[From external assessment of pain to automated multimodal measurement of pain intensity : Narrative review of state of research and clinical perspectives]. / Von der Fremdbeurteilung des Schmerzes zur automatisierten multimodalen Messung der Schmerzintensität : Narrativer Review zum Stand der Forschung und zur klinischen Perspektive.

BACKGROUND: In patients with limited communication skills, the use of conventional scales or external assessment is only possible to a limited extent or not at all. Multimodal pain recognition based on artificial intelligence (AI) algorithms could be a solution. OBJECTIVE: Overview of the methods of automated multimodal pain measurement and their recognition rates that were calculated with AI algorithms. METHODS: In April 2018, 101 studies on automated pain recognition were found in the Web of Science database to illustrate the current state of research. A selective literature review with special consideration of recognition rates of automated multimodal pain measurement yielded 14 studies, which are the focus of this review. RESULTS: The variance in recognition rates was 52.9-55.0% (pain threshold) and 66.8-85.7%; in nine studies the recognition rate was ≥80% (pain tolerance), while one study reported recognition rates of 79.3% (pain threshold) and 90.9% (pain tolerance). CONCLUSION: Pain is generally recorded multimodally, based on external observation scales. With regard to automated pain recognition and on the basis of the 14 selected studies, there is to date no conclusive evidence that multimodal automated pain recognition is superior to unimodal pain recognition. In the clinical context, multimodal pain recognition could be advantageous, because this approach is more flexible. In the case of one modality not being available, e.g., electrodermal activity in hand burns, the algorithm could use other modalities (video) and thus compensate for missing information.

[Multimodal recognition of pain intensity and pain modality with machine learning]. / Multimodale Erkennung von Schmerzintensität und -modalität mit maschinellen Lernverfahren.

BACKGROUND: The 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. OBJECTIVES: This 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 METHODS: On the basis of the X­ITE 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. RESULTS: With 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. CONCLUSION: Priority 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.

[Mimic activity of differentiated pain intensities : Correlation of characteristics of Facial Action Coding System and electromyography]. / Mimische Aktivität differenzierter Schmerzintensitäten : Korrelation der Merkmale von Facial Action Coding System und Elektromyographie.

BACKGROUND: The monitoring of facial expressions to assess pain intensity provides a way to determine the need for pain medication in patients who are not able to do so verbally. OBJECTIVES: In this study two methods for facial expression analysis - Facial Action Coding System (FACS) and electromyography (EMG) of the zygomaticus muscle and corrugator supercilii - were compared to verify the possibility of using EMG for pain monitoring. MATERIAL AND METHODS: Eighty-seven subjects received painful heat stimuli via a thermode on the right forearm in two identical experimental sequences - with and without EMG recording. RESULTS: With FACS, pain threshold and pain tolerance could be distinguished reliably. Multiple regression analyses indicated that some facial expressions had a predictive value. Correlations between FACS and pain intensity and EMG and pain intensity were high, indicating a closer relationship for EMG and increasing pain intensity. For EMG and FACS, a low correlation was observed, whereas EMG correlates much better with pain intensity. CONCLUSIONS: Results show that the facial expression analysis based on FACS represents a credible method to detect pain. Because of the expenditure of time and personal costs, FACS cannot be used properly until automatic systems work accurately. The use of EMG seems to be helpful in the meantime to enable continuous pain monitoring for patients with acute post-operative pain.

Fluorescence enhancement of coumarin-6-sulfonyl chloride amino acid derivatives in cyclodextrin media.

Coumarin-6-sulfonyl (6-CS) amino acid derivatives form inclusion complexes with a- and /-cyclodextrins (CD) in aqueous solution. The stoichiometry of the inclusion complex and the equilibrium constant were investigated. Using a fluorescence technique and alanine-beta-CD as a model, a 1:2 guest-host complex was established, and K = 4.7 x 10(5) mol(-2) l(2) was obtained. Fluorescence enhancement was observed for all derivatives studied, with glycine exhibiting a greater enhancement, and tyrosine showing the least. The stability of the inclusion complex was found to depend on the respective sizes of the guest-host complex and their interaction.

Standards for growth in prepubertal primary school Kuwaiti children, a cross-sectional study.

In a cross-sectional study, anthropometric measurements were made in Kuwaiti primary school children aged six to nine years. The sample included 6765 children, of whom there were 3534 boys and 3231 girls. A minimum of 400 children were included in each of the 16 age-sex groups studied. Anthropometric data are presented as percentiles for weight-for-age, height-for-age, and weight-for-height. The results were smoothed and figures were constructed for the three growth standards. A comparison between locally constructed standards and a Western reference growth standard (Tanner et al.) revealed marked similarities in attainable growth. We think that the Western reference standards are suitable for use in Kuwait and probably in other similar developing countries, and the setting of lower targets for those countries is not recommended.