ABSTRACT
Objective During the coronavirus disease 2019 (COVID-19) pandemic, many social activities have moved online using applications for digital devices (e.g. computers, smartphones). We investigated the needs of telemedicine and trends in medical status and social care situations of Japanese patients with neurological disorders in order to estimate their affinity for an online telemedicine application. Methods We designed an original questionnaire for the present study that asked participants what problems they had with hospital visits, how the COVID-19 pandemic had affected their lives, and whether or not they would like to receive telemedicine. Patients The present study included volunteer caregivers, participants with Parkinson's disease (PD), epilepsy, stroke, dementia, immune-mediated neurological disease (IMMD), spinocerebellar degeneration (SCD), amyotrophic lateral sclerosis (ALS), headache, myopathy, and other neurological diseases from Okayama University Hospital. Results A total of 29.6% of patients wanted to use telemedicine. Patients with headaches (60.0%) and epilepsy (38.1%) were more likely to want to use telemedicine than patients with PD (17.8%) or stroke (19.0%). Almost 90% of patients had access to a digital device, and there was no association between favoring telemedicine, ownership of a digital device, hospital visiting time, or waiting time at the hospital, although age was associated with motivation to telemedicine use (52.6 vs. 62.2 years old, p <0.001*). Conclusion We can contribute to the management of the COVID-19 pandemic and the medical economy by promoting telemedicine, especially for young patients with headaches or epilepsy.
ABSTRACT
Objective: The objective of the current study was to develop a novel, artificial intelligence (AI)-based system to diagnose coronavirus disease (COVID-19) using computed tomography (CT) slice images. Prior research has demonstrated that, if not focused on the lungs, AI diagnoses COVID-19 using information outside the lungs. The inclusion of CT training data from multiple facilities and CT models may also cause AI to diagnose COVID-19 with features that are irrelevant to COVID-19. Thus, the objective of the current study was to evaluate a combination of lung mask images and CT slice images from a single facility, using a single CT model, and use AI to differentiate COVID-19 from other types of pneumonia based solely on information related to the lungs. Method: By superimposing lung mask images on image feature output using an existing AI structure, it was possible to exclude image features other than those around the lungs. The results of this model were also compared with the slice image findings from which only the lung region was extracted. The system adopted an ensemble approach. The outputs of multiple AIs were averaged to differentiate COVID-19 cases from other types of pneumonia, based on CT slice images. Results: The system evaluated 132 scans of COVID-19 cases and 62 scans of non-COVID-19 cases taken at the single facility using a single CT model. The initial sensitivity, specificity, and accuracy of our system, using a threshold value of 0.50, was shown to be 95%, 53%, and 81%, respectively. Setting the threshold value to 0.84 adjusted the sensitivity and specificity to clinically usable values of 76% and 84%, respectively. Conclusion: The system developed in the current study was able to differentiate between pneumonia due to COVID-19 and other types of pneumonia with sufficient accuracy for use in clinical practice. This was accomplished without the inclusion of images of clinically meaningless regions and despite the application of more stringent conditions, compared to prior studies.