{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T03:00:19Z","timestamp":1785380419193,"version":"3.55.0"},"reference-count":74,"publisher":"SAGE Publications","issue":"8","license":[{"start":{"date-parts":[[2021,4,1]],"date-time":"2021-04-01T00:00:00Z","timestamp":1617235200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Neurotrauma"],"published-print":{"date-parts":[[2021,4]]},"abstract":"<jats:p>\n                    Head injury models are notoriously time consuming and resource demanding in simulations, which prevents routine application. Here, we extend a convolutional neural network (CNN) to instantly estimate element-wise distribution of peak maximum principal strain (MPS) of the entire brain (&gt;36 k speedup accomplished on a low-end computing platform). To achieve this, head impact rotational velocity and acceleration temporal profiles are combined into two-dimensional images to serve as CNN input for training and prediction of MPS. Compared with the directly simulated counterparts, the CNN-estimated responses (magnitude and distribution) are sufficiently accurate for 92.1% of the cases\n                    <jats:italic toggle=\"yes\">via<\/jats:italic>\n                    10-fold cross-validation using impacts drawn from the real world (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u20095661; range of peak rotational velocity in augmented data extended to 2\u201340\u2009rad\/sec). The success rate further improves to 97.1% for \u201cin-range\u201d impacts (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u20094298). When using the same CNN architecture to train (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u20093064) and test on an independent, reconstructed National Football League (NFL) impact dataset (\n                    <jats:italic toggle=\"yes\">n<\/jats:italic>\n                    \u2009=\u200953; 20 concussions and 33 non-injuries), 51 out of 53, or 96.2% of the cases, are sufficiently accurate. The estimated responses also achieve virtually identical concussion prediction performances relative to the directly simulated counterparts, and they often outperform peak MPS of the whole brain (e.g., accuracy of 0.83 vs. 0.77\n                    <jats:italic toggle=\"yes\">via<\/jats:italic>\n                    leave-one-out cross-validation). These findings support the use of CNN for accurate and efficient estimation of spatially detailed brain strains across the vast majority of head impacts in contact sports. Our technique may hold the potential to transform traumatic brain injury (TBI) research and the design and testing standards of head protective gears by facilitating the transition from acceleration-based approximation to strain-based design and analysis. This would have broad implications in the TBI biomechanics field to accelerate new scientific discoveries. The pre-trained CNN is freely available online at\n                    <jats:uri xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/github.com\/Jilab-biomechanics\/CNN-brain-strains\">https:\/\/github.com\/Jilab-biomechanics\/CNN-brain-strains<\/jats:uri>\n                    .\n                  <\/jats:p>","DOI":"10.1089\/neu.2020.7281","type":"journal-article","created":{"date-parts":[[2020,10,31]],"date-time":"2020-10-31T01:07:15Z","timestamp":1604106435000},"page":"1023-1035","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":64,"title":["Instantaneous Whole-Brain Strain Estimation in Dynamic Head Impact"],"prefix":"10.1177","volume":"38","author":[{"given":"Kianoosh","family":"Ghazi","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoju","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Songbai","family":"Ji","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering and Worcester Polytechnic Institute, Worcester, Massachustts, USA."},{"name":"Department of Mechanical Engineering, Worcester Polytechnic Institute, Worcester, Massachustts, USA."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"e_1_3_4_2_2","volume-title":"World Report on Road Traffic Injury Prevention","author":"Peden M.","year":"2004","unstructured":"Peden M., , Scurfield R., , Sleet D., , Mohan D., , Hyder A., , Jarawan E., , and Mathers C. 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