DOI: 10.5281/zenodo.17529147
title: prren-visual-risk-detection

description: # PRREN: Visual Recognition Framework for Operating Room Nursing Risk Factors

This repository contains the implementation of **PRREN (Probabilistic Relational Risk Encoding Network)** — a computer vision-based framework for **real-time detection and mitigation of nursing risk factors in operating rooms (ORs)**.  PRREN integrates convolutional and transformer-based architectures to detect safety-related visual events such as instrument mishandling, sterility breaches, and ergonomic risks.

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## Overview

Operating rooms are complex, safety-critical environments where nursing-related risks can compromise patient outcomes.  **PRREN** provides an automated real-time risk assessment system that:

- Detects and quantifies visual cues associated with unsafe nursing practices.  - Models multi-agent interactions and contextual dependencies using relational attention.  - Generates real-time risk alerts and supports adaptive interventions through the **Contextual Risk Intervention Mechanism (CRIM)**.

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## Core Components

| Module | Description ||--------|--------------|| **Relational Attention Mechanism (RAM)** | Models directional influence between nursing agents in multi-agent surgical scenes. || **Context-Aware Risk Encoding (CARE)** | Integrates environmental information such as workflow phase, patient stability, and workload. || **Temporal Smoothness Regularization (TSR)** | Stabilizes predictions and reduces noise in real-time video analysis. || **Contextual Risk Intervention Mechanism (CRIM)** | Converts continuous probabilistic risk signals into actionable alerts. |

Refer to Figures 1–4 (pages 5–10) in the paper for detailed architecture diagrams.

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## Methodology

The PRREN framework combines:- **Convolutional Neural Networks (CNNs)** for spatial feature extraction.  - **Transformer modules** for temporal and contextual modeling.  - **Probabilistic relational inference** to predict risk propagation between agents.

Training is conducted on multi-modal surgical datasets (video, sensor, and audio data), using evaluation metrics such as:- Accuracy, Recall, F1-Score  - AUC-ROC  - Mean Intersection-over-Union (mIoU) for workflow segmentation

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## Datasets

1. **Operating Room Activity Dataset** – annotated surgical videos for activity recognition.  2. **Surgical Nursing Risk Factors Dataset** – multi-label risk dataset with contextual metadata.  3. **Visual Recognition for Medical Environments (VRME)** – large-scale visual dataset emphasizing occlusion and lighting variation.  4. **Operating Room Workflow Analysis Dataset** – synchronized video and sensor data for phase recognition and workflow modeling.

(See Section 4.1 of the paper for detailed dataset descriptions.)

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## Implementation Details

- Framework: **PyTorch**- Hardware: **NVIDIA A100 (80 GB)**- Key parameters:  - Learning rate: `5e-5`  - Weight decay: `0.01`  - Batch size: `8`  - Sequence length: `64 frames`  - Optimizer: `AdamW` with cosine annealing- Multi-modal fusion with early concatenation and cross-modal attention

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## Experimental Results

| Dataset | F1-Score | AUC | Improvement vs. Baselines ||----------|-----------|-----|-----------------------------|| Operating Room Activity | 88.50 | 91.73 | +3.6% || Surgical Nursing Risk Factors | 88.96 | 92.68 | +4.7% || VRME | 87.73 | 91.04 | +3.5% || OR Workflow Analysis | 88.92 | 91.88 | +3.9% |

Refer to Tables 1–4 and Figures 5–8 (pages 13–16) for full benchmark and ablation results.

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## Future Work

- Extend to low-resolution and limited-resource hospital environments.  - Introduce self-adaptive learning for cross-institutional generalization.  - Incorporate additional modalities such as biosensor or IoT data.  

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## Citation

If you use this framework, please cite:

@article{wang2025prren, title={Visual Recognition Framework for Operating Room Nursing Risk Factors}, author={Wang, Shanshan}, journal={Frontiers in Computer Science}, year={2025} }

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## License

Released under the **MIT License**.  You may use, modify, and distribute this work for research and educational purposes.

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## Contact

**Author:** Shanshan Wang  **Email:** wss15637359293@126.com