Hemopneumothorax Detection Through the Process of Artificial Evolution

Background: Tension pneumothorax is a leading cause of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods: Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results: The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise.Conclusions: We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.