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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.

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."

1. 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.

2. 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").

3. 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.

4. 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.

5. 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.

6. 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.

7. 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).

8. 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.

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The Imbio CT Lung Density Analysis Software provides reproducible CT values for pulmonary tissue, which is essential for providing quantitative support for diagnosis and follow up examinations. The Imbio CT Lung Density Analysis Software can be used to support the diagnosis and documentation of pulmonary tissue images (e.g., abnormalities) from CT thoracic datasets. Three-D segmentation of sub-compartments, volumetric analysis, density evaluations and reporting tools are provided.

The Imbio CT Lung Density Analysis Software (Imbio LDA) is a set of image post-processing algorithms that perform image segmentation, registration, thresholding, and classification on CT images of human lungs. The algorithms within the Imbio CT Lung Density Analysis Software are combined into a single command-line executable program that may be run directly from the command-line or through scripting. The Imbio CT Lung Density Analysis Software program performs segmentation, then registration, then thresholding and classification. The program reads in DICOM datasets, processes the data, then writes output DICOM files to a specified directory. The Imbio CT Lung Density Analysis Software is a command-line software application that analyzes DICOM CT lung image datasets and generates reports and DICOM output that show the lungs segmented and overlaid with color-codings representing the results of its thresholding and classification rules. It has simple file management functions for input and output, and separate modules that implement the CT image-processing algorithms. Imbio CT Lung Density Analysis Software does not interface directly with any CT or data collection equipment; instead the software imports data files previously generated by such equipment.

Here's a breakdown of the acceptance criteria and study details for the Imbio CT Lung Density Analysis Software, based on the provided FDA 510(k) summary:

This device is cleared under a 510(k), which means it demonstrates substantial equivalence to a legally marketed predicate device rather than proving de novo safety and effectiveness through extensive clinical trials. Therefore, the "acceptance criteria" here refers more to the demonstration that the device's performance aligns with its specifications and is comparable to the predicate, rather than meeting specific clinical efficacy thresholds.

Study that proves the device meets the acceptance criteria:

The study primarily focused on non-clinical testing to demonstrate substantial equivalence to the predicate device (VIDA Pulmonary Workstation 2 (PW2), K083227).

1. Table of Acceptance Criteria and Reported Device Performance:

Since this is a 510(k) summary focused on substantial equivalence through non-clinical testing, specific quantitative "acceptance criteria" and "reported performance" in a typical clinical trial sense are not explicitly provided with numerical thresholds. Instead, the acceptance criteria were implicitly "functional equivalence," "accurate segmentation," and "accurate thresholding" compared to the predicate device and the ground truth derived from the datasets.

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

• Sample Size: Not explicitly stated as a number of patients or cases. The document mentions "CT datasets available upon request from the COPDGene study (www.copdgene.org) and the DIR-Lab (www.dir-lab.com)." These are large, publicly available research datasets, implying a potentially substantial number of cases were available for testing.
• Data Provenance:
  • Country of Origin: Not specified for individual cases, but COPDGene is a multi-center study in the United States. DIR-Lab also sources data, often from US institutions. So, likely predominantly USA.
  • Retrospective or Prospective: The use of "available upon request" datasets like COPDGene and DIR-Lab strongly suggests a retrospective analysis of existing data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts:

• This information is not provided in the summary. Since the testing was primarily non-clinical and involved comparing software output to established datasets and a predicate device, explicit expert ground truth labeling for a test set (e.g., by radiologists) is not detailed. The "ground truth" for segmentation and density measurements would be derived from the inherent data characteristics and comparison to the predicate's outputs, which are themselves based on accepted medical imaging principles.

4. Adjudication Method for the Test Set:

• Not applicable/Not specified. Given the nature of the non-clinical testing focused on software functionality and comparison to a predicate, an expert adjudication process (like 2+1 reading) is not described. The "direct predicate comparison" served as a primary reference.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Reader Improvement:

• No, an MRMC comparative effectiveness study was not done. The summary explicitly states: "This technology is not new, therefore a clinical study was not considered necessary prior to release. Additionally, there was no clinical testing required to support the medical device as the indications for use is equivalent to the predicate device."

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done:

• Yes, a standalone performance assessment was effectively done. The non-clinical testing detailed ("Direct predicate comparison for scan processing completion, segmentation, and thresholding," and "Software verification and validation testing for each requirement specification," "each algorithmic function," and "at the unit, integration, and system level") assesses the algorithm's performance directly, without human intervention during the processing steps.

7. The Type of Ground Truth Used:

• The primary "ground truth" for the non-clinical testing was:
  • Reference standard from public datasets: The inherent, accepted characteristics of the COPDGene and DIR-Lab CT datasets regarding lung anatomy and density.
  • Comparison to Predicate Device Output: The outputs of the legally marketed predicate device (VIDA PW2) on the same CT lung scans were used as a primary comparative reference, implying that the predicate's performance served as a de facto "ground truth" for equivalence.
  • Software Specifications: The internal design specifications and expected behaviors of the Imbio software itself were also used as a basis for verification and validation.

8. The Sample Size for the Training Set:

• Not specified. The document mentions the use of COPDGene and DIR-Lab datasets for "non-clinical testing" and verifying function. It does not provide details on how the training of the algorithms (if applicable, for example, for segmentation models) was performed or the specific datasets and sample sizes used for that purpose. This summary is focused on the verification and validation of the final product for regulatory submission.

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

• Not specified. As the training set details are not provided, neither is the method for establishing its ground truth. However, for algorithms like segmentation and density analysis, ground truth for training would typically involve manual annotation by expert radiologists or technologists, or derived from other well-established imaging techniques or pathological correlation, depending on the algorithm's specifics.

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