{"title":"Deep learning modelling of structural brain MRI in chronic head and neck pain after mild TBI.","authors":"Sivan Attias, Roni Ramon-Gonen, Yaara Erez, Noam Bosak, Yelena Granovsky, Shahar Shelly","doi":"10.1097/j.pain.0000000000003587","DOIUrl":null,"url":null,"abstract":"<p><strong>Abstract: </strong>Chronic headache is a common complication after mild traumatic brain injury (mTBI), which affects close to 70 million individuals annually worldwide. This study aims to test the utility of a unique, early predictive magnetic resonance imaging (MRI)-based classification model using structural brain MRI scans, a rarely used approach to identify high-risk individuals for post-mTBI chronic pain. We recruited 227 patients with mTBI after a vehicle collision, between March 30, 2016 and December 30, 2019. T1-weighted brain MRI scans from 128 patients within 72 hours postinjury were included and served as input for a pretrained 3D ResNet-18 deep learning model. All patients had initial assessments within the first 72 hours after the injury and performed follow-ups for 1 year. Chronic pain was reported in 43% at 12 months postinjury; remaining 57% were assigned to the recovery group. The best results were achieved for the axial plane with an average accuracy of 0.59 and an average area under the curve (AUC) of 0.56. Across the model's 8 folds. The highest performance across folds reached an AUC of 0.78, accuracy of 0.69, and recall of 0.83. Saliency maps highlighted the right insula, bilateral ventromedial prefrontal cortex, and periaqueductal gray matter as key regions. Our study provides insights at the intersection of neurology, neuroimaging, and predictive modeling, demonstrating that early T1-weighted MRI scans may offer useful information for predicting chronic head and neck pain. Saliency maps may help identify brain regions linked to chronic pain, representing an initial step toward targeted rehabilitation and early intervention for patients with mTBI to enhance clinical outcomes.</p>","PeriodicalId":19921,"journal":{"name":"PAIN®","volume":" ","pages":""},"PeriodicalIF":5.9000,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"PAIN®","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/j.pain.0000000000003587","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ANESTHESIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract: Chronic headache is a common complication after mild traumatic brain injury (mTBI), which affects close to 70 million individuals annually worldwide. This study aims to test the utility of a unique, early predictive magnetic resonance imaging (MRI)-based classification model using structural brain MRI scans, a rarely used approach to identify high-risk individuals for post-mTBI chronic pain. We recruited 227 patients with mTBI after a vehicle collision, between March 30, 2016 and December 30, 2019. T1-weighted brain MRI scans from 128 patients within 72 hours postinjury were included and served as input for a pretrained 3D ResNet-18 deep learning model. All patients had initial assessments within the first 72 hours after the injury and performed follow-ups for 1 year. Chronic pain was reported in 43% at 12 months postinjury; remaining 57% were assigned to the recovery group. The best results were achieved for the axial plane with an average accuracy of 0.59 and an average area under the curve (AUC) of 0.56. Across the model's 8 folds. The highest performance across folds reached an AUC of 0.78, accuracy of 0.69, and recall of 0.83. Saliency maps highlighted the right insula, bilateral ventromedial prefrontal cortex, and periaqueductal gray matter as key regions. Our study provides insights at the intersection of neurology, neuroimaging, and predictive modeling, demonstrating that early T1-weighted MRI scans may offer useful information for predicting chronic head and neck pain. Saliency maps may help identify brain regions linked to chronic pain, representing an initial step toward targeted rehabilitation and early intervention for patients with mTBI to enhance clinical outcomes.
期刊介绍:
PAIN® is the official publication of the International Association for the Study of Pain and publishes original research on the nature,mechanisms and treatment of pain.PAIN® provides a forum for the dissemination of research in the basic and clinical sciences of multidisciplinary interest.