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Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach
Jae Won Seo1, Suyoung Park2, Young Jae Kim3
1Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon, 21999, Republic of Korea.
Scientific Reports
|January 18, 2023
Summary
This study shows an artificial intelligence (AI) algorithm effectively detects iliofemoral deep venous thrombosis using computed tomography angiography. The AI approach improves diagnostic efficiency for critical cases.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Early diagnosis of deep venous thrombosis (DVT) is crucial to prevent severe complications like pulmonary embolism.
- Current computer-aided diagnosis (CAD) methods often overlook clinical diagnostic approaches.
- Iliofemoral DVT detection requires efficient and accurate diagnostic tools.
Purpose of the Study:
- To evaluate an artificial intelligence (AI) algorithm for detecting iliofemoral deep venous thrombosis (DVT) using computed tomography angiography (CTA).
- To investigate the effectiveness of incorporating a clinical diagnostic approach into the AI's feature extraction process.
- To assess the performance of the AI model in a practical, clinical diagnostic setting.
Main Methods:
- Developed and applied a convolutional neural network (CNN)-based RetinaNet model for DVT detection.
- Utilized synthesized images simulating practical diagnostic procedures for training and testing.
- Evaluated model performance using different backbones (ResNet50, ResNet152) and datasets.
Main Results:
- The AI model demonstrated high sensitivity in detecting iliofemoral DVT.
- ResNet50 backbone achieved a sensitivity of 0.843 (±0.037) with 0.608 (±0.139) false positives per image.
- ResNet152 backbone achieved a sensitivity of 0.839 (±0.031) with 0.503 (±0.079) false positives per image.
Conclusions:
- The proposed AI method effectively utilizes lower extremity CTA for iliofemoral DVT detection.
- Integrating a clinical approach into AI feature extraction enhances diagnostic performance.
- The AI algorithm shows potential for improving reporting efficiency in critical iliofemoral DVT cases.
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