This Article Full Text Full Text (PDF) Submit a response Alert me when this article is cited Alert me when eLetters are posted Alert me if a correction is posted Services Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via Google Scholar Google Scholar Articles by Ogunyemi, O. I. Articles by Webber, B. L. Search for Related Content PubMed PubMed Citation Articles by Ogunyemi, O. I. Articles by Webber, B. L.

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>.

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.