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Indicated for displaying images of the tracheobronchial tree to aid the physician in guiding endoscopic tools or catheters in the pulmonary tract and to enable marker placement within soft lung tissue. It does not make a diagnosis and is not an endoscopic tool. Not for pediatric use.

The LungPoint Software is a device that guides a bronchoscope and commercially available endoscopic tools to a prespecified target in or adjacent to the bronchial tree by providing a path, which is displayed on a 3D reconstruction of a CT scan. The Software allows visualization of the interior of the bronchial tree; placement of catheters in the bronchial tree; visualization of a prespecified target in lung tissue; and placement of markers into soft lung tissue to guide radiosurgery and thoracic surgery. The FlexNeedle is an aspiration needle. When used together with the LungPoint Software, the needle can be guided to a prespecified targeted area within the respiratory organs.

The software is installed on an off-the-shelf PC computer system, and is intended to be used with commercially-available flexible bronchoscopes with CT scans that are saved in DICOM format.

The provided text is a 510(k) summary for the LungPoint™ Planning and Virtual Bronchoscopic Navigation (VBN) Software. It primarily focuses on demonstrating substantial equivalence to predicate devices rather than presenting detailed performance studies with acceptance criteria and specific statistical results.

Therefore, many of the requested details about acceptance criteria, performance data, test set characteristics, expert qualifications, and ground truth establishment are not explicitly available in the provided text.

Here's an analysis based on the information that is present:

1. A table of acceptance criteria and the reported device performance

The document states: "No new risks were identified as the use of endoscopic tools, like the FlexNeedle, is inherent to the design and intended use of the existing LungPoint Software. Additionally, no new verification and validation testing was performed as the software was not modified in any way to allow for the use of the FlexNeedle."

This indicates that the primary "acceptance criterion" for this specific 510(k) submission was the absence of new risks or modifications to the software that would necessitate new performance testing. Therefore, there is no direct table of acceptance criteria and reported device performance for this submission. The performance of the underlying LungPoint Software would have been established in previous 510(k) clearances (K091160).

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

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)

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

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

There is no mention of an MRMC comparative effectiveness study or AI assistance in the provided text. The device is described as "guid[ing] a bronchoscope... by providing a path, which is displayed on a 3D reconstruction of a CT scan." This suggests a visualization and navigation tool rather than an AI-assisted diagnostic tool.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

No standalone performance study is mentioned in the text for this submission. The device is explicitly intended to "aid the physician in guiding endoscopic tools," indicating a human-in-the-loop design.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

This information is not provided in the text, as no new performance testing was conducted for this specific submission.

8. The sample size for the training set

This information is not provided in the text, as no new performance testing was conducted for this specific submission. The device is not described as being based on machine learning or requiring a "training set" in the context of this 510(k).

9. How the ground truth for the training set was established

This information is not provided in the text, as no new performance testing was conducted for this specific submission and the device's mechanism isn't described as requiring a training set in this context.

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