Diagnosis and combating COVID-19 using wearable Oura smart ring with deep learning methods.

Area of Science:

• Medical Informatics
• Artificial Intelligence
• Deep Learning

Background:

• The global spread of coronavirus disease (COVID-19) necessitates advanced technological solutions for monitoring and combating the epidemic.
• Artificial intelligence (AI) offers potential strategies for tracking infection spread, identifying high-risk patients, and managing the disease.
• Accurate and rapid diagnostic tools are crucial for effective public health interventions and patient care during the pandemic.

Purpose of the Study:

• To propose a hybrid deep learning method for the rapid and accurate diagnosis of COVID-19.
• To develop an AI-driven approach for assessing COVID-19 severity using patient data and medical imaging.
• To leverage smart technologies like the Oura ring for timely COVID-19 prediction and diagnosis.

Main Methods:

• A hybrid deep learning model combining Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) algorithms was developed.
• A layered approach was employed to analyze patient symptom levels and medical image data (X-ray, CT scans).
• Four weighting parameters were utilized to minimize false positive and false negative classification results.

Main Results:

• The deep learning model achieved rapid diagnosis of COVID-19, with results indicating presence (1) or absence (0) of infection.
• Image classification thresholds (0.5 for initial, 1 for severe) were used to assess disease severity.
• The proposed methods demonstrated effectiveness in reducing classification errors for accurate diagnosis.

Conclusions:

• The hybrid deep learning approach provides a rapid and accurate method for COVID-19 diagnosis.
• AI-powered analysis of patient data and medical images can effectively assess disease severity.
• This technology holds significant potential for improving global patient support and infection control strategies.