Multimodal Bayesian Network for Robust Assessment of Casualties in Autonomous Triage

TL;DR

This paper introduces a rule-based Bayesian network for casualty assessment in mass casualty incidents, improving accuracy and coverage without needing training data, and demonstrating effectiveness in DARPA Triage Challenge scenarios.

Contribution

The work presents a novel expert-knowledge-guided probabilistic model that outperforms vision-only methods in emergency triage, supporting inference with incomplete data and noise.

Findings

Physiological assessment accuracy increased from 15% to 42% and 19% to 46%.

Overall triage accuracy improved from 14% to 53%.

Diagnostic coverage expanded from 31% to 95%.

Abstract

Mass Casualty Incidents can overwhelm emergency medical systems and resulting delays or errors in the assessment of casualties can lead to preventable deaths. We present a decision support framework that fuses outputs from multiple computer vision models, estimating signs of severe hemorrhage, respiratory distress, physical alertness, or visible trauma, into a Bayesian network constructed entirely from expert-defined rules. Unlike traditional data-driven models, our approach does not require training data, supports inference with incomplete information, and is robust to noisy or uncertain observations. We report performance for two missions involving 11 and 9 casualties, respectively, where our Bayesian network model substantially outperformed vision-only baselines during evaluation of our system in the DARPA Triage Challenge (DTC) field scenarios. The accuracy of physiological assessment improved from 15% to 42% in the first scenario and from 19% to 46% in the second, representing nearly threefold increase in performance. More importantly, overall triage accuracy increased from 14% to 53% in all patients, while the diagnostic coverage of the system expanded from 31% to 95% of the cases requiring assessment. These results demonstrate that expert-knowledge-guided probabilistic reasoning can significantly enhance automated triage systems, offering a promising approach to supporting emergency responders in MCIs. This approach enabled Team Chiron to achieve 4th place out of 11 teams during the 1st physical round of the DTC.