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

Liheng Jiang, Xuechun yang, Chang Yu, Zhizhong Wu, Yuting Wang

TL;DR

This paper tackles the challenge of rapid, accurate abdominal trauma diagnosis from CT scans. It introduces a hybrid AI framework that integrates 3D segmentation with a 2D CNN+RNN backend, augmented by auxiliary segmentation losses and an ensemble strategy to boost robustness. Experimental results show that the 2D CNN baseline delivers strong performance, while the combined 3D+2D+RNN setup with ensembling achieves improved reliability and outperforms traditional methods in both public and private test sets. The approach offers a scalable, real-time decision-support tool with potential to improve patient outcomes in emergency trauma care.

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.

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

TL;DR

This paper tackles the challenge of rapid, accurate abdominal trauma diagnosis from CT scans. It introduces a hybrid AI framework that integrates 3D segmentation with a 2D CNN+RNN backend, augmented by auxiliary segmentation losses and an ensemble strategy to boost robustness. Experimental results show that the 2D CNN baseline delivers strong performance, while the combined 3D+2D+RNN setup with ensembling achieves improved reliability and outperforms traditional methods in both public and private test sets. The approach offers a scalable, real-time decision-support tool with potential to improve patient outcomes in emergency trauma care.

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.
Paper Structure (21 sections, 13 equations, 3 figures, 1 table)

This paper contains 21 sections, 13 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. Data Preprocessing
  4. Study-Level Cropping
  5. Volume Creation and Slicing
  6. Label Processing
  7. Model Architecture and Methodology
  8. Model Workflow
  9. 2.5D Approach with 2D CNN + RNN
  10. Auxiliary Segmentation Loss
  11. Model Ensemble Training
  12. Effusion Detection Ensemble
  13. Ensemble Integration
  14. Experiments and Results
  15. Evaluation Metric
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Overall processing workflow and model structure for the 3D segmentation + 2D CNN + RNN approach.
  • Figure 2: Model ensemble structure for organ models, including multiple Coat Medium and V2s models trained on four-fold or full datasets.
  • Figure 3: Model ensemble structure for effusion detection, using Coat Small and V2s models with minimal post-processing.