病人是否感到疼痛，醫(yī)生只能通過詢問才能知道,。但對那些“疼也說不出口”的特殊病人，詢問并不能解決問題。美國研究人員最新開發(fā)出一種方法,，利用電腦便能檢測出病人是否有疼痛感。
斯坦福大學醫(yī)學院的研究人員報告說,，他們利用電腦整理出人在感覺到疼痛時的大腦掃描圖,，讓電腦“記住”這些圖像的特征，從而通過這種神經成像技術檢測人的疼痛感,。
在試驗中,，研究人員讓8名志愿者先后接觸較熱和滾燙的物體，并在他們接觸這兩類物體時分別進行大腦掃描,，隨后讓電腦通過一種基于統(tǒng)計學習理論的模式識別方法來給大腦活動模式進行分類,，從而確定志愿者是否正被疼痛折磨。結果顯示，電腦測定志愿者疼痛感的準確率達80%以上,。
參與這項研究的肖恩·麥基博士說,，目前醫(yī)生只能通過詢問病人才知道他們是否感到疼痛，但過于年幼或年老的病人,，以及患癡呆癥,、失去意識的病人，往往不能給出準確的答案,。
為此,，醫(yī)學界一直在努力開發(fā)疼痛檢測儀器，新技術有望最終解決疼痛檢測問題,，為治療慢性疼痛疾病提供幫助,。
新研究成果發(fā)表在新一期美國《科學公共圖書館—綜合》。（生物谷 Bioon.com）
doi:10.1371/journal.pone.0024124
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Towards a Physiology-Based Measure of Pain: Patterns of Human Brain Activity Distinguish Painful from Non-Painful Thermal Stimulation
Justin E. Brown, Neil Chatterjee, Jarred Younger, Sean Mackey
Pain often exists in the absence of observable injury; therefore, the gold standard for pain assessment has long been self-report. Because the inability to verbally communicate can prevent effective pain management, research efforts have focused on the development of a tool that accurately assesses pain without depending on self-report. Those previous efforts have not proven successful at substituting self-report with a clinically valid, physiology-based measure of pain. Recent neuroimaging data suggest that functional magnetic resonance imaging (fMRI) and support vector machine (SVM) learning can be jointly used to accurately assess cognitive states. Therefore, we hypothesized that an SVM trained on fMRI data can assess pain in the absence of self-report. In fMRI experiments, 24 individuals were presented painful and nonpainful thermal stimuli. Using eight individuals, we trained a linear SVM to distinguish these stimuli using whole-brain patterns of activity. We assessed the performance of this trained SVM model by testing it on 16 individuals whose data were not used for training. The whole-brain SVM was 81% accurate at distinguishing painful from non-painful stimuli (p<0.0000001). Using distance from the SVM hyperplane as a confidence measure, accuracy was further increased to 84%, albeit at the expense of excluding 15% of the stimuli that were the most difficult to classify. Overall performance of the SVM was primarily affected by activity in pain-processing regions of the brain including the primary somatosensory cortex, secondary somatosensory cortex, insular cortex, primary motor cortex, and cingulate cortex. Region of interest (ROI) analyses revealed that whole-brain patterns of activity led to more accurate classification than localized activity from individual brain regions. Our findings demonstrate that fMRI with SVM learning can assess pain without requiring any communication from the person being tested. We outline tasks that should be completed to advance this approach toward use in clinical settings.
