ABSTRACT
This study proposed an infrared image-based method for febrile and non-febrile people screening to comply with the society needs for alternative, quick response, and effective methods for COVID-19 contagious people screening. The methodology consisted of: (i) Developing a method based on the face infrared imaging for early COVID-19 detection in people with and without fever;(ii) Recruiting 1206 emergency room (ER) patients to develop an algorithm for general application of the method, and (iii) Testing the method and algorithm effectiveness in 2558 cases (RTqPCR tested for COVID-19) from 227,261 workers evaluations in five different countries. Artificial intelligence was used with a convolutional neural network (CNN) to develop the algorithm that took face infrared images as input and classified the tested individuals into three groups: fever (high risk), non-febrile (medium risk), and without fever (low risk). The results showed that suspicious and confirmed COVID-19 (+) cases characterized by temperatures below the 37.5 °C fever threshold were identified. Also, average forehead and eye temperatures greater than 37.5 C were not enough to detect fever similarly to the proposed CNN algorithm. Most RT-qPCR confirmed COVID-19 (+) cases found in the 2558 cases sample (17 cases/89.5%) belonged to the CNN selected non-febrile COVID group. The COVID-19 (+) main risk factor was to be in the non-febrile medium-risk group, compared with age, diabetes, high blood pressure, smoking and others. In sum, the proposed method was shown to be a potentially important new tool for COVID-19 (+) people screening for air travel and public places in general. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
ABSTRACT
Background and aims: In early 2020, the novel coronavirus disease (COVID-19) pandemic has impaired medical care of chronic neurological diseases, including epilepsy. The objective of this study is to evaluate the impact of the COVID-19 pandemic in the levels of anxiety, depression, somnolence and quality of life using validated scales in patients with epilepsy in real-life clinical practice. Methods: Self-administered scales of anxiety disorders (GAD-7), depression (NDDI-E), somnolence (Epworth Sleepiness Scale;ESS) and quality of life (QOLIE-31-P) in patients with epilepsy treated in the Refractory Epilepsy Unit of a tertiary hospital were longitudinally analyzed with Generalized Linear Mixed Models. Data were collected before the beginning (December 2019-March 2020) and during the COVID-19 pandemic (September 2020-January 2021). Results: 37 patients, 45.0±17.3 years of age, 43.2% women, epilepsy duration 23.0±14.9 years, number of anti-epileptic drugs 2.1±1.4, answered in the two periods. Significant longitudinal reduction of QOLIE-31-P scores (from 58.9±19.7 to 56.2±16.2, p=0.035) was identified. No statistically significant longitudinal changes in NDDI-E (from 12.3±4.3 to 13.4±4.4, p=0.293) or the number of seizures (from 0.9±1.9 to 2.5±6.2, p=0.125) were found. Significant higher ESS (from 4.9±3.7 to 7.4±4.9, p=0.001) and lower GAD-7 scores (from 8.8±6.2 to 8.3±5.9, corrected p=0.024 adjusted by refractory epilepsy and sleep disturbance) were found during the COVID-19 pandemic. Conclusion: During the COVID-19 pandemic, quality of life was lower in patients with epilepsy, levels of anxiety were reduced and sleepiness levels were raised, without seizure change. Additional studies would be useful to adequately manage these comorbidities.