The present study was conducted to evaluate the use of computer-aided facial expression analysis to assess postoperative pain in children. The study population consisted of patients in the age group of 7-18 years who underwent surgery at Bursa Uludağ University Faculty of Medicine Health Application and Research Hospital Pediatric Surgery Clinic between November 2019 and June 2021. The sample of the study consisted of total 83 children who agreed to participate in the study and who met the sample selection criteria. Data were collected by the researcher using the Wong Baker Faces (WBS) pain rating scale and Visual Analog Scale (VAS). Data were collected from the child, mother, nurse, and one external observer. Facial action units associated with pain were used for machine estimation. OpenFace was used to analyze the child's facial action units and Python was used for machine learning algorithms. Intraclass correlation coefficient, Kappa coefficient, and linear regression analysis were used for statistical analysis of the data. The pain score predicted by the machine and the pain score assessments of the child, mother, nurse, and observer were compared.

Inclusion Criteria:

• The patient is awake and oriented after the surgery,
• The patient must be within the first 12 hours after the operation and not be discharged before the first 36 hours after the operation,
• The patient is between the ages of 7-18,
• It is the mother's staying with the patient as a companion.

Exclusion Criteria:

• Presence of any facial anomalies that may alter facial expression analysis,
• The patient has neurological and mental disorders,
• Having regular opiate use in the last 6 months,
• These are surgical procedures that require the patient to be in a prone position.

The study sample consisted of 7-18 age group patients who underwent surgery in the pediatric surgery clinic of a university hospital.