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 research presented at
the ANESTHESIOLOGY® 2023 annual meeting.
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. 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."
Timothy Heintz, B.S., lead author of the study and a
fourth-year medical student at the University of California San Diego
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. 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% of the time and with VAS 66% of the time.
"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," said Heintz. "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."
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 -; to focus on other areas of care. The researchers plan to
continue to incorporate other variables such as movement and sound into the
model.
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, he said.
