Automated Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques

핵심 개념

This study introduces a novel approach for the automatic detection of peri-pancreatic edema from CT imaging data using deep learning and radiomics techniques.

초록

This study presents a novel method for the automated detection of peri-pancreatic edema, a crucial indicator of disease progression and prognosis in pancreatitis. The key highlights are:

The authors created a unique dataset of 255 patients with pancreatic diseases, featuring annotated pancreas segmentation masks and corresponding diagnostic labels for peri-pancreatic edema.

They evaluated the performance of the LinTransUNet model, a linear Transformer-based segmentation algorithm, in accurately segmenting the pancreas from CT imaging data. The model achieved a dice coefficient of 80.85% and mIoU of 68.73%.

The authors used the segmented pancreas regions to train two distinctive machine learning classifiers for peri-pancreatic edema detection: deep learning-based models and a radiomics-based XGBoost model.

Among the nine deep learning models benchmarked, the Swin-Tiny transformer model demonstrated the highest recall of 98.85 ± 0.42 and precision of 98.38 ± 0.17.

The radiomics-based XGBoost model achieved an accuracy of 79.61 ± 4.04 and recall of 91.05 ± 3.28, showcasing its potential as a supplementary diagnostic tool given its rapid processing speed and reduced training time.

This is the first study to explore the automatic detection of peri-pancreatic edema, providing a strong baseline for future research in this area.

통계

The dataset consists of CT scans from 255 pancreatitis patients, with 179 patients having peri-pancreatic edema and 76 patients without.
The LinTransUNet model achieved a dice coefficient of 80.85% and mIoU of 68.73% in pancreas segmentation.
The Swin-Tiny transformer model achieved the highest recall of 98.85 ± 0.42 and precision of 98.38 ± 0.17 in peri-pancreatic edema detection.
The radiomics-based XGBoost model achieved an accuracy of 79.61 ± 4.04 and recall of 91.05 ± 3.28.

인용구

"This is the first-ever application of pancreatic edema detection, to our best of knowledge."
"We propose to use modern deep learning architectures and radiomics together and created a benchmarking for the first time for this particular problem, impacting clinical evaluation of pancreatitis, specifically detecting peri-pancreatic edema."

핵심 통찰 요약

Detection of Peri-Pancreatic Edema using Deep Learning and Radiomics Techniques

by Ziliang Hong... 게시일 arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17064.pdf

더 깊은 질문

How can the performance of the peri-pancreatic edema detection models be further improved, especially in terms of generalizability across different patient populations and imaging modalities?

To enhance the performance and generalizability of peri-pancreatic edema detection models, several strategies can be implemented:

Data Augmentation: Increasing the diversity of the dataset through techniques like rotation, flipping, and scaling can help the model learn from a wider range of variations present in different patient populations and imaging modalities.

Transfer Learning: Leveraging pre-trained models on large datasets can aid in transferring knowledge from related tasks, improving the model's ability to generalize to new data.

Ensemble Learning: Combining predictions from multiple models can often lead to better performance by capturing diverse patterns and reducing overfitting.

Regularization Techniques: Implementing regularization methods like dropout or L2 regularization can prevent overfitting and improve the model's ability to generalize.

Cross-Validation: Utilizing techniques like k-fold cross-validation can provide a more robust estimate of the model's performance across different subsets of the data, ensuring generalizability.

Domain Adaptation: Fine-tuning the model on data from different imaging modalities or patient populations can help adapt the model to new scenarios, improving its generalizability.

What are the potential limitations and challenges in translating this automated detection approach into clinical practice, and how can they be addressed?

Translating automated peri-pancreatic edema detection into clinical practice may face the following limitations and challenges:

Regulatory Approval: Obtaining regulatory approval for using automated detection tools in clinical settings can be a lengthy and rigorous process. Collaboration with regulatory bodies is essential to address this challenge.

Interpretability: Clinicians may be hesitant to trust automated systems without understanding how they arrive at their conclusions. Providing transparent and interpretable results can help build trust in the system.

Integration with Existing Workflows: Implementing automated detection tools seamlessly into existing clinical workflows without disrupting efficiency is crucial. Close collaboration with healthcare providers is necessary to address this challenge.

Data Privacy and Security: Ensuring patient data privacy and security when using automated detection systems is paramount. Adhering to strict data protection protocols and regulations can help mitigate this risk.

Validation and Verification: Robust validation studies in diverse patient populations and clinical settings are essential to demonstrate the reliability and efficacy of the automated detection approach.

Given the importance of peri-pancreatic edema in pancreatitis management, how can this automated detection technique be integrated with other diagnostic and prognostic tools to provide a comprehensive assessment of the disease state?

Integrating automated peri-pancreatic edema detection with other diagnostic and prognostic tools can offer a comprehensive assessment of pancreatitis. Here are some ways to achieve this integration:

Multi-Modal Imaging Fusion: Combining information from different imaging modalities, such as CT, MRI, and ultrasound, can provide a more comprehensive view of the disease state, enhancing diagnostic accuracy.

Clinical Decision Support Systems: Integrating automated detection tools into clinical decision support systems can assist healthcare providers in interpreting results and making informed treatment decisions.

Risk Stratification Models: Incorporating peri-pancreatic edema detection results into risk stratification models can help predict disease progression and tailor treatment plans based on individual patient profiles.

Longitudinal Monitoring: Using automated detection techniques for peri-pancreatic edema in longitudinal monitoring can track disease evolution over time, enabling early intervention and personalized care.

Collaborative Care Teams: Facilitating collaboration between radiologists, gastroenterologists, and other specialists using a shared platform can ensure holistic patient management based on comprehensive assessments provided by automated detection tools.