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

arXiv: https://arxiv.org/abs/2407.16165 · HTML: https://mlsanity.com/paper/2407.16165/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 72 references that are in the map.
- First posted on arXiv 23 Jul 2024.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
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- [Deep Learning for Medical Image Analysis](https://mlsanity.com/paper/1708.08987/) (2017)
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- [3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes](https://mlsanity.com/paper/1607.00582/) (2016)
- [Outlier-Efficient Hopfield Layers for Large Transformer-Based Models](https://mlsanity.com/paper/2404.03828/) (2024)
- [AutoReP: Automatic ReLU Replacement for Fast Private Network Inference](https://mlsanity.com/paper/2308.10134/) (2023)
- [Mind the Inconspicuous: Revealing the Hidden Weakness in Aligned LLMs' Refusal Boundaries](https://mlsanity.com/paper/2405.20653/) (2024)
- [APEER: Automatic Prompt Engineering Enhances Large Language Model Reranking](https://mlsanity.com/paper/2406.14449/) (2024)
- [LinGCN: Structural Linearized Graph Convolutional Network for Homomorphically Encrypted Inference](https://mlsanity.com/paper/2309.14331/) (2023)
- [Chain-of-Action: Faithful and Multimodal Question Answering through Large Language Models](https://mlsanity.com/paper/2403.17359/) (2024)
- [MaxK-GNN: Extremely Fast GPU Kernel Design for Accelerating Graph Neural Networks Training](https://mlsanity.com/paper/2312.08656/) (2023)
- [Visual Prompting Upgrades Neural Network Sparsification: A Data-Model Perspective](https://mlsanity.com/paper/2312.01397/) (2023)
- [Deep Learning-based Anomaly Detection and Log Analysis for Computer Networks](https://mlsanity.com/paper/2407.05639/) (2024)
- [Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution Networks](https://mlsanity.com/paper/2308.11825/) (2023)
- [Automatic News Generation and Fact-Checking System Based on Language Processing](https://mlsanity.com/paper/2405.10492/) (2024)
- [Theoretical Analysis of Meta Reinforcement Learning: Generalization Bounds and Convergence Guarantees](https://mlsanity.com/paper/2405.13290/) (2024)
- [Real-Time Pill Identification for the Visually Impaired Using Deep Learning](https://mlsanity.com/paper/2405.05983/) (2024)
- [TD3 Based Collision Free Motion Planning for Robot Navigation](https://mlsanity.com/paper/2405.15460/) (2024)
- [Conv-CoA: Improving Open-domain Question Answering in Large Language Models via Conversational Chain-of-Action](https://mlsanity.com/paper/2405.17822/) (2024)
- [Cross-Task Multi-Branch Vision Transformer for Facial Expression and Mask Wearing Classification](https://mlsanity.com/paper/2404.14606/) (2024)
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- [Adaptive speed planning for Unmanned Vehicle Based on Deep Reinforcement Learning](https://mlsanity.com/paper/2404.17379/) (2024)
- [AdaPI: Facilitating DNN Model Adaptivity for Efficient Private Inference in Edge Computing](https://mlsanity.com/paper/2407.05633/) (2024)
- [Research on Autonomous Robots Navigation based on Reinforcement Learning](https://mlsanity.com/paper/2407.02539/) (2024)
- [Towards automatic pulmonary nodule management in lung cancer screening with deep learning](https://mlsanity.com/paper/1610.09157/) (2016)
- [Neural Process for Black-Box Model Optimization Under Bayesian Framework](https://mlsanity.com/paper/2104.02487/) (2021)
- [Research on Driver Facial Fatigue Detection Based on Yolov8 Model](https://mlsanity.com/paper/2406.18575/) (2024)
- [Predict Click-Through Rates with Deep Interest Network Model in E-commerce Advertising](https://mlsanity.com/paper/2406.10239/) (2024)
- [Using Automated Vehicle Data as a Fitness Tracker for Sustainability](https://mlsanity.com/paper/2404.16046/) (2024)

## Built on by (most cited first, 9 of 9)

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- [Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments](https://mlsanity.com/paper/2409.14972/) (2024)
- [Evaluating Modern Approaches in 3D Scene Reconstruction: NeRF vs Gaussian-Based Methods](https://mlsanity.com/paper/2408.04268/) (2024)
- [Enhancing Visual Question Answering through Ranking-Based Hybrid Training and Multimodal Fusion](https://mlsanity.com/paper/2408.07303/) (2024)
- [Cross-border Commodity Pricing Strategy Optimization via Mixed Neural Network for Time Series Analysis](https://mlsanity.com/paper/2408.12115/) (2024)
- [Deep Analysis of Time Series Data for Smart Grid Startup Strategies: A Transformer-LSTM-PSO Model Approach](https://mlsanity.com/paper/2408.12129/) (2024)
- [Graphical Structural Learning of rs-fMRI data in Heavy Smokers](https://mlsanity.com/paper/2409.08395/) (2024)
- [Journey into Automation: Image-Derived Pavement Texture Extraction and Evaluation](https://mlsanity.com/paper/2501.02414/) (2025)
- [A Multiscale Gradient Fusion Method for Edge Detection in Color Images Utilizing the CBM3D Filter](https://mlsanity.com/paper/2408.14013/) (2024)

## 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.