Beyond Benchmarks: A Framework for Post Deployment Validation of CT Lung Nodule Detection AI
Background: Artificial intelligence (AI) assisted lung nodule detection systems are increasingly deployed in clinical settings without site-specific validation. Performance reported under benchmark conditions may not reflect real-world behavior when acquisition parameters differ from training data. Purpose: To propose and demonstrate a physics-guided framework for evaluating the sensitivity of a deployed lung nodule detection model to systematic variation in CT acquisition parameters. Methods: Twenty-one cases from the publicly available LIDC-IDRI dataset were evaluated using a MONAI RetinaNet model pretrained on LUNA16 (fold 0, no fine-tuning). Five imaging conditions were tested: baseli...
| Authors | Daniel Soliman |
|---|---|
| Primary category | eess.IV |
| Published | 25 March 2026 |
| Last updated at source | 25 March 2026 |
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