Feasibility of Radiomics to Differentiate Coronavirus Disease 2019 (COVID-19) from H1N1 Influenza Pneumonia on Chest Computed Tomography: A Proof of Concept.

BACKGROUND: Chest computed tomography (CT) plays an essential role in diagnosing coronavirus disease 2019 (COVID-19). However, CT findings are often nonspecific among different viral pneumonia conditions. The differentiation between COVID-19 and influenza can be challenging when seasonal influenza concurs with the COVID-19 pandemic. This study was conducted to test the ability of radiomics-artificial intelligence (AI) to perform this task. METHODS: In this retrospective study, chest CT images from 47 patients with COVID-19 (after February 2020) and 19 patients with H1N1 influenza (before September 2019) pneumonia were collected from three hospitals affiliated with Arak University of Medical Sciences, Arak, Iran. All pulmonary lesions were segmented on CT images. Multiple radiomics features were extracted from the lesions and used to develop support-vector machine (SVM), k-nearest neighbor (k-NN), decision tree, neural network, adaptive boosting (AdaBoost), and random forest. RESULTS: The patients with COVID-19 and H1N1 influenza were not significantly different in age and sex (P=0.13 and 0.99, respectively). Nonetheless, the average time between initial symptoms/hospitalization and chest CT was shorter in the patients with COVID-19 (P=0.001 and 0.01, respectively). After the implementation of the inclusion and exclusion criteria, 453 pulmonary lesions were included in this study. On the harmonized features, random forest yielded the highest performance (area under the curve=0.97, sensitivity=89%, precision=90%, F1 score=89%, and classification accuracy=89%). CONCLUSION: In our preliminary study, radiomics feature extraction, conjoined with AI, especially random forest and neural network, appeared to yield very promising results in the differentiation between COVID-19 and H1N1 influenza on chest CT.

Current Status and Quality of Machine Learning-Based Radiomics Studies for Glioma Grading: A Systematic Review.

INTRODUCTION: Radiomics now has significant momentum in the era of precision medicine. Glioma is one of the pathologies that has been extensively evaluated by radiomics. However, this technique has not been incorporated into clinical practice. In this systematic review, we selected and reviewed the published studies about glioma grading by radiomics to evaluate this technique's feasibility and its challenges. MATERIAL AND METHODS: Using seven different search strings, we considered all published English manuscripts from 2015 to September 2020 in PubMed, Embase, and Scopus databases. After implementing the exclusion and inclusion criteria, the final papers were selected for the methodological quality assessment based on our in-house Modified Radiomics Standard Scoring (RQS) containing 43 items (minimum score of 0, maximum score of 44). Finally, we offered our opinion about the challenges and weaknesses of the selected papers. RESULTS: By our search, 1,177 manuscripts were found (485 in PubMed, 343 in Embase, and 349 in Scopus). After the implementation of inclusion and exclusion criteria, 18 papers remained for the final analysis by RQS. The total RQS score ranged from 26 (59% of maximum possible score) to 43 (97% of maximum possible score) with a mean of 33.5 (76% of maximum possible score). CONCLUSION: The current studies are promising but very heterogeneous in design with high variation in the radiomics software, the number of extracted features, the number of selected features, and machine learning models. All of the studies were retrospective in design; many are based on small datasets and/or suffer from class imbalance and lack of external validation data-sets.

Artificial Intelligence Empowers Radiologists to Differentiate Pneumonia Induced by COVID-19 versus Influenza Viruses.

BACKGROUND: Given the current pandemic, differentiation between pneumonia induced by COVID-19 or influenza viruses is of utmost clinical significance in the patients' management. For this purpose, this study was conducted to develop sensitive artificial intelligence (AI) models to assist radiologists to decisively differentiate pneumonia due to COVID-19 versus influenza viruses. METHODS: Cross sectional chest CT images (N=12744) from well-evaluated cases of pneumonias induced by COVID-19 or H1N1 Influenza viruses, and normal individuals were collected. We examined the computer tomographic (CT) chest images from 137 individuals. Various pre-trained convolutional neural network models, such as ResNet-50, InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19 were fine-tuned on our datasets. The datasets were used for training (60%), validation (20%), and testing (20%) of the final models. Also, the predictive power and means of precision and recall were determined for each model. RESULTS: Fine-tuned ResNet-50 model differentiated the pneumonia due to COVID-19 or H1N1 influenza virus with accuracies of 96.7% and 92%, respectively This model outperformed all others, i.e., InceptionV3, Wide ResNet, SqueezNet, VGG 16 and VGG 19. CONCLUSION: Fine-tuned and pre-trained image classifying models of AI enable radiologists to reliably differentiate the pneumonia induced by COVID-19 versus H1N1 influenza virus. For this purpose, ResNet-50 followed by InceptionV3 models proved more promising than other AI models. Also in the supplements, we share the source codes and our fine-tuned models for use by researchers and clinicians globally toward the critical task of image differentiation of patients infected with COVID-19 versus H1N1 Influenza viruses.

Telemental Health Care, an Effective Alternative to Conventional Mental Care: a Systematic Review.

BACKGROUND AND OBJECTIVES: Due to the high costs of conventional mental health care, there has been a rise in the application of web-based technologies in recent years, i.e., telemental health care. We conducted this systematic review in 2017, using high quality research articles on the applications, technologies, advantages and challenges associated with telemental health care published since year 2000. METHODS: We used a combination of relevant key words to search four major databases, such as "Web of Sciences, Embase, PubMed and Science Direct". From among 156 articles, which had been published since 2000, twenty five articles met all of the inclusion criteria and were selected for the final review. The information extracted from these articles were used to construct Tables 1 and 2. Also, the materials derived from 55 credible articles were used as further support and complementary facts to substantiate the information presented in the Discussion section. RESULTS: The findings revealed that telemental health care is an extended domain supportive of conventional mental health services. Currently, telemental health care has multiple capabilities and technologies for providing effective interventions to patients with various mental illnesses. It provides clinicians with a wide variety of innovative choices and strategies for mental interventions, in addition to significant future potentials. CONCLUSIONS: Telemental health care can provide effective and adaptable solutions to the care of mental illnesses universally. While being comparable to in-person services, telemental health care is particularly advantageous and inexpensive through the use of current technologies and adaptable designs, especially in isolated communities.

An Evaluation Protocol for Picture Archiving and Communication System: a Systematic Review.

INTRODUCTION: Picture archiving and communication system (PACS) serves to store, transmit, communicate and manage medical images. A logical evaluation protocol assists to determine whether the system is technically, structurally and operationally fit. The purpose of this systematic review was to propose a logical evaluation protocol for PACS, particularly useful for new hospitals and other healthcare institutions in developing countries. METHODS AND MATERIALS: We systematically reviewed 25 out of 267 full-length articles, published between 2000 and 2017, retrieved from four sources: Science Direct, Scopus, PubMed and Google Scholar. The extracted data were tabulated and reviewed successively by three independent panels of experts that oversaw the design of this study and the process by which the PACS evaluation protocol was systematically developed. RESULTS: The outcome data were ranked by expert panels and analyzed statistically, with the reliability established at 0.82 based on the Pearson's correlation coefficient. The essential components and the best options to establish an optimal PACS were organized under nine main sections: system configuration;system network;data storage; datacompression;image input; image characteristics; image presentation; communication link; and system security, with a total of 20 components, each of which capable of working optimally with one or more program options. CONCLUSIONS: This systematic review presents an objective protocol that is an ideal tool for the evaluation of new or existing PACS at healthcare institutions, particularly in developing countries. Despite the significant advantages, the protocol may face minor limitations, largely due to lack of appropriate technical resources in various clinical settings and the host countries.