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Related Experiment Video

Updated: Jun 12, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Novel two-stage deep learning framework for automated pressure injury classification.

Ting-Yu Lai1, Yi-Jiun Chou2, Chun-You Liu1,3

  • 1Artificial Intelligence and Robotic Innovation Center, China Medical University Hospital, Taichung, Taiwan.

BMJ Health & Care Informatics
|March 27, 2026
PubMed
Summary

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This study introduces an AI framework for automatic pressure injury (PI) staging using deep learning on clinical images. The system achieved high accuracy, aiding clinical staff in consistent PI staging.

Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Healthcare
  • Computational Pathology

Background:

  • Accurate pressure injury (PI) staging is crucial for effective treatment and patient outcomes.
  • Current PI staging relies on subjective clinical assessment, leading to variability.
  • Automating PI staging from clinical images can enhance diagnostic consistency and efficiency.

Purpose of the Study:

  • To develop and validate a two-stage artificial intelligence (AI) framework for automatic pressure injury (PI) staging directly from raw clinical images.
  • To improve diagnostic accuracy and reduce subjectivity in PI staging.
  • To support consistent PI staging within clinical workflows.

Main Methods:

  • A retrospective study utilized 1807 PI images with senior nurse annotations.
Keywords:
Deep LearningNursing

Related Experiment Videos

Last Updated: Jun 12, 2026

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images
14:08

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

  • A two-stage deep learning approach was employed: YOLOv9 for lesion detection and DenseNet161 for staging.
  • Performance was evaluated using metrics including accuracy, sensitivity, specificity, F1 score, AUC, and mAP@0.5.

    • Main Results:

    • The YOLOv9 object detection model achieved a mean average precision (mAP@0.5) of 0.796.
    • The staging model demonstrated an overall accuracy of 0.775, sensitivity of 0.775, and specificity of 0.955 on an independent test set.
    • The framework showed performance comparable to or exceeding previous methods, with improved clinical interpretability.

    Conclusions:

    • The proposed AI framework effectively performs PI staging from clinical images, demonstrating robust performance.
    • The system offers standardized and reproducible PI staging, with potential for integration into nursing workflows.
    • Further development is needed to address challenges like intra-wound heterogeneity and image quality variability.