Search

Pain diagnosis remains a challenging task due to its subjective nature, the variability in pain expression among individuals, and the difficult assessment of the underlying biopsychosocial factors. In this complex scenario, artificial intelligence (AI) can offer the potential to enhance diagnostic accuracy, predict treatment outcomes, and personalize pain management strategies. This review aims to dissect the current literature on computer-aided diagnosis methods. It also discusses how AI-driven diagnostic strategies can be integrated into multimodal models that combine various data sources, such as facial expression analysis, neuroimaging, and physiological signals, with advanced AI techniques. Despite the significant advancements in AI technology, its widespread adoption in clinical settings faces crucial challenges. The main issues are ethical considerations related to patient privacy, biases, and the lack of reliability and generalizability. Furthermore, there is a need for high-quality real-world validation and the development of standardized protocols and policies to guide the implementation of these technologies in diverse clinical settings. [ABSTRACT FROM AUTHOR]

Competing Interests: Conflict of interest: The authors certify that they have no affiliation with, or involvement in any organization or entity with any financial or non-financial interest in the subject matter discussed in this manuscript.

Background: Spinal cord stimulation (SCS) is a well-known treatment in patients with failed back surgery syndrome (FBSS). Burst stimulation is a recently developed stimulation modality that seems to be superior to tonic stimulation., Methods: This observational multicenter study compared tonic and burst stimulation during a trial period in patients with FBSS or radiculopathy. All the patients enrolled underwent two weeks of tonic stimulation followed by another two weeks of BurstDR stimulation, without randomization. The primary outcome was the reduction of pain in the legs and back. Health-related quality of life (EQ-5D) and the pain catastrophizing scale (PCS) were assessed before and after the trial. Patients were reevaluated after 12 months., Results: We recruited 23 patients, 57% of whom had FBSS and 43% had radiculopathies. Five patients failed both the tonic and burst stimulation trials. While tonic stimulation reduced leg pain (p < 0.05), the burst mode added an extra pain reduction (ΔNRS 1.2 ± 1.5) (p < 0.01). No significant reduction in back pain was found (p 0.29). Pain on movement was reduced only by BurstDR (p < 0.01). Both stimulation modalities increased EQ-5D and reduced PCS from the baseline (p < 0.0001). At the end of the SCS trial phase, 26% patients chose tonic SCS, while 74% preferred burst. On 12-month follow-up examination, the benefits recorded at the end of the trial were maintained., Conclusions: Burst stimulation confers a greater reduction in leg pain intensity at rest and on movement. Reducing axial pain is still a challenge. Further studies are needed in order to provide each patient with the most appropriate stimulation paradigm., (© 2018 International Neuromodulation Society.)