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Summary

This summary is machine-generated.

This study introduces a new dataset and methods for detecting peri-pancreatic edema in pancreatitis patients using AI. Deep learning and radiomics models show promise for accurate and rapid diagnosis, improving patient care.

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Gastroenterology

Background:

Pancreatitis is a global health concern with increasing incidence.
Peri-pancreatic edema is a critical indicator of pancreatitis progression and prognosis.
Accurate detection of peri-pancreatic edema is essential for effective patient management.

Purpose of the Study:

To develop and evaluate automated methods for detecting peri-pancreatic edema using CT imaging.
To introduce a novel, annotated CT dataset for pancreatic diseases.
To benchmark deep learning and radiomics models for this specific diagnostic task.

Main Methods:

A novel CT dataset of 255 patients with annotated pancreas segmentation masks was created.
LinTransUNet model was evaluated for pancreas segmentation from CT data.
Deep learning (Swin-Tiny transformer) and radiomics (XGBoost) classifiers were used for peri-pancreatic edema detection.

Main Results:

LinTransUNet achieved 80.85% dice coefficient and 68.73% mIoU for pancreas segmentation.
Swin-Tiny transformer model showed high performance for edema detection (98.85% recall, 98.38% precision).
Radiomics-based XGBoost demonstrated 79.61% accuracy and 91.05% recall, offering rapid processing.

Conclusions:

This study presents the first automated approach for peri-pancreatic edema detection.
Combining deep learning and radiomics offers a powerful strategy for pancreatitis diagnosis.
The developed dataset and models can significantly impact clinical evaluation of pancreatitis.