(89 days)
The Vitrea Lung Density Analysis software provides CT values for the pulmonary tissue from CT thoracic datasets. Three-dimensional (3D) segmentation of the left lung and right lung, volumetric analysis, density evaluations and reporting tools are integrated in a specific workflow to offer the physician a quantitative support for diagnosis and follow-up evaluation of lung tissue images.
Vitrea CT Lung Density Analysis assists in analyzing lung densities and volumes. It semiautomatically segments lung tissues with quantifiable controls and renderings to aid communication with the pulmonologist.
The key features are:
- Semi-automatic right lung, left lung, and airway segmentation .
- Visualization of lung density with color-defined Hounsfield Unit (HU) ranges ●
- . Lung density result quantification with HU density range, volume measurements, lunq density index, and the PD15% measurement
- . Density graph/histogram of the classified lung voxels' relative frequencies
- Comparison of upper and lower lung density index ratios .
- Adjustable density thresholds for refining and optimizing HU ranges ●
- Overlay of density quantification results and density graph histogram for reporting
- Export of density values and curves to CSV tables or copy to clipboard for insertion into a ● report
Here's an analysis of the provided text to extract the acceptance criteria and study details. Please note that the document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed clinical trial report with specific acceptance criteria and performance metrics against those criteria. Therefore, some information, particularly quantitative acceptance criteria and specific performance measures, is not explicitly stated in this document.
The document primarily relies on demonstrating equivalence in intended use, technological characteristics, and safety and effectiveness management (design controls, risk management, software verification and validation).
Acceptance Criteria and Reported Device Performance
The document does not explicitly state quantitative acceptance criteria in terms of performance metrics (e.g., sensitivity, specificity, accuracy thresholds for lung density measurements or segmentation). Instead, it implicitly defines "acceptance" as meeting functional requirements, user needs, and demonstrating substantial equivalence to the predicate device.
The reported device performance is largely qualitative, focusing on whether the software functions as designed and meets user expectations.
| Acceptance Criteria (Implicit from document) | Reported Device Performance |
|---|---|
| Functional Requirements Met | "Software testing was completed to ensure the new features operate according to defined requirements." |
| User Needs and Intended Use Conformance | "The validation team conducted workflow testing that provided evidence that the system requirements and features were implemented, reviewed and met." "During external validation of the CT Lung Density Analysis software, experienced users evaluated the visualization, axial plane location, quantification of density, and snapshots among other features. Each user felt that the Vitrea CT Lung Density Analysis software enables the user to assess and quantify lung density." |
| Safety and Risk Mitigation | "Each risk pertaining to these features have been individually assessed to determine if the benefits outweigh the risk. Every risk has been reduced as low as possible and has been evaluated to have a probability of occurrence of harm of 'Improbable.'" "The overall residual risk for the project is deemed acceptable." |
| Equivalence to Predicate Device | The entire "Substantial Equivalence Comparison" section details how the subject device is similar in regulatory classification, intended use (with one noted difference that is deemed not to raise new questions of safety/effectiveness), and numerous technological features for data loading, viewing, segmentation, lung volume analysis, lung density analysis, and data export. |
| Numerical Quantity Verification | For internal validation, "Results of numerical quantities calculated by CT Lung Density Analysis were verified using CT semi-synthetic phantoms and patient based CT datasets." (No specific metrics or thresholds are provided). |
Study Details:
The document combines internal and external validation for its non-clinical testing. It explicitly states that "The subject of this 510(k) notification, Vitrea CT Lung Density Analysis software, did not require clinical studies to support safety and effectiveness of the software."
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Sample size used for the test set and the data provenance:
- Internal Validation (Phantom Testing): "various phantoms and patient based CT datasets." No specific number is given for either the phantoms or patients.
- External Validation: No specific number of cases or datasets is explicitly mentioned. The focus is on user evaluation of features.
- Data Provenance: Not specified, but "patient based CT datasets" implies retrospective patient data. Given the company is US-based (Minnetonka, MN), it's likely US data or data from a similar regulated environment.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
- For Internal Validation: "Results of numerical quantities calculated by CT Lung Density Analysis were verified using CT semi-synthetic phantoms and patient based CT datasets." It doesn't explicitly state the number or qualifications of experts establishing ground truth for the patient datasets. For phantoms, the ground truth is often inherent in the phantom's design or known physical properties.
- For External Validation: "experienced users evaluated the visualization, axial plane location, quantification of density, and snapshots among other features." The number of experienced users is not specified, nor are their exact qualifications (e.g., "radiologist with X years of experience").
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Adjudication method (e.g. 2+1, 3+1, none) for the test set:
- Not specified. The document describes "verification" and "validation," including internal testing and external user acceptance, but does not detail a specific adjudication method for ground truth establishment.
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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:
- No. The document explicitly states that "The subject of this 510(k) notification... did not require clinical studies to support safety and effectiveness of the software." Therefore, no MRMC study comparing human readers with and without AI assistance was conducted or reported.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
- The document implies a standalone evaluation was performed during internal validation, where "Results of numerical quantities calculated by CT Lung Density Analysis were verified using CT semi-synthetic phantoms and patient based CT datasets." This focuses on the algorithmic output against a known or established truth without direct human interpretation as part of the primary performance metric. However, the subsequent "External Validation" involves human interaction with the software.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
- For Internal Validation:
- Semi-synthetic phantoms: Ground truth is inherent in the phantom's known physical properties or generated data.
- Patient-based CT datasets: The type of ground truth is not explicitly stated (e.g., expert consensus on manual measurements, pathology reference standard). It mentions "verified," implying a reference standard was used, but not its nature.
- For the external validation, "ground truth" was more about user acceptance and functionality, rather than specific quantitative medical accuracy against a clinical reference.
- For Internal Validation:
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The sample size for the training set:
- Not specified. The document details software development and testing, but not the specifics of algorithm training if machine learning was used (which is not explicitly stated but implied for segmentation/analysis).
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How the ground truth for the training set was established:
- Not specified. As the sample size for the training set and the specific methods of AI/ML are not disclosed, the method for establishing ground truth for training data is also not provided.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.