This study will refine and pilot the feasibility of introducing a thermal imaging test to detect fever in 100 patients being triaged within the Emergency Department. The only additional research requirement for the patient is to have a thermal image of their face taken. Other triage tests will be routine.

The aims of the feasibility study are to:

• Understand the acceptability of introducing the intervention within the Emergency Department setting
• Establish indicative patient recruitment numbers per week
• Determine the likely proportion of patients recruited from this group who have a high temperature
• Provide preliminary evidence that the technology can identify a high temperature in this diverse group of patients
• Provide preliminary data for machine learning training to support classification of patients as being with or without fever

The feasibility study will then inform the design and size of larger study to further develop and validate the the thermal imaging screening test to provide a 'with/ without' fever result.

Justification for research and project plan Background In the fifty years since the emergence of thermal imaging technology, Thales in Glasgow has built up a world leading capability in the design, manufacture and supply of Thermal Imaging cameras. In addition to the cameras, Thales in Glasgow has a particular expertise in developing image processing algorithms (conventional and artificial intelligence based) to allow the cameras to perform critical user tasks beyond mere imaging.

COVID-19 [SARS-COV-2] has placed a huge challenge on the world. In response to the crisis, Thales is engaged in a number of initiatives, including one aimed at the possible application of thermal imaging cameras to detect people with a fever and hence those who may be suffering from COVID-19. This initiative has received encouraging feedback from prospective users around the world who are looking, not only at the immediate issue in the hospitals, but also forward to a time beyond the current lockdowns, when cost effective techniques for surveying groups of people for potential COVID-19 sufferers will be required. This surveillance, for example, could be in hospitals, at airport gates, in buildings or outside in streets. As part of the initiative Thales Glasgow has been performing analysis and experiments using cameras looking at faces to confirm what temperature differences can be measured.

What Thales lacks is real world thermal images of patients suffering fever and access to clinicians who can advise on the medical aspects of the work. This research would fill these two voids.

Benefit and Rationale Existing thermal camera based fever detection systems suffer from two main issues that restrict their current application: Cost and Sensitivity.In order to achieve the required sensitivity, existing systems often require Black Body (BB) calibration sources to be visible in the scene. These BB sources are often more expensive than the cameras (circa one to two orders of magnitude more expensive). Thales believe they can achieve the required sensitivity without the need for BB sources by using advanced thermal camera correction algorithms.

The variability in skin emissivity makes conversions from irradiance as measured by a thermal camera, to absolute skin temperature inaccurate. Previous work place the emissivity of human skin between 0.990 and 0.999, however earlier research suggests that the value is 0.971 ± 0.005(SD). Typically in existing systems, this inaccurate, absolute temperature is used along with a simple threshold algorithm to find skin above a certain temperature leading to both false positives and false negatives.

Significant statistical correlation between the temperature of multiple facial features and measured human temperature has been observed. Their research found that for most facial features there was a correlation between multiple aspects of the facial view and recorded oral and tympanic temperature, with correlations of 0.5 or greater observed for multiple aspects of both the front and side of the face, with the ear offering the greatest statistical correlation.

Inclusion Criteria:

• Patients ≥ 16 years old
• Patients able to read and understand English
• Patients able to give informed consent
• Patients being triaged through ED for any complaint (not necessarily COVID-19)

Exclusion Criteria:

• Patients not meeting the inclusion criteria
• Patients who do not have capacity to consent
• Patients attending ED who are fast-tracked without triage

All patients attending Queen Elizabeth University Hospital Emergency Department requiring triage