The following article, AI Holds Promise In Detecting Patients' Pain During Surgery, was first published on Flag And Cross.
Artificial intelligence could ease the ordeal of an operation by detecting patients’ pain before, during and after surgery, according to a new study.
American scientists say the automated pain recognition system using artificial intelligence (AI) holds “promise” for those going under the knife.
Early recognition and effective treatment of pain have been previously shown to decrease the length of stays in hospital and prevent long-term health conditions including chronic pain, anxiety and depression.
Subjective methods are currently 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 doctors and nurses rate the patient’s pain based on facial expression, body movement and muscle tension.
The new 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.
Study lead author Timothy Heintz said: “Traditional pain assessment tools can be influenced by racial and cultural biases, potentially resulting in poor pain management and worse health outcomes.
“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.”
Researchers provided the AI model 143,293 facial images from 115 pain episodes and 159 non-pain episodes in 69 patients who underwent a range of elective surgical procedures, including knee and hip replacements to complex heart surgeries.
The research team 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.
Once it was provided enough examples, it used the learned knowledge to make pain predictions.
The AI-automated pain recognition system aligned with CPOT results 88 percent of the time and with VAS 66 percent of the time.
Heintz, a fourth-year medical student at the University of California San Diego, said: “The VAS is less accurate compared to CPOT because VAS is a subjective measurement that can be more heavily influenced by emotions and behaviors than CPOT might be.
“However, our models were able to predict VAS to some extent, indicating there are very subtle cues that the AI system can identify that humans cannot.”
He said if the findings are validated, the state-of-the-art technology may become an additional tool doctors could use to improve patient care.
Heintz said: “For example, cameras could be mounted on the walls and ceilings of the surgical recovery room 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 – to focus on other areas of care.”
The research team plan to continue to incorporate other factors such as movement and sound into the model.
Heintz added: “Concerns about privacy would need to be addressed to ensure patient images are kept private, but the system could eventually include other monitoring features, such as brain and muscle activity to assess unconscious patients.”
The findings are due to be presented at the annual meeting of the American Society of Anesthesiologists in San Francisco.
Produced in association with SWNS Talker
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