Proper management of postoperative pain is an ongoing medical challenge. Inadequate treatment of pain is associated with significantly worse patient outcomes. However, as pain is a subjective experience accurate assessment is difficult.

Commonly used methods for pain assessment include the use of self-reports from patients, or observers assessments.

However, both techniques are subjective to bias. Therefore, automatic assessment of pain based on objective data would enable individualized patient care, optimize provided anesthesia treatment and analgesic regimes.

While research has shown that facial expressions are valid indicators of pain levels, to date research has yet to yield a reliable clinical tool which can be easily implemented in clinical practice.

In this pilot study we intend to assess the feasibility, of facial expression analysis by using machine learning models of artificial intelligence (AI) to accurately predict pain levels of patients experienced in the immediate post operative period.

This pilot trial will take place in two stages:

First stage will include development of an AI algorithm that correlates facial recognition with pain levels.

Second stage will include validation of the algorithm by comparison of to standard pain assessment modalities.

In the first stage each assessment of facial expressions will be filmed in a 30 second segment and will be followed by an immediate pain assessment using two modalities, first will be pain score assessed by an anesthesiologist attending the patient at that moment, second will be VAS assessment by the participant patient. Three objective parameters: heart rate, blood pressure and respiratory rate will be recorded simultaneously from the automated record keeping system used in every patient in the recovery room (post anesthesia care unit-PACU).

These assessments will take place at different time intervals according to the investigator's decision, throughout the participant's staying in the post anesthesia care unit.

After completion of the first stage, the second stage of the study will be done in the same manner as described above regarding patients enrollment. Pain assessment will be done by VAS and physician assessment as described above but this time will be correlated with pain assessment by the algorithm developed in the first stage of the study.

After consenting to participation patients will undergo an explanation on pain assessment using VAS and then will proceed with surgery, inclusion in the study will not affect anesthesia or surgery management in any way.

Study participation will take place in the PACU. Upon admission to the PACU unit, all study participants' facial expressions will be videoed by a camera placed in front of the patient's bed.

The facial expressions will be filmed in 30 second segments. A pain assessment will be measured immediately following filming of each segment using two modalities:

• Pain score assessed by an attending anesthesiologist assigned to the study team.
• VAS assessment by the patient.
• Three objective parameters: heart rate, blood pressure and respiratory rate will be recorded simultaneously from the automated record keeping system used in every patient in the PACU The quantity of segments filmed for each of the participants will be decided by the investigator taking into account participant's cooperation level and VAS levels.

In order to engineer an accurate predictive model the dataset will also include participants reporting a VAS of 0- experiencing no pain.

Data Management:

Following data collection, the data will be forwarded in a coded manner, according to Clalit's data security regulations, to Third Eye systems a facial recognition software company.

For first stage Third Eye systems will analyze and process the data using AI and machine learning models and develop an algorithm that can predict pain level by watching facial expressions.

After completion of the first stage, the second stage of the study will be done in the same manner as described above regarding patients enrollment. Pain assessment will be done by VAS and physician assessment as described above but this time will be correlated with pain assessment by the algorithm developed in the first stage of the study.

This feasibility study is pilot study to examine whether there is a positive correlation, on a relatively small sample size analysis, using simple resources and limited data to perform this study.

In the event that a positive hypothesis can be confirmed, a second stage observational study with a large sample size and an increased data source will be investigated.

Inclusion Criteria:

All patients above 18 presenting for an elective surgery at Beilinson Hospital following obtaining written informed consents form with the ability to comply with the study requirements will be included in our study.

Exclusion Criteria:

• Patients under the age of 18
• Patients unable to sign an informed consent
• Patients with a history of psychiatric disease.

patients above 18 presenting for an elective surgery at Beilinson Hospital