mlsanityData to 7 Oct 2026

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

arXiv 2407.16165 ↗First posted 23 Jul 2024eess.IV cs.CV cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 41 of its 72 references that are in the map. First posted on arXiv 23 Jul 2024.

Builds on

41 of 72 references.

References · 41

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Cited by year

Citing papers per year: 2024: 8; 2025: 1.

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