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Thoracic VCAR is a CT, non-invasive image analysis software package, which may be used in conjunction with CT lung images to aid in the assessment of thoracic disease diagnosis and management. The software will provide automatic segmentation of the lungs and automatic segmentation and tracking of the airway tree. The software will provide quantification of Hounsfield units and display by color the thresholds within a segmented region.

Thoracic VCAR is a CT post-processing software for the GE Advantage Workstation (AW) platform. It is designed for the analysis and processing of volumetric CT chest data. It provides quantitative information to aid in the assessment of respiratory diseases. The primary features of the software are: lung and lobe segmentation to obtain threshold based volume measurements; bronchial tree segmentation and tracking to determine wall thickness measurements; lung maps based on HU values to help the physician in determining the location and extent of disease across both lungs as well as each lobe.

The provided document is a 510(k) Premarket Notification Summary for GE Healthcare's THORACIC VCAR, a CT post-processing software. The document explicitly states:

"Summary of Clinical Tests: The subject of this premarket submission, THORACIC VCAR, did not require clinical studies to support substantial equivalence."

Therefore, the document does not contain information regarding

1. A table of acceptance criteria and the reported device performance
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
6. If a standalone (i.e. algorithm only without human-in-the loop performance) was done
7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
8. The sample size for the training set
9. How the ground truth for the training set was established

The document focuses on non-clinical tests to establish substantial equivalence to predicate devices, including compliance with DICOM Standard NEMA PS 3.1 - 3.18(2008) and the application of quality assurance measures during development (Risk Analysis, Requirements Reviews, Design Reviews, Performance testing, Safety testing, Final acceptance testing).

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