Assessment of Pain Intensity Using Deep Learning Models in Non-Communicative Intensive Care Patients

Pain is a multifaceted and subjective phenomenon frequently experienced by patients in intensive care units. In non-communicating populations, conventional assessment tools are often inadequate and susceptible to observer bias. Deep learning-based facial analysis has emerged as a promising approach for the objective quantification of observable pain-related behavioural indicators.

To evaluate the feasibility and diagnostic accuracy of deep-learning models in categorising pain severity in non-communicative adult intensive care patients, using expert-annotated facial images.

Features were extracted via the DenseNet-169 architecture, dimensionally reduced with principal component analysis and classified using support vector machine, random forest and K-nearest neighbours. Data sets were independently annotated by a multidisciplinary team comprising an intensivist, intensive care nurses and a pain specialist. Model performance was comprehensively assessed through accuracy, precision, sensitivity, the F1 score, the area under the receiver operating characteristic curve and Fleiss' kappa coefficient to ensure robust inter-rater reliability.

A total of 636 facial images obtained from 120 adult intensive care unit patients were analysed. The support vector machine model achieved the highest overall performance, with an accuracy of 96.9% and an area under the receiver operating characteristic curve of 0.994, demonstrating exceptional sensitivity in severe pain classification. While K-nearest neighbours showed superior performance for moderate pain detection, random forest yielded the lowest accuracy across all data sets. Notably, inter-rater agreement was low (k = 0.16), highlighting the significant variability in expert human judgements and the subjective nature of manual pain assessment.

Deep learning-based facial analysis provides a valid, reproducible and standardised method for pain assessment in non-verbal intensive care patients. The creation of a multi-expert annotated data set and the systematic comparison of classifiers across diverse clinical perspectives represent the original contributions of this study.

Automated facial expression analysis minimises inter-observer variability by providing an objective decision support mechanism for critical care nurses. This technology facilitates the standardisation of pain management protocols and bolsters patient safety by reducing the inherent risks of subjective assessment bias.