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Neurology ; 96(15 SUPPL 1), 2021.
Article in English | EMBASE | ID: covidwho-1407941


Objective: NA Background: Deep Brain Stimulation (DBS), an effective therapy for Parkinson's Disease (PD), requires reliable access to trained neurologists, experienced in managing DBS therapy, and specialty care centers, typically located in dense cities. This contributes to increased care access burden for patients residing in distant areas. Here, we investigated a remote care platform that enabled experts to adjust DBS therapy settings in real-time via a secure video-based mobile platform. Design/Methods: This study was designed to prospectively evaluate the feasibility and safety of an investigational remote care platform, developed in alignment with established data and security standards. PD patients, implanted with an Infinity™ DBS system, were enrolled in the study. The primary endpoint of this study was to determine remote care feasibility and safety by evaluating adverse events With in 3 weeks of the programming session conducted at the randomization visit. At the randomization visit, ten subjects connected with their clinician through the secure remote care platform where the clinician could modulate and save updated DBS programming parameters. Results: At the 3-week primary endpoint, one subject experienced diminished therapy, an adverse event that was resolved without sequalae. Remote changes in programming included modulating amplitude, frequency, pulse-width and contact selection. Further, the clinician was able to make changes from variable settings (a different room in-clinic, a remote urban or a rural setting). Conclusions: Previous studies have shown that telemedicine is a potential strategy to improve care access. The current COVID-19 pandemic has emphasized the need to address this imminent challenge of safely accessing health care options. In this study, we characterized the safety and feasibility of our remote care platform, an integrated telemedicine option for PD patients. Evaluation of such remote care platforms advances the field towards low-burden therapy options for patients, clinicians, and government agencies in the rapidly emerging digital health realm.

Non-conventional | WHO COVID | ID: covidwho-477959


The COVID-19 infection is increasing at a rapid rate, with the availability of limited number of testing kits. Therefore, the development of COVID-19 testing kits is still an open area of research. Recently, many studies have shown that chest Computed Tomography (CT) images can be used for COVID-19 testing, as chest CT images show a bilateral change in COVID-19 infected patients. However, the classification of COVID-19 patients from chest CT images is not an easy task as predicting the bilateral change is defined as an ill-posed problem. Therefore, in this paper, a deep transfer learning technique is used to classify COVID-19 infected patients. Additionally, a top-2 smooth loss function with cost-sensitive attributes is also utilized to handle noisy and imbalanced COVID-19 dataset kind of problems. Experimental results reveal that the proposed deep transfer learning-based COVID-19 classification model provides efficient results as compared to the other supervised learning models.