Background: Chronic pain affects a significant proportion of the population in the United States and poses a significant public health concern. Beyond physical discomfort, chronic pain has been increasingly linked to cognitive dysfunction, including impairments in memory, attention, executive function, and decision-making. The relationship is particularly pronounced in older adults and may contribute to the onset or progression of neurodegenerative diseases. Objective: This comprehensive review explores the relationship between chronic pain and cognitive function, emphasizing the underlying neurobiological mechanisms, structural brain changes, and associated comorbidities. Methods: A review was conducted using peer-reviewed studies that began with the earliest pain and cognition mechanisms, followed by further investigation of cognitive effects of chronic pain, neuroimaging findings, and comorbid neuropsychiatric and neurodegenerative conditions. Sources included large-scale cohort studies, functional MRI analyses, and neurobiological investigations focusing on prefrontal cortex activity, default mode network alterations, and gray matter atrophy. Results: Chronic pain is associated with cognitive deficits through multiple converging pathways. It contributes to measurable impairments in cognitive function and is linked to structural and functional brain alterations. Regions of interest include the dorsolateral prefrontal cortex, medial prefrontal cortex, and default mode network, which can be connected to the neural resource hypothesis because of their cognitive domain impairments. A better understanding of these mechanisms highlights the importance of early recognition and multidisciplinary management strategies, including neuromodulation and cognitive rehabilitation. Future research should prioritize longitudinal studies and integrated interventions targeting both pain and cognitive health.

In patients where conservative approaches have failed to relieve from chronic pain, interventional procedures may be an option in well selected patients. In recent years there has been an increase in the use and development of invasive procedures. Concomitantly, there has also been an increase in the complications associated with these procedures. Taken this into consideration, it is important for healthcare providers to take a cautious and vigilant approach, with a focus on patient safety, in order to minimize the risk of adverse events and ensure the best possible outcome for the patient. This may include careful selection of patients for procedures, use of proper techniques and equipment, and close monitoring and follow-up after the procedure. The aim of this narrative review is to summarize the primary complications associated with commonly performed image-guided (fluoroscopy or ultrasound-guided) interventional procedures and provide strategies to reduce the risk of these complications. We conclude that although complications from interventional pain procedures can be mitigated to a certain degree, they cannot be eliminated altogether. In order to avoid adverse events, patient safety should be given considerable attention and physicians should be constantly aware of the possibility of developing complications.

Introduction: Effective pain management is crucial for patient care, impacting comfort, recovery, and overall well-being. Traditional subjective pain assessment methods can be challenging, particularly in specific patient populations. This research explores an alternative approach using computer vision (CV) to detect pain through facial expressions. Methods: The study implements the YOLOv8 real-time object detection model to analyze facial expressions indicative of pain. Given four pain datasets, a dataset of pain-expressing faces was compiled, and each image was carefully labeled based on the presence of pain-associated Action Units (AUs). The labeling distinguished between two classes: pain and no pain. The pain category included specific AUs (AU4, AU6, AU7, AU9, AU10, and AU43) following the Prkachin and Solomon Pain Intensity (PSPI) scoring method. Images showing these AUs with a PSPI score above 2 were labeled as expressing pain. The manual labeling process utilized an open-source tool, makesense.ai, to ensure precise annotation. The dataset was then split into training and testing subsets, each containing a mix of pain and no-pain images. The YOLOv8 model underwent iterative training over 10 epochs. The model’s performance was validated using precision, recall, and mean Average Precision (mAP) metrics, and F1 score. Results: When considering all classes collectively, our model attained a mAP of 0.893 at a threshold of 0.5. The precision for “pain” and “nopain” detection was 0.868 and 0.919, respectively. F1 scores for the classes “pain”, “nopain”, and “all classes” reached a peak value of 0.80. Finally, the model was tested on the Delaware dataset and in a real-world scenario. Discussion: Despite limitations, this study highlights the promise of using real-time computer vision models for pain detection, with potential applications in clinical settings. Future research will focus on evaluating the model’s generalizability across diverse clinical scenarios and its integration into clinical workflows to improve patient care.

Background: Although spinal cord stimulation (SCS) is an effective treatment for managing chronic pain, many patients have understandable questions and concerns regarding this therapy. Artificial intelligence (AI) has shown promise in delivering patient education in healthcare. This study evaluates the reliability, accuracy, and comprehensibility of ChatGPT’s responses to common patient inquiries about SCS. Methods: Thirteen commonly asked questions regarding SCS were selected based on the authors’ clinical experience managing chronic pain patients and a targeted review of patient education materials and relevant medical literature. The questions were prioritized based on their frequency in patient consultations, relevance to decision-making about SCS, and the complexity of the information typically required to comprehensively address the questions. These questions spanned three domains: pre-procedural, intra-procedural, and post-procedural concerns. Responses were generated using GPT-4.0 with the prompt “If you were a physician, how would you answer a patient asking…”. Responses were independently assessed by 10 pain physicians and two non-healthcare professionals using a Likert scale for reliability (1–6 points), accuracy (1–3 points), and comprehensibility (1–3 points). Results: ChatGPT’s responses demonstrated strong reliability (5.1 ± 0.7) and comprehensibility (2.8 ± 0.2), with 92% and 98% of responses, respectively, meeting or exceeding our predefined thresholds. Accuracy was 2.7 ± 0.3, with 95% of responses rated sufficiently accurate. General queries, such as “What is spinal cord stimulation?” and “What are the risks and benefits?”, received higher scores compared to technical questions like “What are the different types of waveforms used in SCS?”. Conclusions: ChatGPT can be implemented as a supplementary tool for patient education, particularly in addressing general and procedural queries about SCS. However, the AI’s performance was less robust in addressing highly technical or nuanced questions.

Background: Chronic knee osteoarthritic (OA) pain is a common and debilitating complaint in elderly patients. Despite numerous pharmaceutical options, the majority of patients still experience long-term pain. Genicular nerve (GN) radiofrequency has become increasingly popular as a treatment for knee pain. This retrospective study aimed to evaluate the effects of pulse dose radiofrequency (PDRF) in patients with chronic knee OA pain. Patients and Methods: Propensity score matching analysis was performed in a retrospective cohort of 78 patients with moderate-severe knee OA pain unresponsive to conservative treatment who underwent PDRF GN or intra-articular (IA) and PDRF GN. Pain relief was measured using the numeric rating scale (NRS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Patient Global Impression of Change (PGIC) at 3 and 6 months post-intervention. Results: A significant reduction in NRS scores was reported at 3 (p< 0.001) and 6 months (p< 0.001) after PDRF in both groups. NRS was lower in PDRF IA + GN than PDRF GN (p< 0.0001). WOMAC pain was significantly reduced at 3 months in PDRF IA + GN group (baseline: 10.12± 3.14, 3 months: 6.25± 2.44, p=0.0001). WOMAC stiffness and function were improved only at 3 months in PDRF IA + GN compared to baseline (p=0.007 and p=0.006, respectively). A longer period of pain relief was reported after PDRF IA + GN (6.75± 2.42 months) compared to PDRF GN (4.31± 2.85 months, p< 0.001) in association with higher PGIC scores. Conclusion: This is the first study that compared two different PDRF techniques. PDRF GN and PDRF IA + GN were both effective in reducing pain at 3 and 6 months follow-up. However, only PDRF IA + GN was able to improve WOMAC scores at 3 months after the treatment with a longer period of efficacy compared to PDRF GN alone.