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

The Regulation of Artificial Intelligence in Digital Radiology in the Scientific Literature: A Narrative Review of Reviews

Today, there is growing interest in artificial intelligence (AI) in the field of digital radiology (DR). This is also due to the push that has been applied in this sector due to the pandemic. Many studies are devoted to the challenges of integration in the health domain. One of the most important challenges is that of regulations. This study conducted a narrative review of reviews on the international approach to the regulation of AI in DR. The design of the study was based on: (I) An overview on Scopus and Pubmed (II) A qualification and eligibility process based on a standardized checklist and a scoring system. The results have highlighted an international approach to the regulation of these systems classified as "software as medical devices (SaMD)" arranged into: ethical issues, international regulatory framework, and bottlenecks of the legal issues. Several recommendations emerge from the analysis. They are all based on fundamental pillars: (a) The need to overcome a differentiated approach between countries. (b) The need for greater transparency and publicity of information both for SaMDs as a whole and for the algorithms and test patterns. (c) The need for an interdisciplinary approach that avoids bias (including demographic) in algorithms and test data. (d) The need to reduce some limits/gaps of the scientific literature production that do not cover the international approach.

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality

Rationale and Objectives

The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on computed tomography. The purpose of this study was to evaluate the ability of a deep convolutional neural network (dCNN) to predict inpatient outcomes associated with COVID-19 pneumonia.

Materials and Methods

A previously trained dCNN was tested on an external validation cohort of 241 patients who presented to the emergency department and received a chest computed tomography scan, 93 with COVID-19 and 168 without. Airspace opacity scoring systems were defined by the extent of airspace opacity in each lobe, totaled across the entire lungs. Expert and dCNN scores were concurrently evaluated for interobserver agreement, while both dCNN identified airspace opacity scoring and raw opacity values were used in the prediction of COVID-19 diagnosis and inpatient outcomes.

Results

Interobserver agreement for airspace opacity scoring was 0.892 (95% CI 0.834-0.930). Probability of each outcome behaved as a logistic function of the opacity scoring (25% intensive care unit admission at score of 13/25, 25% intubation at 17/25, and 25% mortality at 20/25). Length of hospitalization, intensive care unit stay, and intubation were associated with larger airspace opacity score (p = 0.032, 0.039, 0.036, respectively).

Conclusion

The tested dCNN was highly predictive of inpatient outcomes, performs at a near expert level, and provides added value for clinicians in terms of prognostication and disease severity.

The Artificial Intelligence in Digital Radiology: Part 1: The Challenges, Acceptance and Consensus

Artificial intelligence is having important developments in the world of digital radiology also thanks to the boost given to the research sector by the COVID-19 pandemic. In the last two years, there was an important development of studies focused on both challenges and acceptance and consensus in the field of Artificial Intelligence. The challenges and acceptance and consensus are two strategic aspects in the development and integration of technologies in the health domain. The study conducted two narrative reviews by means of two parallel points of view to take stock both on the ongoing challenges and on initiatives conducted to face the acceptance and consensus in this area. The methodology of the review was based on: (I) search of PubMed and Scopus and (II) an eligibility assessment, using parameters with 5 levels of score. The results have: (a) highlighted and categorized the important challenges in place. (b) Illustrated the different types of studies conducted through original questionnaires. The study suggests for future research based on questionnaires a better calibration and inclusion of the challenges in place together with validation and administration paths at an international level.

Utility and diagnostic test properties of pulmonary and cardiovascular point of care ultra-sonography (POCUS) in COVID-19 patients admitted to critical care unit

Background

We conducted this study to assess the diagnostic test properties of point of care ultrasonography (POCUS) of lung and cardiovascular system in prediction of mortality in COVID-19 patients.

Methods

This is a cross-sectional study of 178 Covid-19 patients; POCUS was performed within one hour of admission to the ICU. We estimated sensitivity, specificity, positive predictive value, negative predictive value for prediction of mortality.

Results

The mean (SD) age of these patients was 57.3 (12.8) years. The findings were on cardiac ultrasonography were: mild pericardial effusion (45%), chamber dilatation (15%), hypokinesia (11%), and low ejection fraction (8%). In our study, 30 patients (17%) had died. A cut-off score of > to 13 (for lung ultrasound score [LUS]) had high sensitivity for mortality (93.3%, 95% CI: 77.9–99.2%). However, low ejection fraction (92.3%, 95% CI: 86,6–96.1%), and thrombosis in either vein (96.5%, 95% CI: 92.0–98.9%) were specific for mortality. A combination of LUS > =13 or low ejection fraction or thrombosis or spontaneous echo contrast (slow flow) improved sensitivity for mortality to 96.7% (95% CI: 82.8–99.9%). The agreement between LUS of > =13 and CT score of moderate/severe was 85.7% (95% CI: 62.8–100%). The interrater agreement between these two parameters was 0.82 (95% CI: 0.68, 0.97).

Conclusions

Multi-organ POCUS is effective in diagnosis, prognosis, and management of COVID-19 patients. Rather than just lung ultrasound, clinicians should use multiorgan POCUS for early identification of severe lung involvement and thrombotic changes; it may help reduce mortality in these patients.

GA, Chegodar AS, Rumyantsev KA, Sviridov FS, Osipova AV, Danishevich AM, Noskova KK, Pinashina ES, Orlova NV, Slezinger VM, Chernova ME, Tovstyko KA, Bodunova NA. Clinical portrait of a patient with COVID-19. The experience of a multidisciplinary clinic

Aim. Evaluation of the clinical characteristics in patients with COVID-19. Materials and methods. The article presents clinical and instrumental data of 1169 patients included in a single-center mixed study. Patients were tested for COVID-19 using polymerase chain reaction, computed tomography (CT), and antibody screening. Clinical history data were collected. Results. In patients with confirmed COVID-19, lung damage and a positive test for antibodies were observed in 75.5 and 45.2% of cases, respectively. The most common symptoms of COVID-19 were: fever (73.2%), weakness, (72.7%) dry cough (62.8%) and shortness of breath (55.4%). Patients with CT-visualized lung lesions were more likely to have clinical symptoms and elevated levels of antibodies. Patients with chronic diseases of the endocrine system, circulatory system, and respiratory system had a more severe course of the disease (CT-14: 91.296.3%) than patients without chronic diseases (CT-14: 85,1%). Conclusion. We have compiled a clinical profile of patients with COVID-19 and highlighted the most significant clinical characteristics corresponding to a more severe course of the disease. Our data showed that patients with chronic diseases require a personalized approach and the development of specific criteria for the diagnosis and treatment of COVID-19.