[From external assessment of pain to automated multimodal measurement of pain intensity : Narrative review of state of research and clinical perspectives]

Deep learning for video-based automated pain recognition in rabbits

Despite the wide range of uses of rabbits (Oryctolagus cuniculus) as experimental models for pain, as well as their increasing popularity as pets, pain assessment in rabbits is understudied. This study is the first to address automated detection of acute postoperative pain in rabbits. Using a dataset of video footage of n = 28 rabbits before (no pain) and after surgery (pain), we present an AI model for pain recognition using both the facial area and the body posture and reaching accuracy of above 87%. We apply a combination of 1 sec interval sampling with the Grayscale Short-Term stacking (GrayST) to incorporate temporal information for video classification at frame level and a frame selection technique to better exploit the availability of video data.

The impact of analgesic on EMG and other biosignals in a postoperative setting

Background

In the clinical context, the assessment of pain in patients with inadequate communication skills is standardly performed externally by trained medical staff. Automated pain recognition (APR) could make a significant contribution here. Hereby, pain responses are captured using mainly video cams and biosignal sensors. Primary, the automated monitoring of pain during the onset of analgesic sedation has the highest relevance in intensive care medicine. In this context, facial electromyography (EMG) represents an alternative to recording facial expressions via video in terms of data security. In the present study, specific physiological signals were analyzed to determine, whether a distinction can be made between pre-and post-analgesic administration in a postoperative setting. Explicitly, the significance of the facial EMG regarding the operationalization of the effect of analgesia was tested.

Methods

N = 38 patients scheduled for surgical intervention where prospectively recruited. After the procedure the patients were transferred to intermediate care. Biosignals were recorded and all doses of analgesic sedations were carefully documented until they were transferred back to the general ward.

Results

Almost every biosignal feature is able to distinguish significantly between ‘before’ and ‘after’ pain medication. We found the highest effect sizes (r = 0.56) for the facial EMG.

Conclusion

The results of the present study, findings from research based on the BioVid and X-ITE pain datasets, staff and patient acceptance indicate that it would now be appropriate to develop an APR prototype.

"What About Automated Pain Recognition for Routine Clinical Use?" A Survey of Physicians and Nursing Staff on Expectations, Requirements, and Acceptance

Background: Over the last 12 years, the fundamentals of automated pain recognition using artificial intelligence (AI) algorithms have been investigated and optimized. The main target groups are patients with limited communicative abilities. To date, the extent to which anesthetists and nurses in intensive care units would benefit from an automated pain recognition system has not been investigated.

Methods:N = 102 clinical employees were interviewed. To this end, they were shown a video in which the visionary technology of automated pain recognition, its basis and goals are outlined. Subsequently, questions were asked about: (1) the potential benefit of an automated pain recognition in clinical context, (2) preferences with regard to the modality used (physiological, paralinguistic, video-based, multimodal), (3) the maximum willingness to invest, (4) preferences concerning the required pain recognition rate and finally (5) willingness to use automated pain recognition.

Results: The respondents expect the greatest benefit from an automated pain recognition system to be “to avoid over- or undersupply of analgesics in patients with limited communicative abilities,” a total of 50% of respondents indicated that they would use automated pain recognition technology, 32.4% replied with “perhaps” and 17.4% would not use it.

Conclusion: Automated pain recognition is, in principle, accepted by anesthetists and nursing staff as a possible new method, with expected benefits for patients with limited communicative skills. However, studies on automated pain recognition in a clinical environment and proof of its acceptance and practicability are absolutely necessary before such systems can be implemented.