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Deep Learning-based Artificial Intelligence Improves Accuracy of Error-prone Lung Nodules.
Chou-Chin Lan1,2, Min-Shiau Hsieh2,3, Jong-Kai Hsiao2,4
1Division of Pulmonary Medicine, Taipei Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, New Taipei City, Taiwan.
International Journal of Medical Sciences
|April 4, 2022
Summary
Artificial intelligence (AI) significantly reduces false positives in lung nodule detection on CT scans. This AI-assisted approach improves detection accuracy, especially for challenging nodules, aiding early lung cancer diagnosis.
Area of Science:
- Medical Imaging
- Artificial Intelligence in Medicine
- Oncology
Background:
- Early lung cancer detection improves patient outcomes.
- Chest CT scans are crucial for identifying lung nodules.
- Existing AI modules for nodule detection have high false-positive rates, limiting clinical utility.
Purpose of the Study:
- To evaluate the effectiveness of AI assistance in reducing false positives during lung nodule detection on CT scans.
- To assess the impact of AI on the sensitivity and specificity of nodule detection by radiologists.
- To determine if AI assistance improves the detection of challenging, error-prone lung nodules.
Main Methods:
- A deep learning-based AI program was utilized.
- CT images from 60 patients were analyzed.
- Five senior radiologists participated, with three using AI assistance and two performing manual detection.
Main Results:
- AI assistance significantly reduced false positives from 0.617-0.650/scan to 0.067-0.2/scan.
- Sensitivity improved with AI assistance, ranging from 59.2-77.3% compared to 59.2-67.0% without AI.
- AI improved the detection of nodules with challenging characteristics like central locations, ground-glass appearance, and small sizes.
Conclusions:
- AI-assisted detection of lung nodules on CT scans substantially decreases false positives.
- The AI program enhances the detection of difficult-to-identify nodules, crucial for early lung cancer diagnosis.
- AI assistance shows promise for improving radiologist performance in lung nodule detection within clinical settings.
