New AI tool may detect patients’ pain before, during & after surgery

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An automated pain recognition system using artificial intelligence (AI) holds promise as an unbiased method to detect pain in patients before, during and after surgery, according to a research.

Currently, subjective methods are used to assess pain, including the Visual Analog Scale (VAS) -- where patients rate their own pain -- and the Critical-Care Pain Observation Tool (CPOT) -- where health care professionals rate the patient's pain based on facial expression, body movement and muscle tension.

The automated pain recognition system uses two forms of AI, computer vision (giving the computer "eyes") and deep learning so it can interpret the visuals to assess patients' pain.

"Traditional pain assessment tools can be influenced by racial and cultural biases, potentially resulting in poor pain management and worse health outcomes," said lead author Timothy Heintz, a fourth-year medical student at the University of California San Diego.

"Further, there is a gap in perioperative care due to the absence of continuous observable methods for pain detection. Our proof-of-concept AI model could help improve patient care through real-time, unbiased pain detection."

Early recognition and effective treatment of pain have been shown to decrease the length of hospital stays and prevent long-term health conditions such as chronic pain, anxiety and depression.

Researchers provided the AI model 143,293 facial images from 115 pain episodes and 159 non-pain episodes in 69 patients who had a wide range of elective surgical procedures, from knee and hip replacements to complex heart surgeries.

The researchers taught the computer by presenting it with each raw facial image and telling it whether or not it represented pain, and it began to identify patterns. Using heat maps, the researchers discerned that the computer focused on facial expressions and facial muscles in certain areas of the face, particularly the eyebrows, lips and nose.

The AI-automated pain recognition system aligned with CPOT results 88 per cent of the time and with VAS 66 per cent of the time.

If the findings are validated, this technology may be an additional tool physicians could use to improve patient care. For example, cameras could be mounted on the walls and ceilings of the surgical recovery room (post-anesthesia care unit) to assess patients' pain -- even those who are unconscious -- by taking 15 images per second.

This also would free up nurses and health professionals -- who intermittently take time to assess the patient's pain. However, concerns about privacy would need to be addressed to ensure patient images are kept private.

The AI tool was presented at the ongoing ANESTHESIOLOGY 2023 annual meeting in San Francisco, US.

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