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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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The three-dimensional weakly supervised deep learning algorithm for traumatic splenic injury detection and sequential

Chi-Tung Cheng^1,2, Hou-Shian Lin^1,2, Chih-Po Hsu^1,2

¹Department of Trauma and Emergency Surgery.

International Journal of Surgery (London, England)

|March 31, 2023

Summary

This summary is machine-generated.

A deep learning (DL) model accurately detects splenic injuries on abdominal CT scans, improving diagnosis in trauma cases. This artificial intelligence approach aids in identifying these common, potentially lethal injuries.

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Area of Science:

Radiology
Medical Imaging
Artificial Intelligence

Background:

Splenic injury is the most common solid visceral injury in blunt abdominal trauma.
High-resolution abdominal CT is crucial for detection, yet injuries are sometimes overlooked.
Deep learning (DL) shows promise in medical image analysis.

Purpose of the Study:

To develop a 3D, weakly supervised DL algorithm for detecting splenic injury on abdominal CT.
To utilize a sequential localization and classification approach for improved accuracy.

Main Methods:

A dataset of 600 patients (50% with splenic injuries) from a tertiary trauma center was used.
A two-step DL algorithm (localization and classification) was constructed and evaluated.
Performance metrics included AUROC, accuracy, sensitivity, specificity, PPV, and NPV, with external validation.

Main Results:

The DL model achieved an AUROC of 0.901, with accuracy, sensitivity, and specificity of 0.88, 0.81, and 0.92, respectively.
Heatmaps correctly identified 96.3% of splenic injury sites in true positive cases.
External validation showed a sensitivity of 0.92 and accuracy of 0.80.

Conclusions:

The developed DL model effectively identifies splenic injury on CT scans.
This AI tool has potential for practical application in trauma diagnosis.