Artificial Intelligence to Evaluate Postoperative Pain Based on Facial Expression

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

RECRUITING

Total Enrollment

120 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-06-17

Study Completion Date

2026-05-09

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Patients' subjective complaints about pain intensity are difficult to objectively evaluate, and may lead to inadequate pain management, especially in patients with communication difficulties.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Acute Pain Assessment Using Facial Expression Analysis

NCT03957967

Acute Pre and Post Operative Pain

COMPLETED

Efficiency of Facial Expression Diagnostic System in Pain Assessment After Geriatric Surgery

NCT05756140

Pain Aging Problems

RECRUITING

Pain Detection Through Automated Video Analysis

NCT04011189

Pain, Postoperative

ACTIVE_NOT_RECRUITING NA

Application of Non-invasive Neurophysiological Methods in Predicting and Evaluating Postoperative Pain in Adult Patients Undergoing Major Thoracic Surgery

NCT06731010

Postoperative Pain, Acute Postoperative Pain, Chronic Postoperative Pain After Thoracic Surgery

COMPLETED

ASSESSMENT OF PAIN USING A PAIN DIAGNOSTIC SYSTEM THAT MEASURS FACIAL MOVEMENT USING

NCT06485856

Nursing Pain Assessment Critical Care +2 more

RECRUITING NA

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Artificial Intelligence Facial Expression Analgesia Pain, Postoperative

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

taking a picture of a painful facial expression

Immediately after surgery, the patient's facial expression and the NRS score and ANI score reported by the patient are checked together.

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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

Minimum Eligible Age

19 Years

Maximum Eligible Age

75 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No