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Deep learning-enabled system for rapid pneumothorax screening on chest CT.
Xiang Li1, James H Thrall1, Subba R Digumarthy1
1Massachusetts General Hospital, Department of Radiolgoy, United States.
European Journal of Radiology
|October 5, 2019
Summary
A deep learning (DL) program accurately detects pneumothorax on CT scans. This AI tool shows high sensitivity and specificity, aiding rapid diagnosis for critical patient management.
Area of Science:
- Radiology
- Artificial Intelligence
- Medical Imaging
Background:
- Prompt diagnosis of pneumothorax is crucial for patient management.
- Chest CT is a primary imaging modality for diagnosing pneumothorax.
Purpose of the Study:
- To develop and evaluate a deep learning (DL) based image classification program for pneumothorax detection on chest CT.
- To assess the accuracy, sensitivity, and specificity of the DL program.
Main Methods:
- An eight-layer convolutional neural network (CNN) was trained on 80 chest CT scans (50 with pneumothorax, 30 without).
- The CNN classified 2D image patches and generated 3D heat-maps including size, location, and shape descriptors.
- A support vector machine (SVM) was used for final classification.
Main Results:
- The DL program achieved 100% sensitivity and 82.5% specificity in a test set of 200 chest CTs.
- All 160 pneumothoraces in the test set were detected.
- False positives were mainly due to emphysema or imaging artifacts.
Conclusions:
- The DL-based program demonstrates high accuracy for automatic pneumothorax detection on chest CT.
- Integration with high-performance computing and radiologist expertise can enable rapid pre-screening of cases.
- This AI tool can assist in the rapid identification of pneumothorax, a critical finding.
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