  Trauma is a significant cause of mortality and disability, particularly among
individuals under forty. Traditional diagnostic methods for traumatic injuries,
such as X-rays, CT scans, and MRI, are often time-consuming and dependent on
medical expertise, which can delay critical interventions. This study explores
the application of artificial intelligence (AI) and machine learning (ML) to
improve the speed and accuracy of abdominal trauma diagnosis. We developed an
advanced AI-based model combining 3D segmentation, 2D Convolutional Neural
Networks (CNN), and Recurrent Neural Networks (RNN) to enhance diagnostic
performance. Our model processes abdominal CT scans to provide real-time,
precise assessments, thereby improving clinical decision-making and patient
outcomes. Comprehensive experiments demonstrated that our approach
significantly outperforms traditional diagnostic methods, as evidenced by
rigorous evaluation metrics. This research sets a new benchmark for automated
trauma detection, leveraging the strengths of AI and ML to revolutionize trauma
care.
