Smartphone-based detection of COVID-19 and associated pneumonia using thermal imaging and a transfer learning algorithm.
Oshrit HofferRafael Y BrzezinskiAdam GanimPerry ShalomZehava Ovadia-BlechmanLital Ben-BaruchNir LewisRacheli PeledCarmi ShimonNili Naftali-ShaniEyal KatzYair ZimmerNeta RabinPublished in: Journal of biophotonics (2024)
COVID-19-related pneumonia is typically diagnosed using chest x-ray or computed tomography images. However, these techniques can only be used in hospitals. In contrast, thermal cameras are portable, inexpensive devices that can be connected to smartphones. Thus, they can be used to detect and monitor medical conditions outside hospitals. Herein, a smartphone-based application using thermal images of a human back was developed for COVID-19 detection. Image analysis using a deep learning algorithm revealed a sensitivity and specificity of 88.7% and 92.3%, respectively. The findings support the future use of noninvasive thermal imaging in primary screening for COVID-19 and associated pneumonia.
Keyphrases
- deep learning
- coronavirus disease
- sars cov
- high resolution
- convolutional neural network
- computed tomography
- healthcare
- machine learning
- artificial intelligence
- endothelial cells
- respiratory syndrome coronavirus
- magnetic resonance imaging
- loop mediated isothermal amplification
- community acquired pneumonia
- dual energy
- real time pcr
- mass spectrometry
- label free
- respiratory failure
- contrast enhanced
- pet ct
- fluorescence imaging
- mechanical ventilation