RSNA International Trends: A Global Perspective on the COVID-19 Pandemic and Radiology in Late 2020

Driving healthcare forward: The potential of mobile MRI and CT units in streamlining radiological services in Saudi Arabia - A narrative review

BACKGROUND AND PURPOSE

This narrative review focuses on the role of mobile MRI and CT units in addressing the challenges of healthcare accessibility and patient wait times in Saudi Arabia. It underscores the growing demand for diagnostic imaging amid infrastructural and geographical barriers, emphasizing mobile units as innovative solutions for enhancing radiological services across diverse Saudi landscapes. The purpose of this study is to assess how these mobile technologies can mitigate service delays, improve patient outcomes, and support healthcare delivery in remote or underserved areas, reflecting on global trends towards more dynamic, patient-centered healthcare models.

METHODS

This review utilizes an expanded database search and refined keywords to ensure comprehensive literature coverage. The study focused on peer-review articles and grey literatures that directly examined the impact of these mobile units on healthcare accessibility, wait times, and service delivery. A thematic analysis identified significant contributions to accessibility improvements, emergency responses, and rural healthcare, highlighting areas for further research and policy development.

DISCUSSION

Mobile units have advanced technical specifications with high-field magnets and multi-slice CT scanners on par with fixed facilities. They prioritize patient comfort and safety with examination areas, control rooms, and waiting areas. Telemedicine capabilities allow real-time image transmission to specialists. Strategic deployment can address workforce shortages by distributing services equitably. Mobile units represent cost-effective solutions to expand healthcare access without fixed infrastructure.

CONCLUSION

Integration of mobile MRI and CT units in Saudi Arabia can transform access to diagnostic imaging by decentralizing services and directly reaching patients, including rural areas. Evidence shows mobile units reduce diagnostic delays and optimize resource use. Despite challenges, strategic investments and collaborations can overcome obstacles to make radiological services more equitable, flexible and patient-focused in Saudi Arabia.

Quantitative CT Texture Analysis of COVID-19 Hospitalized Patients during 3-24-Month Follow-Up and Correlation with Functional Parameters

BACKGROUND

To quantitatively evaluate CT lung abnormalities in COVID-19 survivors from the acute phase to 24-month follow-up. Quantitative CT features as predictors of abnormalities' persistence were investigated.

METHODS

Patients who survived COVID-19 were retrospectively enrolled and underwent a chest CT at baseline (T0) and 3 months (T3) after discharge, with pulmonary function tests (PFTs). Patients with residual CT abnormalities repeated the CT at 12 (T12) and 24 (T24) months after discharge. A machine-learning-based software, CALIPER, calculated the CT percentage of the whole lung of normal parenchyma, ground glass (GG), reticulation (Ret), and vascular-related structures (VRSs). Differences (Δ) were calculated between time points. Receiver operating characteristic (ROC) curve analyses were performed to test the baseline parameters as predictors of functional impairment at T3 and of the persistence of CT abnormalities at T12.

RESULTS

The cohort included 128 patients at T0, 133 at T3, 61 at T12, and 34 at T24. The GG medians were 8.44%, 0.14%, 0.13% and 0.12% at T0, T3, T12 and T24. The Ret medians were 2.79% at T0 and 0.14% at the following time points. All Δ significantly differed from 0, except between T12 and T24. The GG and VRSs at T0 achieved AUCs of 0.73 as predictors of functional impairment, and area under the curves (AUCs) of 0.71 and 0.72 for the persistence of CT abnormalities at T12.

CONCLUSIONS

CALIPER accurately quantified the CT changes up to the 24-month follow-up. Resolution mostly occurred at T3, and Ret persisting at T12 was almost unchanged at T24. The baseline parameters were good predictors of functional impairment at T3 and of abnormalities' persistence at T12.

Qualitative and semi-quantitative ultrasound assessment in delta and Omicron Covid-19 patients: data from high volume reference center

OBJECTIVE

to evaluate the efficacy of US, both qualitatively and semi-quantitatively, in the selection of treatment for the Covid-19 patient, using patient triage as the gold standard.

METHODS

Patients admitted to the Covid-19 clinic to be treated with monoclonal antibodies (mAb) or retroviral treatment and undergoing lung ultrasound (US) were selected from the radiological data set between December 2021 and May 2022 according to the following inclusion criteria: patients with proven Omicron variant and Delta Covid-19 infection; patients with known Covid-19 vaccination with at least two doses. Lung US (LUS) was performed by experienced radiologists. The presence, location, and distribution of abnormalities, such as B-lines, thickening or ruptures of the pleural line, consolidations, and air bronchograms, were evaluated. The anomalous findings in each scan were classified according to the LUS scoring system. Nonparametric statistical tests were performed.

RESULTS

The LUS score median value in the patients with Omicron variant was 1.5 (1-20) while the LUS score median value in the patients with Delta variant was 7 (3-24). A difference statistically significant was observed for LUS score values among the patients with Delta variant between the two US examinations (p value = 0.045 at Kruskal Wallis test). There was a difference in median LUS score values between hospitalized and non-hospitalized patients for both the Omicron and Delta groups (p value = 0.02 on the Kruskal Wallis test). For Delta patients groups the sensitivity, specificity, positive and negative predictive values, considering a value of 14 for LUS score for the hospitalization, were of 85.29%, 44.44%, 85.29% and 76.74% respectively.

CONCLUSIONS

LUS is an interesting diagnostic tool in the context of Covid-19, it could allow to identify the typical pattern of diffuse interstitial pulmonary syndrome and could guide the correct management of patients.

Scenario Planning Approach to Adapting in the COVID Era

RATIONALE AND OBJECTIVES

The COVID-19 pandemic has caused much uncertainty and disruption in healthcare resulting in many challenges for strategic planning. Scenario planning is a tool that allows healthcare leaders to plan healthcare delivery strategies by incorporating the uncertainties into the analysis and planning process.

MATERIALS AND METHODS

Variables were identified which will have major impact on the future, but whose future direction is uncertain. The extremes of these drivers were used to generate multiple scenarios. A subset of scenarios was used to evaluate potential tactics to determine which may be high yield in the face of uncertainty.

RESULTS

Unlike traditional strategic planning, scenario planning does not develop a single future with a path to that future. Scenario planning evaluates tactics to determine which would be helpful in specific scenarios, multiple different futures or under specific conditions.

CONCLUSION

We present a scenario planning model which can be used to determine specific tactics to accommodate the uncertainty due to variable healthcare delivery needs in the COVID-19 era.

Imaging volumes during COVID-19: A Victorian health service experience

BACKGROUND

The World Health Organisation declared the coronavirus disease 2019 (COVID-19) a pandemic on March 11, 2020. While globally, the relative caseload has been high, Australia’s has been relatively low. During the pandemic, radiology services have seen significant changes in workflow across modalities and a reduction in imaging volumes.

AIM

To investigate differences in modality imaging volumes during the COVID-19 pandemic across a large Victorian public health network.

METHODS

A retrospective analysis from January 2019 to December 2020 compared imaging volumes across two periods corresponding to the pandemic’s first and second waves. Weekly volumes across patient class, modality and mobile imaging were summed for periods: wave 1 (weeks 11 to 16 for 2019; weeks 63 to 68 for 2020) and wave 2 (weeks 28 to 43 for 2019; weeks 80 to 95 for 2020). Microsoft Power Business Intelligence linked to the radiology information system was used to mine all completed examinations.

RESULTS

Summed weekly data during the pandemic’s first wave showed the greatest decrease of 29.8% in adult outpatient imaging volumes and 46.3% in paediatric emergency department imaging volumes. Adult nuclear medicine demonstrated the greatest decrease of 37.1% for the same period. Paediatric nuclear medicine showed the greatest decrease of 47.8%, with angiography increasing by 50%. The pandemic’s second wave demonstrated the greatest decrease of 23.5% in adult outpatient imaging volumes, with an increase of 18.2% in inpatient imaging volumes. The greatest decrease was 28.5% in paediatric emergency department imaging volumes. Nuclear medicine showed the greatest decrease of 37.1% for the same period. Paediatric nuclear medicine showed the greatest decrease of 36.7%. Mobile imaging utilisation increased between 57.8% and 135.1% during the first and second waves. A strong correlation was observed between mobile and non-mobile imaging in the emergency setting (Spearman’s correlation coefficient = -0.743, P = 0.000). No correlation was observed in the inpatient setting (Spearman’s correlation coefficient = -0.059, P = 0.554).

CONCLUSION

Nuclear medicine was most impacted, while computed tomography and angiography were the least affected by the pandemic. The impact was less during the pandemic’s second wave. Mobile imaging shows continuous growth during both waves.

An Africa point of view on quality and safety in imaging

Africa has seen an upsurge in diagnostic imaging utilization, with benefits of efficient and accurate diagnosis, but these could easily be offset by undesirable effects attributed to unjustified, unoptimized imaging and poor quality examinations. This paper aims to present Africa’s position regarding quality and safety in imaging, give reasons for the rising interest in quality and safety, define quality and safety from an African context, list drivers for quality and safety in Africa, discuss the impact of COVID-19 on quality and safety, and review Africa’s progress using the Bonn Call for Action framework while proposing a way forward for imaging quality and safety in Africa. In spite of a healthcare setting characterized by meagre financial, human and technology resources, a rapidly widening disease-burden spectrum, growing proportion of non-communicable diseases and resurgence of tropical and global infections, Africa has over the last ten years made significant strides in quality and safety for imaging. These include raising radiation-safety awareness, interest and application of evidence-based radiation safety recommendations and guidance tools, establishing facility and national diagnostic reference levels (DRLs) and strengthening end-user education and training. Major challenges are: limited human resource, low prioritization of imaging in relation to other health services, low level of integration of imaging into the entire health service delivery, insufficient awareness for radiation safety awareness, a radiation safety culture which is emerging, insufficient facilities and opportunities for education and training. Solutions to these challenges should target the entire hierarchy of health service delivery from prioritization, policy, planning, processes to procedures.

Application of artificial intelligence in diagnosing COVID-19 disease symptoms on chest X-rays: A systematic review

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results: The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

Abdominal and gastrointestinal manifestations in COVID-19 patients: Is imaging useful?

Coronavirus disease 2019 (COVID-19) can be considered a systemic disease with a specific tropism for the vascular system, in which the alterations of the microcirculation have an important pathogenetic role. The lungs are the main organ involved in COVID-19, and severe progressive respiratory failure is the leading cause of death in the affected patients; however, many other organs can be involved with variable clinical manifestations. Concerning abdominal manifestations, the gastrointestinal tract and the hepatobiliary system are mainly affected, although the pancreas, urinary tract and spleen may also be involved. The most common gastrointestinal symptoms are loss of appetite, followed by nausea and vomiting, diarrhea and abdominal pain. Gastrointestinal imaging findings include bowel wall thickening, sometimes associated with hyperemia and mesenteric thickening, fluid-filled segments of the large bowel and rarely intestinal pneumatosis and ischemia. Hepatic involvement manifests as an increase in the enzymatic levels of alanine aminotransferase, aspartate aminotransferase, serum bilirubin and γ-glutamyl transferase with clinical manifestations in most cases mild and transient. The most frequent radiological features are hepatic steatosis, biliary sludge and gallstones. Edematous acute pancreatitis, kidney infarct and acute kidney injury from acute tubular necrosis have been described more rarely in COVID-19. Lastly, splenic involvement is characterized by splenomegaly and by the development of solitary or multifocal splenic infarcts with classic wedge-shaped or even rounded morphology, with irregular or smooth profiles. In summary, the abdominal radiological findings of COVID-19 are nonspecific and with poor pathological correlation reported in the literature. Ultrasound and particularly computed tomography with multiphasic acquisition are the diagnostic methods mainly utilized in COVID-19 patients with abdominal clinical symptoms and signs. Although radiological signs are not specific of abdominal and gastrointestinal involvement, the diagnostic imaging modalities and in particular computed tomography are helpful for the clinician in the management, evaluation of the severity and evolution of the COVID-19 patients.

COVID-19 and Breast Radiologist Wellness: Impact of Gender, Financial Loss, and Childcare Need

PURPOSE

The purpose of this study was to evaluate the emotional and financial impact of coronavirus disease 2019 (COVID-19) on breast radiologists to understand potential consequences on physician wellness and gender disparities in radiology.

METHODS

A 41-question survey was distributed from June to September 2020 to members of the Society of Breast Imaging and the National Consortium of Breast Centers. Psychological distress and financial loss scores were calculated on the basis of survey responses and compared across gender and age subgroups. A multivariate logistic model was used to identify factors associated with psychological distress scores.

RESULTS

A total of 628 surveys were completed (18% response rate); the mean respondent age was 52 ± 10 years, and 79% were women. Anxiety was reported by 68% of respondents, followed by sadness (41%), sleep problems (36%), anger (25%), and depression (23%). A higher psychological distress score correlated with female gender (odds ratio [OR], 1.9; P = .001), younger age (OR, 0.8 per SD; P = .005), and a higher financial loss score (OR, 1.4; P < .0001). Participants whose practices had not initiated wellness efforts specific to COVID-19 (54%) had higher psychological distress scores (OR, 1.4; P = .03). Of those with children at home, 38% reported increased childcare needs, higher in women than men (40% versus 29%, P < .001). Thirty-seven percent reported that childcare needs had adversely affected their jobs, which correlated with higher psychological distress scores (OR, 2.2-3.3; P < .05).

CONCLUSIONS

Psychological distress was highest among younger and female respondents and those with greater pandemic-specific childcare needs and financial loss. Practice-initiated COVID-19-specific wellness efforts were associated with decreased psychological distress. Policies are needed to mitigate pandemic-specific burnout and worsening gender disparities.

COVID-19 imaging: Diagnostic approaches, challenges, and evolving advances

The role of radiology and the radiologist have evolved throughout the coronavirus disease-2019 (COVID-19) pandemic. Early on, chest computed tomography was used for screening and diagnosis of COVID-19; however, it is now indicated for high-risk patients, those with severe disease, or in areas where polymerase chain reaction testing is sparsely available. Chest radiography is now utilized mainly for monitoring disease progression in hospitalized patients showing signs of worsening clinical status. Additionally, many challenges at the operational level have been overcome within the field of radiology throughout the COVID-19 pandemic. The use of teleradiology and virtual care clinics greatly enhanced our ability to socially distance and both are likely to remain important mediums for diagnostic imaging delivery and patient care. Opportunities to better utilize of imaging for detection of extrapulmonary manifestations and complications of COVID-19 disease will continue to arise as a more detailed understanding of the pathophysiology of the virus continues to be uncovered and identification of predisposing risk factors for complication development continue to be better understood. Furthermore, unidentified advancements in areas such as standardized imaging reporting, point-of-care ultrasound, and artificial intelligence offer exciting discovery pathways that will inevitably lead to improved care for patients with COVID-19.

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.

Shedding Light on the Direct and Indirect Impact of the COVID-19 Pandemic on the Lebanese Radiographers or Radiologic Technologists: A Crisis within Crises

With the novel coronavirus disease 2019 (COVID-19) pandemic, the need for radiologic procedures is increasing for the effective diagnosis and follow-up of pulmonary diseases. There is an immense load on the radiographers’ shoulders to cope with all the challenges associated with the pandemic. However, amidst this crisis, Lebanese radiographers are also suffering from a socioeconomic crisis and record hyperinflation that have posed additional challenges. A cross-sectional study was conducted among registered Lebanese radiographers to assess the general, workplace conditions, health and safety, mental/psychologic, financial, and skill/knowledge development impacts. Despite applying an adapted safety protocol, institutions are neither providing free RT-PCR testing to their staff nor showing adequate support for infected staff members, thus causing distress about contracting the virus from the workplace. Aggravated by the deteriorating economic situation that affected the radiographers financially, they additionally suffer from severe occupational physical and mental burnout. Regardless of that, they used their free time during the lockdown for skill/knowledge development and have performed many recreational activities. This cross-sectional study highlighted the different ways the pandemic has impacted the radiographers: physically, psychologically, and financially. It aimed to shed light on what these frontline heroes are passing through in the midst of all these unprecedented crises.

Willingness to Receive SARS-CoV-2 Vaccination and Associated Factors among Chinese Adults: A Cross Sectional Survey

Vaccination is a key strategy to prevent the pandemic caused by the coronavirus disease 2019 (COVID-19). This study aims to investigate the willingness of Chinese adults to be vaccinated against COVID-19 and further explore the factors that may affect their willingness. We used a self-design anonymous questionnaire to conduct an online survey via the Sojump. A total of 1009 valid questionnaires were analyzed. The age of the participants ranged from 18 to 74. Among them, 609 (60.4%, 95%CI: 57.4-63.4%) were willing to receive the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine. Logistic regression analysis results showed that the age of 30-49 (OR = 2.042, 95%CI: 1.098-3.799), universities and colleges education (OR = 1.873, 95% CI = 1.016-3.451), master degree or above education (OR = 1.885, 95%CI = 1.367-2.599), previous influenza vaccination history (OR = 2.176, 95%CI: 1.474-3.211), trust in the effectiveness of the vaccine (OR = 6.419, 95%CI: 3.717-11.086), and close attention to the latest news of the vaccine (OR = 1.601, 95%CI: 1.046-2.449) were facilitative factors that affected their willingness to be vaccinated. More than half of the adults in China would be willing to receive a SARS-CoV-2 vaccine. Middle-aged people with higher education, those who had been vaccinated against influenza, and those who believed that COVID-19 vaccine was effective and paid close attention to it were more willing to be vaccinated. Our findings can provide reference for the implementation of vaccination and the prevention of COVID-19 in China. More studies are needed after the vaccine is launched.

Lung Lesion Burden found on Chest CT as a Prognostic Marker in Hospitalized Patients with High Clinical Suspicion of COVID-19 Pneumonia: a Brazilian experience

OBJECTIVE

To investigate the relationship between lung lesion burden (LLB) found on chest computed tomography (CT) and 30-day mortality in hospitalized patients with high clinical suspicion of coronavirus disease 2019 (COVID-19), accounting for tomographic dynamic changes.

METHODS

Patients hospitalized with high clinical suspicion of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in a dedicated and reference hospital for COVID-19, having undergone at least one RT-PCR test, regardless of the result, and with one CT compatible with COVID-19, were retrospectively studied. Clinical and laboratory data upon admission were assessed, and LLB found on CT was semi-quantitatively evaluated through visual analysis. The primary outcome was 30-day mortality after admission. Secondary outcomes, including the intensive care unit (ICU) admission, mechanical ventilation used, and length of stay (LOS), were assessed.

RESULTS

A total of 457 patients with a mean age of 57±15 years were included. Among these, 58% presented with positive RT-PCR result for COVID-19. The median time from symptom onset to RT-PCR was 8 days [interquartile range 6-11 days]. An initial LLB of ≥50% using CT was found in 201 patients (44%), which was associated with an increased crude at 30-day mortality (31% vs. 15% in patients with LLB of <50%, p<0.001). An LLB of ≥50% was also associated with an increase in the ICU admission, the need for mechanical ventilation, and a prolonged LOS after adjusting for baseline covariates and accounting for the CT findings as a time-varying covariate; hence, patients with an LLB of ≥50% remained at a higher risk at 30-day mortality (adjusted hazard ratio 2.17, 95% confidence interval 1.47-3.18, p<0.001).

CONCLUSION

Even after accounting for dynamic CT changes in patients with both clinical and imaging findings consistent with COVID-19, an LLB of ≥50% might be associated with a higher risk of mortality.

Lessons learned in transitioning to AI in the medical imaging of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic has wreaked havoc across the world. It also created a need for the urgent development of efficacious predictive diagnostics, specifically, artificial intelligence (AI) methods applied to medical imaging. This has led to the convergence of experts from multiple disciplines to solve this global pandemic including clinicians, medical physicists, imaging scientists, computer scientists, and informatics experts to bring to bear the best of these fields for solving the challenges of the COVID-19 pandemic. However, such a convergence over a very brief period of time has had unintended consequences and created its own challenges. As part of Medical Imaging Data and Resource Center initiative, we discuss the lessons learned from career transitions across the three involved disciplines (radiology, medical imaging physics, and computer science) and draw recommendations based on these experiences by analyzing the challenges associated with each of the three associated transition types: (1) AI of non-imaging data to AI of medical imaging data, (2) medical imaging clinician to AI of medical imaging, and (3) AI of medical imaging to AI of COVID-19 imaging. The lessons learned from these career transitions and the diffusion of knowledge among them could be accomplished more effectively by recognizing their associated intricacies. These lessons learned in the transitioning to AI in the medical imaging of COVID-19 can inform and enhance future AI applications, making the whole of the transitions more than the sum of each discipline, for confronting an emergency like the COVID-19 pandemic or solving emerging problems in biomedicine.