Automated processing of social media content for radiologists: applied deep learning to radiological content on twitter during COVID-19 pandemic

Usage of Student-Created Anatomical Diagrams Shared on Social Media

Social media (SoMe) is a resource for electronic materials in medical education, but has been particularly relevant for anatomy education due to the visual nature of the discipline. Although the distribution of expert/faculty-created anatomy content has been documented, the usefulness of novice/student-created content distributed via SoMe remains undetermined. To address this, original anatomical diagrams (n = 127) created by a novice educator were disseminated via the Anatomy Adventures Instagram account and evaluated for their usefulness. Audience engagement was evaluated using descriptive statistics, with a mean number of likes for all posts (n = 61) of 62.54 + 15.70. Statistically significant differences in the number of likes across content topics were assessed using a Kruskal-Wallis test (H(41.09) = 4, p < 0.005). An 11-item survey (10.6% response rate) explored the (1) population demographics, (2) diagram utility, and (3) suggestions for improvement. Responses were converted to percent frequencies and assessed with chi-square. Descriptive codes were applied to open-ended responses according to published methods. Of the 111 survey responses, 95% of participants were 18-30 years, with the majority of participants being medical students (69.3%), undergraduate/graduate students (16.2%), and fully employed (12.6%). Participants report using the diagrams to study for coursework or board examinations (54%), while non-medical use (42.4%) included leisure viewing or reviewing for their occupation. The usefulness of the diagrams was attributed to their (1) simplicity (43%), (2) style (24.6%), and (3) color-coding (12.3%) (p = 0.0025). These data indicate that Instagram may be utilized by novice educators to provide accurate and accessible resources.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40670-023-01736-9.

Mask images on Twitter increase during COVID-19 mandates, especially in Republican counties

Wearing masks reduces the spread of COVID-19, but compliance with mask mandates varies across individuals, time, and space. Accurate and continuous measures of mask wearing, as well as other health-related behaviors, are important for public health policies. This article presents a novel approach to estimate mask wearing using geotagged Twitter image data from March through September, 2020 in the United States. We validate our measure using public opinion survey data and extend the analysis to investigate county-level differences in mask wearing. We find a strong association between mask mandates and mask wearing-an average increase of 20%. Moreover, this association is greatest in Republican-leaning counties. The findings have important implications for understanding how governmental policies shape and monitor citizen responses to public health crises.

Machine learning-based automatic detection of novel coronavirus (COVID-19) disease

The pandemic was announced by the world health organization coronavirus (COVID-19) universal health dilemma. Any scientific appliance which contributes expeditious detection of coronavirus with a huge recognition rate may be excessively fruitful to doctors. In this environment, innovative automation like deep learning, machine learning, image processing and medical image like chest radiography (CXR), computed tomography (CT) has been refined promising solution contrary to COVID-19. Currently, a reverse transcription-polymerase chain reaction (RT-PCR) test has been used to detect the coronavirus. Due to the moratorium period is high on results tested and huge false negative estimates, substitute solutions are desired. Thus, an automated machine learning-based algorithm is proposed for the detection of COVID-19 and the grading of nine different datasets. This research impacts the grant of image processing and machine learning to expeditious and definite coronavirus detection using CXR and CT medical imaging. This results in early detection, diagnosis, and cure for the accomplishment of COVID-19 as early as possible. Firstly, images are preprocessed by normalization to enhance the quality of the image and removing of noise. Secondly, segmentation of images is done by fuzzy c-means clustering. Then various features namely, statistical, textural, histogram of gradients, and discrete wavelet transform are extracted (92) and selected from the feature vector by principle component analysis. Lastly, k-NN, SRC, ANN, and SVM are used to make decisions for normal, pneumonia, COVID-19 positive patients. The performance of the system has been validated by the k (5) fold cross-validation technique. The proposed algorithm achieves 91.70% (k-Nearest Neighbor), 94.40% (Sparse Representation Classifier), 96.16% (Artificial Neural Network), and 99.14% (Support Vector Machine) for COVID detection. The proposed results show feature combination and selection improves the performance in 14.34 s with machine learning and image processing techniques. Among k-NN, SRC, ANN, and SVM classifiers, SVM shows more efficient results that are promising and comparable with the literature. The proposed approach results in an improved recognition rate as compared to the literature review. Therefore, the algorithm proposed shows immense potential to benefit the radiologist for their findings. Also, fruitful in prior virus diagnosis and discriminate pneumonia between COVID-19 and other pandemics.

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.