Advanced AI Framework for Enhanced Detection and Assessment of Abdominal Trauma: Integrating 3D Segmentation with 2D CNN and RNN Models

TL;DR

This paper presents an advanced AI framework combining 3D segmentation, 2D CNN, and RNN models to improve the speed and accuracy of abdominal trauma detection from CT scans, outperforming traditional methods.

Contribution

The study introduces a novel integrated AI model that enhances diagnostic performance for abdominal trauma using combined 3D, 2D, and sequential neural networks.

Findings

Significantly improved diagnostic accuracy over traditional methods

Real-time assessment capability for clinical use

Sets a new benchmark in automated trauma detection

Abstract

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.