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

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

Published on: April 13, 2013

Deep learning-based pressure injury staging: a multicentre study involving 59 hospitals.

Lu Zhou^1,2,3, Zhengyang Zhang^1,2, Junxia Wang⁴

¹Department of Nursing, Peking University People's Hospital, Beijing, China.

Journal of Global Health

|June 19, 2026

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A new deep learning model accurately stages pressure injuries, aiding clinical decisions. This AI model was successfully translated into a smartphone application for broader use.

Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Deep learning for medical diagnostics

Background:

Accurate pressure injury staging is crucial for effective patient care and reducing healthcare burdens.
Developing reliable AI tools can significantly improve the accuracy and efficiency of pressure injury assessment.
This study focuses on creating a deep learning model for pressure injury recognition and its translation into a practical application.

Purpose of the Study:

To develop and evaluate a deep learning-based model for accurate pressure injury recognition and staging.
To translate the best-performing AI model into a preliminary smartphone application for clinical use.

Main Methods:

A multicentre retrospective study involving 1903 pressure injury images from 59 hospitals.

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Last Updated: Jun 20, 2026

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

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

Published on: April 13, 2013

Evaluation of three AI models: Mask R-CNN with ResNet-18, Mask R-CNN with Swin Transformer, and Segmenting Objects by Locations v2.

Performance assessment using metrics like mean average precision (mAP), average precision (AP), and average recall (AR).

Main Results:

The Mask R-CNN model with Swin Transformer achieved the highest performance (mAP = 0.894).
This model outperformed other evaluated AI models in pressure injury staging accuracy.
The best-performing model was successfully integrated into a smartphone application.

Conclusions:

The developed deep learning system demonstrates promising performance for pressure injury staging.
This AI tool can potentially support clinical decision-making in pressure injury management.
Further validation with larger, diverse datasets is recommended to enhance clinical applicability.