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Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax
Jarrel Seah1,2, Cyril Tang2, Quinlan D Buchlak2,3
1Radiology, Alfred Health, Melbourne, Victoria, Australia jarrel.seah@annalise.ai.
BMJ Open
|December 8, 2021
Summary
A deep convolutional neural network (DCNN) accurately detects pneumothorax in chest X-rays, even with complicating factors like drains or fractures. This artificial intelligence tool shows resilience in real-world clinical scenarios.
Area of Science:
- Artificial Intelligence in Radiology
- Medical Imaging Analysis
- Deep Learning Applications
Background:
- Chest radiography is crucial for diagnosing pneumothorax.
- Deep convolutional neural networks (DCNNs) show promise in medical image interpretation.
- Evaluating DCNN performance across diverse clinical subgroups is essential for reliable deployment.
Purpose of the Study:
- To assess a commercial DCNN's ability to detect simple and tension pneumothorax.
- To investigate DCNN performance stratified by specific subgroups (e.g., drains, fractures, emphysema, positioning).
- To test the hypothesis that DCNN performance remains consistent across these subgroups.
Main Methods:
- Retrospective case-control study utilizing a dataset of 2557 chest radiography studies.
- Ground-truthing by three subspecialty thoracic radiologists.
- Performance evaluation using Area Under the Receiver Operating Characteristic Curve (AUC) for DCNN detection of pneumothorax.
Main Results:
- The DCNN demonstrated high performance in detecting pneumothorax, with an AUC of 0.981 for simple and 0.997 for tension pneumothorax.
- DCNN performance was statistically non-inferior across all evaluated subgroups compared to the overall dataset.
- No significant performance degradation was observed in the presence of intercostal drains, fractures, subcutaneous emphysema, or varying patient positioning.
Conclusions:
- Commercially available DCNNs can reliably detect pneumothorax, exhibiting resilience to common complicating factors.
- The study supports the clinical utility of DCNNs in chest radiography, even when facing 'hidden stratification' in patient data.
- Comprehensive training enables DCNNs to maintain performance across clinically relevant subgroups, enhancing their applicability in practice.
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Pneumothorax is a medical condition defined by the buildup of air in the pleural space between the lungs and the chest wall. This accumulation of air can lead to partial or complete lung collapse, resulting in a range of clinical manifestations. Understanding the clinical presentation and effective management strategies is crucial for healthcare professionals in providing timely and appropriate care to individuals with pneumothorax.
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A pneumothorax is a condition where air builds up in the space between the lung and the chest wall, causing the lung to collapse. This condition arises when air enters the space between the parietal and visceral pleura, disrupting the negative pressure essential for lung inflation. This can lead to a partial or complete collapse of the lung.
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