PDF(Pubmed)
Abstract：
Recent studies have posited that machine learning (ML) techniques accurately classify individuals with and without pain solely based on neuroimaging data. These studies claim that self-report is unreliable, making \"objective\" neuroimaging classification methods imperative. However, the relative performance of ML on neuroimaging and self-report data have not been compared. This study used commonly reported ML algorithms to measure differences between \"objective\" neuroimaging data and \"subjective\" self-report (ie, mood and pain intensity) in their ability to discriminate between individuals with and without chronic pain. Structural magnetic resonance imaging data from 26 individuals (14 individuals with fibromyalgia and 12 healthy controls) were processed to derive volumes from 56 brain regions per person. Self-report data included visual analog scale ratings for pain intensity and mood (ie, anger, anxiety, depression, frustration, and fear). Separate models representing brain volumes, mood ratings, and pain intensity ratings were estimated across several ML algorithms. Classification accuracy of brain volumes ranged from 53 to 76%, whereas mood and pain intensity ratings ranged from 79 to 96% and 83 to 96%, respectively. Overall, models derived from self-report data outperformed neuroimaging models by an average of 22%. Although neuroimaging clearly provides useful insights for understanding neural mechanisms underlying pain processing, self-report is reliable and accurate and continues to be clinically vital.
CONCLUSIONS: The present study compares neuroimaging, self-reported mood, and self-reported pain intensity data in their ability to classify individuals with and without fibromyalgia using ML algorithms. Overall, models derived from self-reported mood and pain intensity data outperformed structural neuroimaging models.
CONCLUSIONS: The present study compares neuroimaging, self-reported mood, and self-reported pain intensity data in their ability to classify individuals with and without fibromyalgia using ML algorithms. Overall, models derived from self-reported mood and pain intensity data outperformed structural neuroimaging models.
摘要：
最近的研究认为，机器学习（ML）技术仅根据神经影像学数据对有和没有疼痛的个体进行准确分类。这些研究声称自我报告是不可靠的，制定“客观”神经影像学分类方法势在必行。然而,ML在神经影像学和自我报告数据上的相对表现尚未进行比较.本研究使用通常报告的ML算法来测量“客观”神经影像学数据和“主观”自我报告之间的差异（即，情绪和疼痛强度）区分有和没有慢性疼痛的个体的能力。来自26个个体(14个患有纤维肌痛的个体和12个健康对照)的结构磁共振成像数据被处理以从每人56个大脑区域获得体积。自我报告数据包括疼痛强度和情绪的视觉模拟量表评分(即，愤怒,焦虑,抑郁症,挫败感,和恐惧）。代表大脑体积的独立模型，情绪评级，并对几种ML算法的疼痛强度等级进行了估计。脑容积的分类准确率在53%到76%之间,而情绪和疼痛强度等级从79％到96％和83％到96％，分别。总的来说,来自自我报告数据的模型平均优于神经成像模型22%.尽管神经成像清楚地提供了理解疼痛处理的神经机制的有用见解，自我报告是可靠和准确的，并继续在临床上至关重要。
结论：本研究比较了神经影像学，自我报告的情绪，和自我报告的疼痛强度数据，他们能够使用ML算法对有和没有纤维肌痛的个体进行分类。总的来说,从自我报告的情绪和疼痛强度数据得出的模型优于结构性神经成像模型.
结论：本研究比较了神经影像学，自我报告的情绪，和自我报告的疼痛强度数据，他们能够使用ML算法对有和没有纤维肌痛的个体进行分类。总的来说,从自我报告的情绪和疼痛强度数据得出的模型优于结构性神经成像模型.
