Reliability, Accuracy, and Comprehensibility of AI-Based Responses to Common Patient Questions Regarding Spinal Cord Stimulation

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(5), P. 1453 - 1453

Published: Feb. 21, 2025

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

Language: Английский

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Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: March 18, 2025

Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain challenge due to lack of objective pain biomarkers. The present study applies machine learning predict which patients will respond SCS based on intraoperative electroencephalogram (EEG) data and recognized outcome measures. included 20 who were undergoing surgery. During monitoring, EEG signals recorded under OFF (baseline) ON conditions, including tonic high density (HD) stimulation. Once spectral features extracted during offline analysis, principal component analysis (PCA) recursive feature elimination approach used selection. A subset features, clinical characteristics the preoperative patient reported measures (PROMs) build predictive model. Responders nonresponders grouped 50% reduction in 3-month postoperative Numeric Rating Scale (NRS) scores. two groups had no statistically significant differences with respect demographics (including age, diagnosis, location) or PROMs, except NRS (worst pain: p = 0.028; average < 0.001) Oswestry Disability Index scores (ODI, 0.030). Alpha-theta peak power ratio differed significantly between CP3-CP4 T3-T4 (p 0.019), lowest activity decision tree model performed best, achieving 88.2% accuracy, an F1 score 0.857, area curve (AUC) receiver operating characteristic (ROC) 0.879. Our findings suggest that combination subjective self-reports, intraoperatively obtained EEGs, well-designed algorithms might be potentially distinguish nonresponders. Machine deep hold enormous potential responses resulting refined selection improved outcomes.

Language: Английский

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Should we Oppose or Combine Waveforms for Spinal Cord Stimulation in PSPS-T2 Patients? A Prospective Randomized Crossover Trial (MULTIWAVE Study)

Journal of Pain, Journal Year: 2023, Volume and Issue: 24(12), P. 2319 - 2339

Published: July 18, 2023

Refractory persistent spinal pain syndrome after surgery (PSPS-T2) can be successfully addressed by cord stimulation (SCS). While conventional generates paresthesia, recent systems enable the delivery of paresthesia-free stimulation. Studies have claimed non-inferiority/superiority selected compared with paresthesia-based stimulation, but comparative efficacy between different waveforms still needs to determined in a given patient. We designed randomized controlled 3-month crossover trial compare relief versus high frequency burst 28 PSPS-T2 patients implanted multiwave SCS systems. Our secondary objectives were determine these 3 on surface, quality life, functional capacity, psychological distress, and validated composite multidimensional clinical response index provide holistic comparisons at 3-, 6-, 9-, 15-month post-randomization. The preferred modality was documented during follow-up periods. No difference observed this study (P = .08). led significant relief, life improvement, improvement index, all other outcomes visits. Forty-four percent chose keep period. By giving possibility switch and/or combine several waveforms, overall rate responders further increased 25%. In study, or do not appear superior wherefore should considered as valid option. However, combining through personalized therapy, might significantly improve responses. PERSPECTIVE: This article assesses comparing (including burst) modalities patient presenting PSPS-T2. Switching contribute increasing global rate.

Language: Английский

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Development and Implementation of Automated Referral Triaging System for Spinal Cord Stimulation Procedure in Pain Medicine

Journal of Medical Systems, Journal Year: 2025, Volume and Issue: 49(1)

Published: Jan. 21, 2025

Language: Английский

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Treatment modalities for patients with Persistent Spinal Pain Syndrome Type II: A systematic review and network meta-analysis

Communications Medicine, Journal Year: 2025, Volume and Issue: 5(1)

Published: March 5, 2025

Appropriate management of patients with Persistent Spinal Pain Syndrome Type 2 (PSPS-T2) remains challenging. The need for robust evidence treatment modalities is urgently pressing. aim this systematic review and network meta-analysis (NMA) to compare different PSPS-T2 on pain intensity. study protocol was prospectively registered (PROSPERO;CRD42022360160). Four databases were consulted from database inception December 18th, 2023. Randomised controlled trials interventions included. revised Cochrane Risk Bias Tool used assess risk bias. A NMA standardized mean differences calculated pairwise comparisons between all modalities. Here we include 49 studies in the 13 NMA. high bias indicated 65.3% studies. Half investigate neuromodulation (mainly Cord Stimulation), 16 explore minimal invasive options (predominantly epidural injections), 6 focus conservative treatments (physiotherapy/cognitive training medication) reoperation. Comparison versus a combination results an effect size 0.45 (95% CI: 0.14-0.76), clearly favouring (z = 2.88; p 0.004). Additionally, standardised difference 0.36 0.18-0.53) compared placebo/sham 4.03; < 0.0001). No statistically significant found neuromodulation. Neuromodulation, followed by options, seems be most effective option obtain relief PSPS-T2. Nevertheless, personalized approach tailored individual patient needs essential optimizing outcomes, since should adjusted based failure or success prior therapies.

Language: Английский

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Multifidus Fat Infiltration in Patients with Persistent Spinal Pain Syndrome Type II Treated with Spinal Cord Stimulation: A Preliminary Report

Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(9), P. 3124 - 3124

Published: April 30, 2025

Background/Objectives: Functional spinal instability from multifidus dysfunction has been proposed as a mechanism for chronic postsurgical pain. Prior studies reported structural impairments in the lumbar patients with low back pain, including reduced cross-sectional area, muscle thickness, and increased fat infiltration. This preliminary report examined prevalence of infiltration after Spinal Cord Stimulation (SCS), an established pain management technique. It also assessed inter-rater reliability evaluating using MRI. Methods: The medical imaging data four Persistent Pain Syndrome Type II (PSPS II) treated SCS were collected. Two independent operators performed manual segmentation on axial MRI images spine. fat-to-muscle ratio was quantified rated four-point classification system, categorizing normal, mild, moderate, or severe. To assess segmentations, determined. Results: median at levels L2–L3 46.12 (Q1–Q3: 44.88–47.35). At L3–L4, L4–L5, L5–S1, values 50.45 45.57–52.98), 52.11 48.81–52.80), 52.84 49.09–56.39), respectively. An ICC value one (95% CI 0.999 to 1, p < 0.001) found agreement volume muscle. Conclusions: All had moderate-to-severe each level. Although time-consuming, feasible yielded excellent when determining volume. Future endeavors should focus automation classification.

Language: Английский

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Cell Therapy for Spinal Cord Injury

Published: Jan. 1, 2025

Language: Английский

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Spinal Cord Stimulation Guidelines and Consensus Statements: Systematic Review and Appraisal of Guidelines for Research and Evaluation II Assessment

Neuromodulation Technology at the Neural Interface, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

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The Application of Artificial Intelligence to Enhance Spinal Cord Stimulation Efficacy for Chronic Pain Management: Current Evidence and Future Directions

Current Pain and Headache Reports, Journal Year: 2025, Volume and Issue: 29(1)

Published: May 20, 2025

Language: Английский

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Objective wearable measures correlate with self-reported chronic pain levels in people with spinal cord stimulation systems

npj Digital Medicine, Journal Year: 2023, Volume and Issue: 6(1)

Published: Aug. 15, 2023

Spinal Cord Stimulation (SCS) is a well-established therapy for treating chronic pain. However, perceived treatment response to SCS may vary among people with pain due diverse needs and backgrounds. Patient Reported Outcomes (PROs) from standard survey questions do not provide the full picture of what has happened patient since their last visit, digital PROs require patients visit an app or otherwise regularly engage software. This study aims assess feasibility using biomarkers collected wearables during predict PRO outcomes. Twenty participants were recruited implanted SCS. During six months study, activity physiological metrics data 15 was used develop machine learning pipeline objectively levels categories measures. The model reached accuracy 0.768 ± 0.012 in predicting intensity mild, moderate, severe. Feature importance analysis showed that smartwatch such as heart rate, rate variability, step count, stand time can contribute modeling different aspects results suggest wearable be outcomes pain, enabling continuous, real-time monitoring use therapies.

Language: Английский

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