Analgesia, Artificial Intelligence, Facial Expression, Pain, Postoperative
Conditions
Brief summary
Patients' subjective complaints about pain intensity are difficult to objectively evaluate, and may lead to inadequate pain management, especially in patients with communication difficulties.
Detailed description
Analgesia nociception index (ANI 0-100) and patient-reported numeric rating scale (NRS 0-10) were trained on a convolutional neural network (CNN) model by linking the patients' facial expression with the score. By applying the predicted pain score by the AI model to evaluate pain, it is intended to measure the intensity of pain in an automatic, fast, and objective way for appropriate pain management.
Interventions
Immediately after surgery, the patient's facial expression and the NRS score and ANI score reported by the patient are checked together.
Sponsors
Study design
Eligibility
Inclusion criteria
* Patients aged 19-75 years who were scheduled for elective laparoscopic abdominal surgery under general anesthesia * American Society of Anesthesiology (ASA) class I-II
Exclusion criteria
* Patients who have difficulty in communicating and reporting pain * Underlying diseases: liver, kidney, brain * Patients with BMI greater than 30 and less than 18.5 * Alcohol or drug dependent patients * Patients with severe or acute respiratory failure * Opioid, NSAID allergy * Patients who are scheduled to be admitted to the intensive care unit after surgery * Patients who undergo cooperative surgery
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Facial expression | immediately preoperative, postoperative time | Painful facial expression |
| analgesia nociception index | immediately preoperative, postoperative time | ANI score |
| numeric rating scale | immediately preoperative, postoperative time | pain score |
Countries
South Korea