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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.

This premarket notification covers Broncus' LungPoint VBN System. The VBN System is a software only device, providing a navigation system to help the bronchoscopist plan and proceed to a predefined target site (also referred to as region of interest (ROI) in the tracheobronchial tree. Specifically, the VBN system provides guidance to targets preselected by the bronchoscopist in lung tissue. In doing so, the VBN can provide guidance to lymph nodes to enable tissue sampling. It can also facilitate the return to an exact location in the lungs that had previously been treated for assessment of or continued therapy, or enable marker placement.

The provided document is a 510(k) summary for the Broncus LungPoint™ Virtual Bronchoscopic Navigation (VBN) Software. It primarily focuses on demonstrating substantial equivalence to a predicate device after software modifications, rather than presenting a detailed study with specific acceptance criteria and performance metrics for the device itself.

Therefore, much of the requested information cannot be extracted from this document because the submission does not detail a study conducted to establish acceptance criteria and prove the device meets them in the way clinical performance studies typically do for diagnostic or therapeutic devices. This 510(k) is for a software modification, and the performance data section mentions "design control process," "labeling changes, risk analysis, and design verification," rather than a clinical performance study.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

This information is not available in the provided document. The 510(k) summary refers to design verification and risk analysis as evidence of performance, but it does not specify quantitative acceptance criteria or corresponding reported device performance metrics in a clinical context.

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

This information is not available in the provided document. No specific test set or clinical study data is detailed for the performance validation.

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 available in the provided document. There is no mention of a test set requiring ground truth established by experts.

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

This information is not available in the provided document. No test set requiring ground truth adjudication is described.

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

This information is not available in the provided document. The submission details software modifications for a navigation system, not a diagnostic AI system that would typically undergo an MRMC study to compare human reader performance with and without AI assistance.

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

This information is not applicable/not available in the provided document in the context of typical standalone performance studies for AI software. The device is described as a "Virtual Bronchoscopic Navigation (VBN) Software" that "provides guidance to targets preselected by the bronchoscopist." This implies a human-in-the-loop system where the software aids the physician, rather than acting as a standalone diagnostic algorithm. No standalone performance metrics are provided.

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

This information is not available in the provided document. There is no mention of ground truth as no formal clinical performance study is detailed.

8. The sample size for the training set

This information is not available in the provided document. The document refers to software modifications and design verification, not to the training of a machine learning model, which would involve a training set.

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

This information is not available in the provided document. As there is no mention of a training set, the method for establishing its ground truth is also not provided.

Summary of what the document does state about performance:

The document states under "8. Performance Data":
"The planned modifications were subjected to the Broncus design control process. Appropriate labeling changes, risk analysis, and design verification were performed to assure that the VBN software continues to meet its intended use."

And under "9. Safety and Effectiveness":
"Risk management is ensured via a hazard analysis and FMECA, which are used to identify potential hazards. These potential hazards are controlled via software development, verification testing and/or validation testing."

This indicates that the performance verification for this 510(k) submission was based on internal design control processes, risk analysis, and software verification/validation testing, rather than a clinical trial or performance study against predefined clinical acceptance criteria. The submission is focused on demonstrating substantial equivalence of the modified software to its predicate, particularly regarding an "enhanced graphical user interface (GUI)" and streamlined planning/procedure processes.

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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.

This premarket notification covers Broncus' LungPoint VBN System. The VBN System is a software only device, providing a navigation system to help the bronchoscopist plan and proceed to a predefined target site in the tracheobronchial tree. Specifically, the VBN system provides global quidance to targets preselected by the bronchoscopist in peripheral airways. In doing so, the VBN can provide local guidance to lymph nodes to enable tissue sampling. It can also facilitate the return to an exact location in the lungs that had previously been treated for assessment of or continued therapy. The VBN software is installed on an off-the-shelf PC computer system, and is intended to be used with commercially-available flexible bronchoscopes with HRCT scans that are saved in DICOM format.

Here's a breakdown of the acceptance criteria and study information for the LungPoint™ Virtual Bronchoscopic Navigation (VBN) System, based on the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The 510(k) summary does not explicitly state "acceptance criteria" but rather presents performance data from studies. The interpretation is that the demonstrated accuracy values were deemed acceptable by the FDA for clearance.

2. Sample Size Used for the Test Set and Data Provenance

• Animal Study: The document mentions "an animal study" in a "canine model" but does not specify the exact number of animals or trials conducted.
• Phantom Study: The document refers to "an earlier phantom study performed by Merritt et al" but does not specify the sample size (number of phantom cases/measurements).
• Data Provenance:
  • Animal Study: Canine model (prospective, as it was conducted to evaluate the system).
  • Phantom Study: Not explicitly stated, but phantom studies are typically controlled and designed prospectively.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The provided 510(k) summary does not include information on the number or qualifications of experts used to establish ground truth for either the animal or phantom studies. The ground truth for accuracy was likely established through direct measurement of physical distances, rather than expert consensus on subjective interpretations.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for the test set. Given the nature of measuring distance error in physical or virtual environments, it's unlikely that adjudications by multiple readers were required in the same way they would be for subjective image interpretations.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not done or reported in this 510(k) summary. The studies focused on the standalone accuracy of the navigation system rather than its impact on human reader performance.

6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance

Yes, the reported studies primarily assess the standalone performance of the LungPoint VBN system. The accuracy measurements (distance error) evaluate the system's ability to precisely align virtual targets with actual targets, which is an intrinsic performance characteristic of the algorithm/system rather than its direct impact on a human user's diagnostic ability.

7. Type of Ground Truth Used

• Animal Study: The ground truth was based on the "actual target in the real bronchoscope video" which implies physical, measurable locations marked or identified in a live setting, against which the virtual targets were compared. This would be a form of direct measurement/physical truth.
• Phantom Study: Similarly, the ground truth for the phantom study would have been based on physical precision measurements within the phantom model.

8. Sample Size for the Training Set

The 510(k) summary does not mention or specify a sample size for the training set for the VBN software. As a navigation system, its core function is to process existing HRCT scans (DICOM format) to create virtual pathways. While software development involves testing and calibration, the summary does not detail a separate "training set" in the context of machine learning model development. This submission precedes the widespread emphasis on AI/ML training data reporting in regulatory submissions.

9. How the Ground Truth for the Training Set Was Established

Since no training set is mentioned in the provided document, there is no information on how its ground truth was established. For a navigation system of this type, the "training" (if applicable in a more traditional software sense) would likely involve adherence to anatomical models and engineering specifications for calculating virtual paths and registering images.

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