Artificial intelligence to evaluate postoperative pain based on facial expression recognition

Abstract

Funding informationThis work has been funded equally by the Centre Hospitalier Universitaire de Nice (CHUN) and Orange SA. The CHUN designed and sponsored the study, performed the inclusions and data collection, interpreted the analysis, wrote the report and decided to submit it. Orange SA was in charge of the image processing, image data analysis, deep learning system development and statistical analysis.

Exploring the Role of Artificial Intelligence in Gauging Pain Severity caused by Local Dental Anesthetic Injections in Children: a cross-sectional study

“…CNNs can be trained to identify patterns and features in images that may not be visible to human raters, potentially improving the accuracy and reliability of pain assessment [39]. Furthermore, CNNs can process large amounts of data quickly, making them a potentially useful tool in busy clinical settings [40].…”

Using AI to Detect Pain through Facial Expressions: A Review

“…The principal outcomes differed among studies. For instance, one study focused only on the detection of pain [35], eight studies only on the estimation of multilevel pain intensity [36,37,39,41,42,44,45,48], and four studied both the detection of pain and the assessment of multilevel pain intensity [40,46,47,49]. Additionally, two studies proposed their automated detection model to differentiate between genuine and faked facial expressions of pain [38,43].…”

“…Four studies applied their automated pain detection systems to videos from their recruited patients [36,38,43,49], and eleven used them on at least one public database of pre-recorded patients experiencing pain [35,37,[39][40][41][42][44][45][46][47][48].…”

“…Of the four studies that recruited patients for AI/ML model assessment, one consisted of 1189 patients undergoing different surgeries in a single healthcare center [36]. In addition, two studies assessed pain induced through cold pressor methods in 26 healthy university students [43] and healthy volunteers [38].…”

Artificial intelligence and machine learning in pain research: a data scientometric analysis

“…Another application of ML is the recognition of pain-related facial expressions. 20 Pain intensity was assessed in 1,189 adult patients undergoing surgery, and 2,971 photographs of facial expressions were included, most of which (44%) were taken when patients were not in pain, while only 13.5% of photographs were taken when patients were in severe pain of ≥7 on an 11-point Numerical Rating Scale. After splitting the data set into training/testing/validation subsets, a convolutional neural network was trained to predict pain intensity based on facial expression.…”