• S Walter, A Al-Hamadi, S Gruss, S Frisch, H C Traue, and P Werner.
• Sektion Medizinische Psychologie, Klinik für Psychosomatische Medizin und Psychotherapie, Universitätsklinikum Ulm, Frauensteige 6, 89075, Ulm, Deutschland. steffen.walter@uni-ulm.de.
• Schmerz. 2020 Oct 1; 34 (5): 400-409.

BackgroundThe objective recording of subjectively experienced pain is a problem that has not been sufficiently solved to date. In recent years, data sets have been created to train artificial intelligence algorithms to recognize patterns of pain intensity. The multimodal recognition of pain with machine learning could provide a way to reduce an over- or undersupply of analgesics, explicitly in patients with limited communication skills.ObjectivesThis study investigated the methodology of automated multimodal recognition of pain intensity and modality using machine-learning techniques of artificial intelligence. Multimodal recognition rates of experimentally induced phasic electrical and heat pain stimuli were compared with uni- and bimodal recognition rates.Material And MethodsOn the basis of the X‑ITE Pain Database, healthy subjects were stimulated with phasic electro-induced pain and heat pain, and their corresponding pain responses were recorded with multimodal sensors (acoustic, video-based, physiological). After complex signal processing, machine-learning methods were used to calculate recognition rates with respect to pain intensity (baseline vs. pain threshold, pain tolerance, mean value of pain threshold and tolerance) and pain modality (electrical vs. heat). Finally, a statistical comparison of uni- vs. multimodal and bi- vs. multimodal detection rates was performed.ResultsWith few exceptions, multimodal recognition of pain intensity rates was statistically superior to unimodal recognition rates, regardless of the pain modality. Multimodal pain recognition distinguished significantly better between heat and electro-induced pain. Further, multimodal recognition rates were predominantly superior to bimodal recognition rates.ConclusionPriority should be given to the multimodal approach to the recognition of pain intensity and modality compared with unimodality. Further clinical studies should clarify whether multimodal automated recognition of pain intensity and modality is in fact superior to bimodal recognition.

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