(14 days)
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.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).