LungVision is an AI-powered software system that helps clinicians navigate tools within the lungs during procedures. It combines previously acquired 3D CT images with real-time fluoroscopic X-ray images to provide detailed guidance for targeting lung lesions, thus enhancing the precision of pulmonary interventions.
The LungVision System is intended to enable users to segment previously acquired 3D CT datasets and overlay and register these 3D segmented data sets with fluoroscopic live X-ray images of the same anatomy in order to support catheter/device navigation during pulmonary procedures.
LungVision integrates software applications that provide 2D and 3D medical image acquisition, including real-time fluoroscopic video. It co-registers live fluoroscopic images to previously created 3D CT image sets, applies image enhancements, and includes a tablet interface for interaction. The system uses AI-driven intraoperative tomographic imaging, employing CABT limited angle tomography based on the SIRT algorithm.
Performance testing included bench tests, verification of hazard mitigation, and system performance. Clinical validation involved physician evaluation with historical LungVision cases, synthetic simulated test cases, rigid lung model testing, and CBCT scan cases. Mean accuracy ranged from 3.15 mm in simulations to 5.34 mm in clinical CBCT cases, confirming lesion marking accuracy comparable across imaging modalities.
No predicate devices specified
Submission
4/5/2024
FDA Approval
10/1/2024
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