{
  "abstract": "Background and Aims Combat-related chronic pain presents a significant challenge in both military and veteran populations, often leading to reduced operational readiness and quality of life. Traditional pain assessment tools can be time-consuming, subjective, and limited in capturing multidimensional pain patterns. The Rapid Pain Assessment Tool (R-PAT), enhanced with artificial intelligence (AI) support, was developed to provide a fast, consistent, and objective evaluation of chronic pain in combat-exposed individuals.Methods A prospective observational study was conducted involving 44 patients with combat-related chronic pain. Participants underwent evaluation using the R-PAT, which incorporates a brief digital questionnaire and physiological metrics (e.g., heart rate variability, facial expression analysis via computer vision, and speech pattern analysis) ( figure 1). An AI model trained on previous chronic pain datasets was used to classify pain intensity and type. Standard pain scores (Numeric Rating Scale – NRS) were used as a comparator. Correlations between AI-predicted pain levels and NRS were analyzed using Pearson correlation coefficients. Model accuracy, sensitivity, and specificity were also calculated.Results The AI-supported R-PAT demonstrated a strong positive correlation with patient-reported NRS scores (r = 0.84, p < 0.001). The AI model achieved an overall accuracy of 88.6%, with a sensitivity of 91.2% and specificity of 85.3% in identifying moderate to severe pain. Average time to complete the assessment was 2.7 ± 0.9 minutes, significantly faster than traditional methods (p < 0.01). Patients reported high satisfaction with the tool (mean score 4.5/5). Subgroup analysis indicated higher accuracy among patients with musculoskeletal and neuropathic pain patterns.Abstract OP27 Figure 1Basic view of the software toolConclusions The R-PAT with AI support offers a rapid, accurate, and user-friendly solution for assessing combat-related chronic pain. Its integration of physiological data and machine learning provides an objective complement to traditional scales, enhancing clinical decision-making and potentially guiding more personalized pain management strategies in military healthcare settings. Further large-scale validation is warranted.",
  "authors": [
    {
      "affiliations": [
        "Podilskiy Regional Center of Oncology, Vinnitsa, Ukraine"
      ],
      "name": "Dmytro Dmytriiev"
    },
    {
      "affiliations": [
        "Vinnitsya Regional Clinical Hospital n.a Pirogov, Vinnitsya, Ukraine"
      ],
      "name": "Andrii Popelnukha"
    }
  ],
  "title": "OP27 Rapid pain assessment tool (R-PAT) with AI support for combat-related chronic pain",
  "uid": "897cc77f-e8f5-5945-875f-46b7514774d6"
}