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Related Experiment Video

Updated: May 5, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

996

A Vision Transformer-Based Deep Learning Framework for Patient-Level Classification of Acute Pancreatitis and Normal

Gürkan Güneri1, Elif Kır Yazar2, Mesut Furkan Yazar2

  • 1Department of General Surgery, Faculty of Medicine, Bilecik Şeyh Edebali University, Bilecik 11100, Türkiye.

Diagnostics (Basel, Switzerland)
|May 4, 2026
PubMed
Summary

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Computed Tomography01:10

Computed Tomography

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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
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This summary is machine-generated.

A deep learning system using Vision Transformer (ViT) accurately classifies acute pancreatitis (AP) from CT scans. This patient-level assessment enhances diagnostic capabilities for this serious inflammatory pancreatic disease.

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Acute pancreatitis (AP) is a severe inflammatory condition with high mortality.
  • Early and accurate diagnosis of AP is crucial for effective patient management.
  • Current diagnostic methods can be enhanced by advanced computational approaches.

Purpose of the Study:

  • To develop and evaluate a deep learning system for automated AP classification from contrast-enhanced CT images.
  • To compare the performance of Convolutional Neural Network (CNN) and Transformer-based deep learning models.
  • To focus on patient-level assessment for improved clinical applicability.

Main Methods:

  • A dataset of CT images from 183 patients (103 normal, 80 with AP) was curated.
  • Patient-level data splitting was employed to prevent data leakage and ensure objective evaluation.
Keywords:
acute pancreatitisclassificationcomputed tomography (CT)deep learningpatient-level diagnosisvision transformer (ViT)

Related Experiment Videos

Last Updated: May 5, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

996
  • Deep learning models including ResNet50, EfficientNet, ConvNeXtV2, Swin Transformer, and Vision Transformer (ViT) were compared.

    • Main Results:

    • Transformer-based models, particularly ViT, demonstrated superior performance in patient-level evaluation.
    • ViT achieved 89.19% accuracy, an 86.67% F1-score, and an AUC of 0.946 at the patient level.
    • Transformer architectures effectively capture global contextual features, leading to more reliable diagnostic performance.

    Conclusions:

    • Transformer-based deep learning models show significant promise for diagnosing acute pancreatitis.
    • Patient-level evaluation enhances the clinical relevance and usability of AI diagnostic tools.
    • The ViT-based approach offers a robust and accurate method for AP detection in CT imaging.