Combining Geometric and Probabilistic Reasoning for Computer-based Penetrating-Trauma Assessment
- Affiliations of the authors: Brigham and Women's Hospital, Boston, Massachusetts (OIO, NA);MCP-Hahnemann University, Philadelphia, Pennsylvania (JRC); and University of Edinburgh, Scotland, UK (BLW)
- Correspondence and reprints: Omolola Ogunyemi, PhD, Decision Systems Group, BWH, 20 Shattuck Street, Boston MA 02115; e-mail: <ogunyemi{at}dsg.harvard.edu>
- Received 14 June 2001
- Accepted 24 October 2001
Abstract
Objective To ascertain whether three-dimensional geometric and probabilistic reasoning methods can be successfully combined for computer-based assessment of conditions arising from ballistic penetrating trauma to the chest and abdomen.
Design The authors created a computer system (TraumaSCAN) that integrates three-dimensional geometric reasoning about anatomic likelihood of injury with probabilistic reasoning about injury consequences using Bayesian networks. Preliminary evaluation of TraumaSCAN was performed via a retrospective study testing performance of the system on data from 26 cases of actual gunshot wounds.
Measurements Areas under the receiver operating characteristics (ROC) curve were calculated for each condition modeled in TraumaSCAN that was present in the 26 cases. The comprehensiveness and relevance of the TraumaSCAN diagnosis for the 26 cases were used to assess the overall performance of the system. To test the ability of TraumaSCAN to handle limited findings, these measurements were calculated both with and without input of observed findings into the Bayesian network.
Results For the 11 conditions assessed, the worst area under the ROC curve with no observed findings input into the Bayesian network was 0.542 (95% CI, 0.146–0.937), the median was 0.883 (95% CI, 0.713–1.000), and the best was 1.00 (95% CI, 1.000–1.000). The worst area under the ROC curve with all observed findings input into the Bayesian network was 0.835 (95% CI, 0.602–1.000), the median was 0.941 (95% CI, 0.827–1.000), and the best was 0.992 (95% CI, 0.965–1.000). A comparison of the areas under the curve obtained with and without input of observed findings into the Bayesian network showed that there were significant differences for 2 of the 11 conditions assessed.
Conclusion A computer-based method that combines geometric and probabilistic reasoning shows promise as a tool for assessing ballistic penetrating trauma to the chest and abdomen.
Footnotes
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This work has been supported by contract NO1-LM-4-3515 from the National Library of Medicine and grant DAMD17-94-J-4486 from the Advanced Research Projects Agency.
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↵* If the prior probability of ricochet could be determined, this would serve as a better value.
