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Potential for AI as first reader in lung cancer screening
2
Zitationen
10
Autoren
2025
Jahr
Abstract
PURPOSE: To retrospectively assess the agreement between human and automated AI-based readings for low-dose computed tomography (LDCT) outcomes according to LungRADS v1.1 in lung cancer screening (LCS); to test the diagnostic performance of both readings. METHODS: ) with 95 % CI. RESULTS: 0.47; 95 %CI: 0.43-0.50). Sensitivity and specificity were 91.2 % and 75.7 % for AI, and 89.7 % and 90.0 % for human reading (p-value 0.5637 and < 0.0001). PPV and NPV were 6.0 % and 99.8 % for AI, and 13.1 % and 99.8 % for human reading (p-value < 0.0001 and 0.9351). The expected reduction in LDCT reading workload when using AI as first reader was 74.7 %. CONCLUSION: AI reading showed comparable sensitivity but lower specificity than human reading. High NPV of AI may support its use as a first reader in LCS.
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