Global wealth disparities drive adherence to COVID-safe pathways in head and neck cancer surgery

Peer reviewe

Automatic evaluation of the lung condition of COVID-19 patients using X-ray images and convolutional neural networks

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro and AUCmicro up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro and AUCmicro values are achieved. If ResNet152 is utilized, AUCmacro and AUCmicro values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure

Epidemiological Predictive Modeling of COVID-19 Infection: Development, Testing, and Implementation on the Population of the Benelux Union

Since the outbreak of coronavirus disease-2019 (COVID-19), the whole world has taken interest in the mechanisms of its spread and development. Mathematical models have been valuable instruments for the study of the spread and control of infectious diseases. For that purpose, we propose a two-way approach in modeling COVID-19 spread: a susceptible, exposed, infected, recovered, deceased (SEIRD) model based on differential equations and a long short-term memory (LSTM) deep learning model. The SEIRD model is a compartmental epidemiological model with included components: susceptible, exposed, infected, recovered, deceased. In the case of the SEIRD model, official statistical data available online for countries of Belgium, Netherlands, and Luxembourg (Benelux) in the period of March 15 2020 to March 15 2021 were used. Based on them, we have calculated key parameters and forward them to the epidemiological model, which will predict the number of infected, deceased, and recovered people. Results show that the SEIRD model is able to accurately predict several peaks for all the three countries of interest, with very small root mean square error (RMSE), except for the mild cases (maximum RMSE was 240.79 ± 90.556), which can be explained by the fact that no official data were available for mild cases, but this number was derived from other statistics. On the other hand, LSTM represents a special kind of recurrent neural network structure that can comparatively learn long-term temporal dependencies. Results show that LSTM is capable of predicting several peaks based on the position of previous peaks with low values of RMSE. Higher values of RMSE are observed in the number of infected cases in Belgium (RMSE was 535.93) and Netherlands (RMSE was 434.28), and are expected because of thousands of people getting infected per day in those countries. In future studies, we will extend the models to include mobility information, variants of concern, as well as a medical intervention, etc. A prognostic model could help us predict epidemic peaks. In that way, we could react in a timely manner by introducing new or tightening existing measures before the health system is overloaded

Estimation of covid-19 epidemiology curve of the united states using genetic programming algorithm

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22nd January 2020–3rd December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy

Application of artificial intelligence-based regression methods in the problem of covid-19 spread prediction: A systematic review

COVID-19 is one of the greatest challenges humanity has faced recently, forcing a change in the daily lives of billions of people worldwide. Therefore, many efforts have been made by researchers across the globe in the attempt of determining the models of COVID-19 spread. The objectives of this review are to analyze some of the open-access datasets mostly used in research in the field of COVID-19 regression modeling as well as present current literature based on Artificial Intelligence (AI) methods for regression tasks, like disease spread. Moreover, we discuss the applicability of Machine Learning (ML) and Evolutionary Computing (EC) methods that have focused on regressing epidemiology curves of COVID-19, and provide an overview of the usefulness of existing models in specific areas. An electronic literature search of the various databases was conducted to develop a comprehensive review of the latest AI-based approaches for modeling the spread of COVID-19. Finally, a conclusion is drawn from the observation of reviewed papers that AI-based algorithms have a clear application in COVID-19 epidemiological spread modeling and may be a crucial tool in the combat against coming pandemics

Head and neck cancer surgery during the COVID-19 pandemic: An international, multicenter, observational cohort study

Background: The aims of this study were to provide data on the safety of head and neck cancer surgery currently being undertaken during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This international, observational cohort study comprised 1137 consecutive patients with head and neck cancer undergoing primary surgery with curative intent in 26 countries. Factors associated with severe pulmonary complications in COVID-19–positive patients and infections in the surgical team were determined by univariate analysis. Results: Among the 1137 patients, the commonest sites were the oral cavity (38%) and the thyroid (21%). For oropharynx and larynx tumors, nonsurgical therapy was favored in most cases. There was evidence of surgical de-escalation of neck management and reconstruction. Overall 30-day mortality was 1.2%. Twenty-nine patients (3%) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 30 days of surgery; 13 of these patients (44.8%) developed severe respiratory complications, and 3.51 (10.3%) died. There were significant correlations with an advanced tumor stage and admission to critical care. Members of the surgical team tested positive within 30 days of surgery in 40 cases (3%). There were significant associations with operations in which the patients also tested positive for SARS-CoV-2 within 30 days, with a high community incidence of SARS-CoV-2, with screened patients, with oral tumor sites, and with tracheostomy. Conclusions: Head and neck cancer surgery in the COVID-19 era appears safe even when surgery is prolonged and complex. The overlap in COVID-19 between patients and members of the surgical team raises the suspicion of failures in cross-infection measures or the use of personal protective equipment. Lay Summary: Head and neck surgery is safe for patients during the coronavirus disease 2019 pandemic even when it is lengthy and complex. This is significant because concerns over patient safety raised in many guidelines appear not to be reflected by outcomes, even for those who have other serious illnesses or require complex reconstructions. Patients subjected to suboptimal or nonstandard treatments should be carefully followed up to optimize their cancer outcomes. The overlap between patients and surgeons testing positive for severe acute respiratory syndrome coronavirus 2 is notable and emphasizes the need for fastidious cross-infection controls and effective personal protective equipment

Head and neck cancer surgery during the COVID-19 pandemic: An international, multicenter, observational cohort study

Background: The aims of this study were to provide data on the safety of head and neck cancer surgery currently being undertaken during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This international, observational cohort study comprised 1137 consecutive patients with head and neck cancer undergoing primary surgery with curative intent in 26 countries. Factors associated with severe pulmonary complications in COVID-19–positive patients and infections in the surgical team were determined by univariate analysis. Results: Among the 1137 patients, the commonest sites were the oral cavity (38%) and the thyroid (21%). For oropharynx and larynx tumors, nonsurgical therapy was favored in most cases. There was evidence of surgical de-escalation of neck management and reconstruction. Overall 30-day mortality was 1.2%. Twenty-nine patients (3%) tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) within 30 days of surgery; 13 of these patients (44.8%) developed severe respiratory complications, and 3.51 (10.3%) died. There were significant correlations with an advanced tumor stage and admission to critical care. Members of the surgical team tested positive within 30 days of surgery in 40 cases (3%). There were significant associations with operations in which the patients also tested positive for SARS-CoV-2 within 30 days, with a high community incidence of SARS-CoV-2, with screened patients, with oral tumor sites, and with tracheostomy. Conclusions: Head and neck cancer surgery in the COVID-19 era appears safe even when surgery is prolonged and complex. The overlap in COVID-19 between patients and members of the surgical team raises the suspicion of failures in cross-infection measures or the use of personal protective equipment. Lay Summary: Head and neck surgery is safe for patients during the coronavirus disease 2019 pandemic even when it is lengthy and complex. This is significant because concerns over patient safety raised in many guidelines appear not to be reflected by outcomes, even for those who have other serious illnesses or require complex reconstructions. Patients subjected to suboptimal or nonstandard treatments should be carefully followed up to optimize their cancer outcomes. The overlap between patients and surgeons testing positive for severe acute respiratory syndrome coronavirus 2 is notable and emphasizes the need for fastidious cross-infection controls and effective personal protective equipment