  Trauma significantly impacts global health, accounting for over 5 million
deaths annually, which is comparable to mortality rates from diseases such as
tuberculosis, AIDS, and malaria. In Iran, the financial repercussions of road
traffic accidents represent approximately 2% of the nation's Gross National
Product each year. Bleeding is the leading cause of mortality in trauma
patients within the first 24 hours following an injury, making rapid diagnosis
and assessment of severity crucial. Trauma patients require comprehensive scans
of all organs, generating a large volume of data. Evaluating CT images for the
entire body is time-consuming and requires significant expertise, underscoring
the need for efficient time management in diagnosis. Efficient diagnostic
processes can significantly reduce treatment costs and decrease the likelihood
of secondary complications. In this context, the development of a reliable
Decision Support System (DSS) for trauma triage, particularly focused on the
abdominal area, is vital. This paper presents a novel method for detecting
liver bleeding and lacerations using CT scans, utilising the GAN Pix2Pix
translation model. The effectiveness of the method is quantified by Dice score
metrics, with the model achieving an accuracy of 97% for liver bleeding and 93%
for liver laceration detection. These results represent a notable improvement
over current state-of-the-art technologies. The system's design integrates
seamlessly with existing medical imaging technologies, making it a practical
addition to emergency medical services. This research underscores the potential
of advanced image translation models like GAN Pix2Pix in improving the
precision and speed of medical diagnostics in critical care scenarios.
