{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T21:36:44Z","timestamp":1784151404694,"version":"3.55.0"},"reference-count":54,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T00:00:00Z","timestamp":1672704000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:sec id=\"sec001\"><jats:title>Background<\/jats:title><jats:p>Minor head trauma in children is a common reason for emergency department visits, but the risk of traumatic brain injury (TBI) in those children is very low. Therefore, physicians should consider the indication for computed tomography (CT) to avoid unnecessary radiation exposure to children. The purpose of this study was to statistically assess the differences between control and mild TBI (mTBI). In addition, we also investigate the feasibility of machine learning (ML) to predict the necessity of CT scans in children with mTBI.<\/jats:p><\/jats:sec><jats:sec id=\"sec002\"><jats:title>Methods and findings<\/jats:title><jats:p>The study enrolled 1100 children under the age of 2 years to assess pre-verbal children. Other inclusion and exclusion criteria were per the PECARN study. Data such as demographics, injury details, medical history, and neurological assessment were used for statistical evaluation and creation of the ML algorithm. The number of children with clinically important TBI (ciTBI), mTBI on CT, and controls was 28, 30, and 1042, respectively. Statistical significance between the control group and clinically significant TBI requiring hospitalization (csTBI: ciTBI+mTBI on CT) was demonstrated for all nonparametric predictors except severity of the injury mechanism. The comparison between the three groups also showed significance for all predictors (p&lt;0.05). This study showed that supervised ML for predicting the need for CT scan can be generated with 95% accuracy. It also revealed the significance of each predictor in the decision tree, especially the \"days of life.\"<\/jats:p><\/jats:sec><jats:sec id=\"sec003\"><jats:title>Conclusions<\/jats:title><jats:p>These results confirm the role and importance of each of the predictors mentioned in the PECARN study and show that ML could discriminate between children with csTBI and the control group.<\/jats:p><\/jats:sec>","DOI":"10.1371\/journal.pone.0278562","type":"journal-article","created":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T18:46:43Z","timestamp":1672771603000},"page":"e0278562","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":12,"title":["Statistical and machine learning approaches to predict the necessity for computed tomography in children with mild traumatic brain injury"],"prefix":"10.1371","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-155X","authenticated-orcid":true,"given":"Tadashi","family":"Miyagawa","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marina","family":"Saga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minami","family":"Sasaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Miyuki","family":"Shimizu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Akira","family":"Yamaura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2023,1,3]]},"reference":[{"issue":"18","key":"pone.0278562.ref001","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.1001\/jama.2014.3979","article-title":"Trends in visits for traumatic brain injury to emergency departments in the United States","volume":"311","author":"JR Marin","year":"2014","journal-title":"JAMA\u2014J Am Med Assoc"},{"issue":"9","key":"pone.0278562.ref002","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1111\/acem.12458","article-title":"Variation in emergency department head computed tomography use for pediatric head trauma","volume":"21","author":"JR Marin","year":"2014","journal-title":"Acad Emerg Med"},{"issue":"4","key":"pone.0278562.ref003","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1016\/j.jpeds.2015.06.052","article-title":"Variation in Computed Tomography Imaging for Pediatric Injury-Related Emergency Visits","volume":"167","author":"JR Marin","year":"2015","journal-title":"J Pediatr"},{"issue":"3","key":"pone.0278562.ref004","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1111\/acem.13129","article-title":"External Validation of the PECARN Head Trauma Prediction Rules in Japan","volume":"24","author":"K Ide","year":"2017","journal-title":"Acad Emerg Med"},{"issue":"1","key":"pone.0278562.ref005","doi-asserted-by":"crossref","DOI":"10.3390\/app10010150","article-title":"A hybrid geometric morphometric deep learning approach for cut and trampling mark classification","volume":"10","author":"LA Courtenay","year":"2020","journal-title":"Appl Sci"},{"issue":"8","key":"pone.0278562.ref006","doi-asserted-by":"crossref","first-page":"1599","DOI":"10.1016\/j.ajem.2019.158439","article-title":"Validation of the PECARN head trauma prediction rules in Japan: A multicenter prospective study","volume":"38","author":"K Ide","year":"2019","journal-title":"Am J Emerg Med"},{"issue":"7","key":"pone.0278562.ref007","doi-asserted-by":"crossref","first-page":"648","DOI":"10.1001\/jamapediatrics.2019.1068","article-title":"Comparison of Machine Learning Optimal Classification Trees with the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules","volume":"173","author":"D Bertsimas","year":"2019","journal-title":"JAMA Pediatr"},{"key":"pone.0278562.ref008","volume":"1","author":"M. 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