Machine learning prediction of combat basic training injury from 3D body shape images.
<h4>Introduction</h4>Athletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic recruits may be discharged fro...
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0235017&type=printable |
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| author | Steven Morse Kevin Talty Patrick Kuiper Michael Scioletti Steven B Heymsfield Richard L Atkinson Diana M Thomas |
| author_facet | Steven Morse Kevin Talty Patrick Kuiper Michael Scioletti Steven B Heymsfield Richard L Atkinson Diana M Thomas |
| author_sort | Steven Morse |
| collection | DOAJ |
| description | <h4>Introduction</h4>Athletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic recruits may be discharged from service due to injury. We postulate that certain body characteristics may be used to predict risk of injury with physical activity.<h4>Methods</h4>US Army basic training recruits between the ages of 17 and 21 (N = 17,680, 28% female) were scanned for uniform fitting using the 3D body imaging scanner, Human Solutions of North America at Fort Jackson, SC. From the 3D body imaging scans, a database consisting of 161 anthropometric measurements per basic training recruit was used to predict the probability of discharge from the US Army due to injury. Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.<h4>Results</h4>The ANN model outperformed two other models, (ANN, AUC = 0.70, [0.68,0.72], logistic regression AUC = 0.67, [0.62,0.72], random forest AUC = 0.65, [0.61,0.70]).<h4>Conclusions</h4>Body shape profiles generated from a three-dimensional body scanning imaging in military personnel predicted dischargeable physical injury. The ANN model can be programmed into the scanner to deliver instantaneous predictions of risk, which may provide an opportunity to intervene to prevent injury. |
| format | Article |
| id | doaj-art-24a161a180f54684829569a03ef9d06b |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-24a161a180f54684829569a03ef9d06b2025-01-18T05:31:08ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01156e023501710.1371/journal.pone.0235017Machine learning prediction of combat basic training injury from 3D body shape images.Steven MorseKevin TaltyPatrick KuiperMichael SciolettiSteven B HeymsfieldRichard L AtkinsonDiana M Thomas<h4>Introduction</h4>Athletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic recruits may be discharged from service due to injury. We postulate that certain body characteristics may be used to predict risk of injury with physical activity.<h4>Methods</h4>US Army basic training recruits between the ages of 17 and 21 (N = 17,680, 28% female) were scanned for uniform fitting using the 3D body imaging scanner, Human Solutions of North America at Fort Jackson, SC. From the 3D body imaging scans, a database consisting of 161 anthropometric measurements per basic training recruit was used to predict the probability of discharge from the US Army due to injury. Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.<h4>Results</h4>The ANN model outperformed two other models, (ANN, AUC = 0.70, [0.68,0.72], logistic regression AUC = 0.67, [0.62,0.72], random forest AUC = 0.65, [0.61,0.70]).<h4>Conclusions</h4>Body shape profiles generated from a three-dimensional body scanning imaging in military personnel predicted dischargeable physical injury. The ANN model can be programmed into the scanner to deliver instantaneous predictions of risk, which may provide an opportunity to intervene to prevent injury.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0235017&type=printable |
| spellingShingle | Steven Morse Kevin Talty Patrick Kuiper Michael Scioletti Steven B Heymsfield Richard L Atkinson Diana M Thomas Machine learning prediction of combat basic training injury from 3D body shape images. PLoS ONE |
| title | Machine learning prediction of combat basic training injury from 3D body shape images. |
| title_full | Machine learning prediction of combat basic training injury from 3D body shape images. |
| title_fullStr | Machine learning prediction of combat basic training injury from 3D body shape images. |
| title_full_unstemmed | Machine learning prediction of combat basic training injury from 3D body shape images. |
| title_short | Machine learning prediction of combat basic training injury from 3D body shape images. |
| title_sort | machine learning prediction of combat basic training injury from 3d body shape images |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0235017&type=printable |
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