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At the beginning of 2020, the World Health Organization (WHO) started a coordinated global effort to counterattack the potential exponential spread of the SARS-Cov2 virus, responsible for the coronavirus disease, officially named COVID-19. This comprehensive initiative included a research roadmap published in March 2020, including nine dimensions, from epidemiological research to diagnostic tools and vaccine development. With an unprecedented case, the areas of study related to the pandemic received funds and strong attention from different research communities (universities, government, industry, etc.), resulting in an exponential increase in the number of publications and results achieved in such a small window of time. Outstanding research cooperation projects were implemented during the outbreak, and innovative technologies were developed and improved significantly. Clinical and laboratory processes were improved, while managerial personnel were supported by a countless number of models and computational tools for the decision-making process. This chapter aims to introduce an overview of this favorable scenario and highlight a necessary discussion about ethical issues in research related to the COVID-19 and the challenge of low-quality research, focusing only on the publication of techniques and approaches with limited scientific evidence or even practical application. A legacy of lessons learned from this unique period of human history should influence and guide the scientific and industrial communities for the future.

The adoption of computer-aided diagnosis and treatment systems based on different types of artificial neural networks (ANNs) is already a reality in several hospital and ambulatory premises. This chapter aims to present a discussion focused on the challenges and trends of adopting these computerized systems, highlighting solutions based on different types and approaches of ANN, more specifically, feed-forward, recurrent, and deep convolutional architectures. One section is focused on the application of AI/ANN solutions to support cardiology in different applications, such as the classification of the heart structure and functional behavior based on echocardiography images; the automatic analysis of the heart electric activity based on ECG signals; and the diagnosis support of angiogram images during surgical interventions. Finally, a case study is presented based on the application of a deep learning convolutional network together with a recent technique called transfer learning to detect brain tumors using an MRI images data set. According to the findings, the model has a high degree of specificity (precision of 0.93 and recall of 0.94 for images with no brain tumor) and can be used as a screening tool for images that do not contain a brain tumor. The f1-score for images with brain tumor was 0.93. The results achieved are very promising and the proposed solution may be considered to be used as a computer-aided diagnosis tool based on deep learning convolutional neural networks. Future works will consider other techniques and compare them with the one presented here. With the comprehensive approach and overview of multiple applications, it is valid to conclude that computer-aided diagnosis and treatment systems are important tools to be considered today and will be an essential part of the trend of personalized medicine over the coming years.

Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives.

The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session.

In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested:  (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison  (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons.  (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages.