An Automatic CNN-based Face Mask Detection Algorithm Tested During the COVID-19 Pandemics

The ongoing COVID-19 pandemic has highlighted the importance of wearing face masks as a preventive measure to reduce the spread of the virus. In medical settings, such as hospitals and clinics, healthcare professionals and patients are required to wear surgical masks for infection control. However, the use of masks can hinder facial recognition technology, which is commonly used for identity verification and security purposes. In this paper, we propose a convolutional neural network (CNN) based approach to detect faces covered by surgical masks in medical settings. We evaluated the proposed CNN model on a test set comprising of masked and unmasked faces. The results showed that our model achieved an accuracy of over 96% in detecting masked faces. Furthermore, our model demonstrated robustness to different mask types and fit variations commonly encountered in medical settings. Our approaches reaches state of the art results in terms of accuracy and generalization. © 2022 Copyright for this paper by its authors.

Analysis Pre and Post COVID-19 Pandemic Rorschach Test Data of Using EM Algorithms and GMM Models

The global spread of the COVID-19 virus has become one of the greatest challenges that humanity has faced in recent years. The unprecedented circumstances of forced isolation and uncertainty that it has imposed on us continue to impact our mental well-being, whether or not we have been directly affected by the virus. Over a period of nearly three years (2017-2020), data was collected from multiple administrations of the Rorschach test, one of the most renowned and extensively studied psychological tests. This study involved the clustering of data, collected through the RAP3 software, to analyze the distinctive trends in data recorded before and after the pandemic. This was achieved through the implementation of the well-established machine learning algorithm, Expectation-Maximization. The proposed solution effectively identifies the key variables that significantly influence the subject's score and provides a reliable solution. Additionally, the solution offers an intuitive visualization that can assist psychologists in accurately interpreting shifts in trends and response distributions within a large amount of data in the two periods. © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0)

Understanding knowledge and behaviors related to CoViD-19 epidemic in Italian undergraduate students: the EPICO study

Background: On February 2020, the novel coronavirus (2019-nCoV) epidemic began in Italy. In order to contain the spread of the virus, the Italian government adopted emergency measures nationwide, including closure of schools and universities, workplaces and subsequently lockdown. This survey was carried out among Italian undergraduates to explore their level of knowledge about the epidemic and the behaviors they adopted during the lockdown.

Hierarchical convolutional models for automatic pneu-monia diagnosis based on X-ray images: new strategies in public health.

Conclusions: Despite some limits, our findings support the notion that deep learning methods can be used to simplify the diagnostic process and improve disease management. Background: In order to help physicians and radiologists in diagnosing pneumonia, deep learning and other artificial intelligence methods have been described in several researches to solve this task. The main objective of the present study is to build a stacked hierarchical model by combining several models in order to increase the procedure accuracy. Methods: Firstly, the best convolutional network in terms of accuracy were evaluated and described. Later, a stacked hierarchical model was built by using the most relevant features extracted by the selected two models. Finally, over the stacked model with the best accuracy, a hierarchically dependent second stage model for inner-classification was built in order to detect both inflammation of the pulmonary alveolar space (lobar pneumonia) and interstitial tissue involvement (interstitial pneumonia). Results: The study shows how the adopted staked model lead to a higher accuracy. Having a high accuracy on pneumonia detection and classification can be a paramount asset to treat patients in real health-care environments.

The exponential phase of the COVID-19 pandemic in Central Italy: an integrated care pathway

The Coronavirus Disease (Covid-19) pandemic is rapidly spreading across the world, representing an unparalleled challenge for health care systems. There are differences in the estimated fatality rates, which cannot be explained easily. In Italy, the estimated case fatality rate was 12.7% in mid-April, while Germany remained at 1.8%. Moreover, it is to be noted that different areas of Italy have very different lethality rates. Due to the complexity of Covid-19 patient management, it is of paramount importance to develop a well-defined clinical workflow in order to avoid the inconsistent management of patients. The Integrated Care Pathway (ICP) represents a multidisciplinary outline of anticipated care to support patient management in the Sant'Andrea Hospital, Rome. The main objective of this pilot study was to develop a new ICP evaluated by care indicators, in order to improve the COVID-19 patient management. The suggested ICP was developed by a multi-professional team composed of different specialists and administrators already involved in clinical and management processes. After a review of current internal practices and published evidences, we identified (1) the activities performed during care delivery, (2) the responsibilities for these activities, (3) hospital structural adaptation needs and potential improvements, and (4) ICP indicators. The process map formed the basis of the final ICP document;160 COVID-19 inpatients were considered, and the effect of the ICP implementation was evaluated over time during the exponential phase of the COVID-19 pandemic. In conclusion, a rapid adoption of ICP and regular audits of quality indicators for the management of COVID-19 patients might be important tools to improve the quality of care and outcomes.

Reducing the psychological burden of isolated oncological patients by means of decision trees

This century has seen several outbreaks of epidemics caused by a common sub-family of coronaviruses such as the responsible for COVID-19 outbreak. The most ominous variants have developed a peculiar viral mechanisms that allows the virus to directly attack the pulmonary tissues often causing a set of dangerous symptoms. It made quite evident that we need a global response to prepare health systems for future epidemics. Unfortunately, during such kind of diseases’ outbreaks a large amount of time is required to the caregivers for sanitization and cleaning operations, therefore tampering with number and duration of visits to patients, especially in oncology wards. Such patients are then left alone for a long time, it follows that their perceived quality of service is greatly diminished, often determining ill-fated consequences also on the psychological side, with significant fallbacks on the recovery possibilities and speed. In this paper we explore an algorithmic approach to automatic communication interfaces that could enhance and enforce the perceived quality of care by the patients in in order to reduce predisposing factors that could potentially tamper with the patient’s ability to recover, also preventing the occurrence of precipitating factors that could lead a therapy to complete failure. The proposed interface could be used to connect the patients with a psychological support when it is most needed, and, moreover, to connect them with their physicians and families, and also to the outside world. In particular we aim to provide the psychological support that is actually excluded in pandemics such as the COVID-19 emergency, mainly in order to enforce the healthcare and sanification protocols, due to its potential unsafety related to the introduction of more personnel into the hospital. © 2020 Copyright for this paper by its authors.

SARS-CoV-2 indoor contamination: considerations on anti-COVID-19 management of ventilation systems, and finishing materials in healthcare facilities.

Abstract: Many of the devastating pandemics and outbreaks of last centuries have been caused by enveloped viruses. The recent pandemic of Coronavirus disease 2019 (COVID-19) has seriously endangered the global health system. In particular, hospitals have had to deal with a frequency in the emergency room and a request for beds for infectious diseases never faced in the last decades. It is well-known that hospitals are environments with a high infectious risk. Environmental control of indoor air and surfaces becomes an important means of limiting the spread of SARS-CoV-2. In particular, to preserve an adequate indoor microbiological quality, an important non-pharmacological strategy is represented by Heating, Ventilation and Air Conditioning (HVAC) systems and finishing materials. Starting from the SARS-CoV-2 transmission routes, the paper investigates the hospital risk analysis and management, the indoor air quality and determination of microbial load, surface management and strategies in cleaning activities, HVAC systems' management and filters' efficiency. In conclusion, the paper suggests some strategies of interventions and best practices to be taken into considerations for the next steps in design and management.

A cloud-oriented architecture for the remote assessment and follow-up of hospitalized patients

During the last months the dramatic COVID-19 outbreak has exposed the fragility of our healthcare system, as well as the need for a smart remote follow-up system for the patients, in order to less the burden on the healthcare service and reduce the average hospitalization time. In this paper we proposed a solution designed to grant maximum flexibility by means of the allocation of resources on a cloud service for the remote follow-up of patients. Such resources can be used as a remote support for the caregiver both when planning or enforcing a therapeutic path. A major explanation behind follow-up regards the location and treatment of potentially adverse reactions after treatments. Physical side effects of the different modalities of treatment might be various and crippling after chemotherapy and radiotherapy. Moreover remote follow up can be a life-changing solution also on the economical side, due to the implication of therapeutic attendances for patients as far as missed work and travel costs that must likewise be comprehended in the overall economical burden. In an investigation of patients with testicular disease, Campbell et al. Finally such a solution could effectively improve the patient's adherence to the therapeutic plan. The ability to remotely follow follow-up is therefore a monetarily alluring choice as far as investment funds, also given the improved efficiencies, reduced cost and number of missed working days for the patient. Patients with a patient-held record may also take advantage of a more conscious and motivated interest over their own wellbeing, illness and treatment, with a direct impact on patient's adherence to the therapeutic plan. © 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).