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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
Published on: December 19, 2020
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Distribution-based detection of radiographic changes in pneumonia patterns: A COVID-19 case study
Sofia C Pereira1,2, Joana Rocha1,2, Aurélio Campilho1,2
1Institute for Systems and Computer Engineering, Technology and Science (INESC-TEC), Portugal.
Heliyon
|December 16, 2024
Summary
This study introduces a novel population-based approach for detecting COVID-19 pneumonia on chest radiographs by analyzing distributional anomalies. This method outperforms traditional single-image analysis, offering a potential early warning system for emerging respiratory diseases.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Public Health
Background:
- Chest radiograph classification is crucial for diagnosing respiratory diseases.
- The COVID-19 pandemic heightened the need for accurate radiographic analysis.
- Conventional methods struggle with overlapping radiological features between COVID-19 and other pneumonias.
Purpose of the Study:
- To develop a novel approach for identifying COVID-19 pneumonia using distributional anomaly detection.
- To compare the efficacy of population-based (distributional) versus instance-based methods for COVID-19 detection.
- To establish a potential early warning system for emerging infectious respiratory diseases.
Main Methods:
- Utilized a population-based approach focusing on sets of chest radiographs.
- Employed distributional anomaly detection to identify deviations from normal pneumonia patterns.
- Used an autoencoder for feature extraction and compared instance-based and distribution-based assessments.
Main Results:
- The proposed distribution-based methodology demonstrated superior performance compared to conventional instance-based techniques.
- The approach effectively identified radiographic changes associated with COVID-19 positive cases.
- Significant separability was observed between COVID-positive and COVID-negative pneumonia radiographs using the distributional method.
Conclusions:
- Distributional anomaly detection offers a more robust method for identifying COVID-19 pneumonia than instance-based approaches.
- This approach can serve as an early warning system for detecting shifts in radiographic data, signaling potential outbreaks.
- Continuous monitoring of distributional shifts in medical imaging data can enable prompt public health responses to emerging health threats.
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