Pain Detection Through Automated Video Analysis

Summary

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Description

Post-surgical pain, if inadequately controlled, has deleterious short and long term consequences for the patient. Although most patients are able to report their pain scores, a minority are unable to do so and assessing their pain can prove to be a challenge for healthcare professionals. In recent y...

Post-surgical pain, if inadequately controlled, has deleterious short and long term consequences for the patient. Although most patients are able to report their pain scores, a minority are unable to do so and assessing their pain can prove to be a challenge for healthcare professionals. In recent years, facial recognition tools have been developed based on the premise that subtle facial variations signifies pain. However, changes in body, and head posture can also represent pain. As such, these tools are with their limitations and are only validated on certain groups of patients, thus may not be sensitive enough to detect pain in post-surgical patients. This pilot study will be conducted on 20 patients presenting for major gynaecological surgery, with the obtained data used to fine tune the algorithm. The patients will be video-taped pre-surgically in the pre-evaluation anaesthetic clinic and post-surgically in the ward. They will be asked to rate their pain scores on the numerical rating scale and fill in questionnaires on their psychological and quality of health status. The pain scores will be correlated with the results obtained from the pain assessment algorithm. The refined algorithm can be subsequently evaluated with a larger group of patients undergoing different pain conditions, and could potentially be used in clinical practice as a tool to assess pain if found to be sensitive.

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