models into clinical practice. By adopting established tools for model evaluation to the task of evaluating datasets, this study provides a systematic appraisal of publicly available COVID-19 chest X-ray datasets, determining their potential use and evaluating potential sources of bias. Only 9 out of more than a hundred identified datasets met at least the criteria for proper assessment of risk of bias and could be analysed in detail. Remarkably most of the datasets utilised in 201 papers published in peer-reviewed journals, are not among these 9 datasets, thus leading to models with high risk of bias. This raises concerns about the suitability of such models for clinical use. This systematic review highlights the limited description of datasets employed for modelling and aids researchers to select the most suitable datasets for their task.

Radiological profile of patients undergoing Chest X-ray and computed tomography scans during COVID-19 outbreak

BACKGROUND & OBJECTIVE

Radiology has played a significant role in the diagnosis and quantifying the severity of COVID 19 pulmonary disease. This study was conducted to assess patterns and severity of COVID-19 pulmonary disease based on radiological imaging.

METHODS

A prospective observational study was conducted in a large tertiary care public sector teaching hospital of Karachi, Pakistan from June 2020 till August 2020. All confirmed and suspected COVID-19 patients referred for chest X-rays and computed tomography (CT) scans were evaluated along with RT-PCR results. Suspected patients were followed for RT-PCR. Radiological features and severity of imaging studies were determined.

RESULTS

Of 533 patients in whom X-rays were performed, majority had severe/critical findings, i.e., 304 (57.03%). Of 97 patients in whom CT scan was performed, mild/moderate findings were observed in 63 (64.94%) patients. Of 472 patients with abnormal X-rays, majority presented with alveolar pattern 459 (97.2%), bilateral lung involvement 453 (89.6%), and consolidation 356 (75.4%). Moreover, lobar predominance showed lower zone preponderance in 446 (94.5%) patients. Of 88 patients with abnormal CT findings, ground-glass opacity (GGO) 87 (98.9%) and crazy paving 69 (78.4%) were the most common findings. An insignificantly higher association of PCR positive cases was observed with severe/critical X-rays (p-value 0.076) and CT scan findings (p-value 0.431).

CONCLUSION

Most common patterns on CT scans were GGO and crazy paving. While on chest radiographs, bilateral lung involvement with alveolar pattern and consolidation were most common findings. On X-rays, majority had severe/critical whereas CT scan had mild/moderate findings.

Imaging in the COVID-19 era: Lessons learned during a pandemic

The first year of the coronavirus disease 2019 (COVID-19) pandemic has been a year of unprecedented changes, scientific breakthroughs, and controversies. The radiology community has not been spared from the challenges imposed on global healthcare systems. Radiology has played a crucial part in tackling this pandemic, either by demonstrating the manifestations of the virus and guiding patient management, or by safely handling the patients and mitigating transmission within the hospital. Major modifications involving all aspects of daily radiology practice have occurred as a result of the pandemic, including workflow alterations, volume reductions, and strict infection control strategies. Despite the ongoing challenges, considerable knowledge has been gained that will guide future innovations. The aim of this review is to provide the latest evidence on the role of imaging in the diagnosis of the multifaceted manifestations of COVID-19, and to discuss the implications of the pandemic on radiology departments globally, including infection control strategies and delays in cancer screening. Lastly, the promising contribution of artificial intelligence in the COVID-19 pandemic is explored.

Highlighting COVID-19: What the imaging exams show about the disease

Coronavirus disease 2019 (COVID-19), a global emergency, is caused by severe acute respiratory syndrome coronavirus 2. The gold standard for its diagnosis is the reverse transcription polymerase chain reaction, but considering the high number of infected people, the low availability of this diagnostic tool in some contexts, and the limitations of the test, other tools that aid in the identification of the disease are necessary. In this scenario, imaging exams such as chest X-ray (CXR) and computed tomography (CT) have played important roles. CXR is useful for assessing disease progression because it allows the detection of extensive consolidations, besides being a fast and cheap method. On the other hand, CT is more sensitive for detecting lung changes in the early stages of the disease and is also useful for assessing disease progression. Of note, ground-glass opacities are the main COVID-19-related CT findings. Positron emission tomography combined with CT can be used to evaluate chronic and substantial damage to the lungs and other organs; however, it is an expensive test. Lung ultrasound (LUS) has been shown to be a promising technique in that context as well, being useful in the screening and monitoring of patients, disease classification, and management related to mechanical ventilation. Moreover, LUS is an inexpensive alternative available at the bedside. Finally, magnetic resonance imaging, although not usually requested, allows the detection of pulmonary, cardiovascular, and neurological abnormalities associated with COVID-19. Furthermore, it is important to consider the challenges faced in the radiology field in the adoption of control measures to prevent infection and in the follow-up of post-COVID-19 patients.

A 3D hologram with mixed reality techniques for better understanding the pulmonary lesions of COVID-19: Randomized Controlled Trial

Background

The COVID-19 outbreak has now become a pandemic and has had a serious adverse impact on global public health. The effect of COVID-19 on the lungs can be determined through 2D computed tomography (CT) imaging, which requires a high level of spatial imagination on the part of the medical provider.

Objective

The purpose of this study is to determine whether viewing a 3D hologram with mixed reality techniques can improve medical professionals’ understanding of the pulmonary lesions caused by COVID-19.

Methods

The study involved 60 participants, including 20 radiologists, 20 surgeons, and 20 medical students. Each of the three groups was randomly divided into two groups, either the 2D CT group (n=30; mean age 29 years [range 19-38 years]; males=20) or the 3D holographic group (n=30; mean age 30 years [range 20=38 years]; males=20). The two groups completed the same task, which involved identifying lung lesions caused by COVID-19 for 6 cases using a 2D CT or 3D hologram. Finally, an independent radiology professor rated the participants' performance (out of 100). All participants in two groups completed a Likert scale questionnaire regarding the educational utility and efficiency of 3D holograms. The National Aeronautics and Space Administration Task Load Index (NASA-TLX) was completed by all participants.

Results

The mean task score of the 3D hologram group (mean 91.98, SD 2.45) was significantly higher than that of the 2D CT group (mean 74.09, SD 7.59; P<.001). With the help of 3D holograms, surgeons and medical students achieved the same score as radiologists and made obvious progress in identifying pulmonary lesions caused by COVID-19. The Likert scale questionnaire results showed that the 3D hologram group had superior results compared to the 2D CT group (teaching: 2D CT group median 2, IQR 1-2 versus 3D group median 5, IQR 5-5; P<.001; understanding and communicating: 2D CT group median 1, IQR 1-1 versus 3D group median 5, IQR 5-5; P<.001; increasing interest: 2D CT group median 2, IQR 2-2 versus 3D group median 5, IQR 5-5; P<.001; lowering the learning curve: 2D CT group median 2, IQR 1-2 versus 3D group median 4, IQR 4-5; P<.001; spatial awareness: 2D CT group median 2, IQR 1-2 versus 3D group median 5, IQR 5-5; P<.001; learning: 2D CT group median 3, IQR 2-3 versus 3D group median 5, IQR 5-5; P<.001). The 3D group scored significantly lower than the 2D CT group for the “mental,” “temporal,” “performance,” and “frustration” subscales on the NASA-TLX.

Conclusions

A 3D hologram with mixed reality techniques can be used to help medical professionals, especially medical students and newly hired doctors, better identify pulmonary lesions caused by COVID-19. It can be used in medical education to improve spatial awareness, increase interest, improve understandability, and lower the learning curve.

Trial Registration

Chinese Clinical Trial Registry ChiCTR2100045845; http://www.chictr.org.cn/showprojen.aspx?proj=125761

Diagnostic, Prognostic, and Therapeutic Use of Radiopharmaceuticals in the Context of SARS-CoV-2

The coronavirus disease 2019 (COVID-19) outbreak has devastated the healthcare systems and economies of over 200 countries in just a few months. The etiological agent of COVID-19, SARS-CoV-2, is a highly contagious virus that can be transmitted by asymptomatic and symptomatic carriers alike. While in vitro testing techniques have allowed for population-wide screening, prognostic tools are required to assess the disease severity and therapeutic response, contributing to improve the patient clinical outcomes. Moreover, no specific antiviral against COVID-19 exists at the time of publication, severely limiting treatment against the infection. Hence, there is an urgent clinical need for innovative therapeutic strategies that may contribute to manage the COVID-19 outbreak and prevent future pandemics. Herein, we critically examine recent diagnostic, prognostic, and therapeutic advancements for COVID-19 in the field of radiopharmaceuticals. First, we summarize the gold standard techniques used to diagnose COVID-19, including in vitro assays and imaging techniques, and then discuss how radionuclide-based nuclear imaging provides complementary information for prognosis and treatment management of infected patients. Second, we introduce new emerging types of radiotherapies that employ radioimmunoconjugates, which have shown selective cytotoxic response in oncological studies, and critically analyze how these compounds could be used as therapeutic agents against SARS-CoV-2. Finally, this Perspective further discusses the emerging applications of radionuclides to study the behavior of pulmonary SARS-CoV-2 aerosol particles.

Current limitations to identify COVID-19 using artificial intelligence with chest X-ray imaging

The scientific community has joined forces to mitigate the scope of the current COVID-19 pandemic. The early identification of the disease, as well as the evaluation of its evolution is a primary task for the timely application of medical protocols. The use of medical images of the chest provides valuable information to specialists. Specifically, chest X-ray images have been the focus of many investigations that apply artificial intelligence techniques for the automatic classification of this disease. The results achieved to date on the subject are promising. However, some results of these investigations contain errors that must be corrected to obtain appropriate models for clinical use. This research discusses some of the problems found in the current scientific literature on the application of artificial intelligence techniques in the automatic classification of COVID-19. It is evident that in most of the reviewed works an incorrect evaluation protocol is applied, which leads to overestimating the results.

Independent role of CT chest scan in COVID-19 prognosis: Evidence from the machine learning classification

Background: The current coronavirus disease-19 (COVID-19) pandemic call attention to the key role informatics play in healthcare. The present study discovers an independent role of computerised tomography chest (CT) scans in prognosis of COVID-19 using classification learning algorithms. Methods: In this retrospective study, 57 RT PCR positive COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India) after approval from the Institutional Ethics Committee. A set of 21 features including clinical findings and laboratory parameters and chest CT severity score were recorded. The CT score with mild, moderate and severe categories was chosen as response variable. The dimensionality reduction of feature space was performed and classifiers including, decision trees, K-nearest neighbours, support vector machine and ensemble learning were trained with principal components. The model with highest accuracy and area under the ROC curve (AUC) was selected. Results: The median age of patients was 55 years (range: 20-99 years) with 37 males. The feature space was reduced from 21 to 7 predictors, that included fever, cough, fibrin degradation products, haemoglobin, neutrophil-lymphocyte ratio, ferritin and procalcitonin. The linear support vector machine was chosen as the best classifier with 73.7 % and 0.69 accuracy and AUC for severe CT chest score, respectively. The variance contributed by first three principal components were 97.5 %, 2.4 % and 0.0 %, respectively. Conclusion: In view of low degree of relationships between predictors and chest CT scan severity score category as interpreted from accuracy and AUC it can be concluded that chest CT scan has an independent role in the prognosis of COVID-19 patients.