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The VIDA|vision software provides reproducible CT values for pulmonary tissue, which is essential for providing quantitative support for diagnosis and follow up examinations. VIDA|vision can be used to support the physician in the diagnosis and documentation of pulmonary tissue images (e.g., abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of sub-compartments, volumetric analysis, density evaluations, low density cluster analysis and reporting tools are combined with a dedicated workflow. The VIDA vision software package is also intended to be a real-time interactive evaluation in space and time for CT volume data sets that provides the reconstruction of two dimensional images into a three-dimensional image format.

VIDA|vision is a self-contained image analysis software package. This real-time interactive evaluation in space and time of CT volume datasets provides the reconstruction of two-dimensional images into a three-dimensional image format.

VIDA|vision can be used to support the physician in the diagnosis, treatment planning, and documentation of chest diseases, including lung cancer, asthma, COPD, interstitial lung disease and other lung abnormalities e.g. when examining the pulmonary and thoracic tissue (i.e. lung parenchyma) in CT thoracic datasets.

Evaluation (3D segmentation & isolation of sub-compartments, volumetric analysis, density evaluations, and low density cluster analysis), editing, and reporting tools are combined with a dedicated workflow.

VIDA|vision is designed to analyze pulmonary CT slice data and display analysis results. Each voxel of the scan is measured by Hounsfield units (HU), a measurement of x-ray attenuation that is applied to each volume element in three dimensional space ("voxel"). The HU are utilized to distinguish between air, water, tissue and bone, such distinction is common in the industry.

VIDA|vision provides computed tomography (CT) viewing, airway analysis, and parenchymal density analysis in one application. VIDA|vision provides imaging of bronchial airways that can be used to assess therapy effectiveness and treatment plan based on CT scan data. VIDA|vision reconstructs multiple cross-section images from CT data into a computer model displaying complex bronchial branches.

VIDA|vision provides quantitative measurements and tabulates quantitative properties. VIDA|vision focuses on what is visible to the eye and applies volumetric methods that might otherwise be too tedious to use. The software does not perform any function which cannot be accomplished by a trained user utilizing manual tracing methods; the intent of the software is to save time and automate potential error prone manual tasks.

VIDA|vision has functions for loading, analyzing, and saving datasets, and will generate screen displays, computations and aggregate statistics. VIDA|vision data output may be exported in pdf format or to a csv file.

The provided document is a 510(k) Premarket Notification from the FDA to VIDA Diagnostics Inc. for their device, VIDA|vision. The document primarily focuses on demonstrating substantial equivalence to a predicate device, not on presenting detailed performance metrics and acceptance criteria from a clinical or non-clinical study for a specific AI function.

Specifically, the document states:

• "No human clinical testing was required to support a substantial equivalence finding."
• "Lung and lobe segmentation performance was tested against the predicate performance to demonstrate substantial equivalence." However, it does not provide the specific acceptance criteria or detailed results of this performance testing.

Therefore, many of the requested details about acceptance criteria and the study that proves the device meets them (such as specific performance metrics, sample sizes, expert qualifications, and ground truth methodologies) are not available in the provided text. The document focuses on regulatory compliance and comparison to a predicate, rather than a detailed performance study with quantifiable results.

Based on the information available:

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

• Acceptance Criteria: Not explicitly stated in terms of specific numerical thresholds for AI performance (e.g., specific accuracy, sensitivity, specificity values). The general acceptance criterion appears to be "demonstrate substantial equivalence" to the predicate device in terms of lung and lobe segmentation performance.
• Reported Device Performance: No specific numerical performance metrics (e.g., accuracy, DICE score) are provided. It only states that "Results of testing demonstrate that the device has met all product specifications and user needs within its intended use." for lung and lobe segmentation.

2. Sample size used for the test set and the data provenance:

• Sample Size: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not specified.

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

• Not specified.

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:

• No MRMC study is mentioned or summarized in the document. The statement "No human clinical testing was required" implies such a study assessing human reader improvement with AI assistance was not performed or submitted for this clearance.

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

• It states that "Lung and lobe segmentation performance was tested against the predicate performance." This implies a standalone technical performance evaluation was done for the segmentation algorithm, but no specific metrics or methodology are provided to describe "standalone performance" in detail.

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

• Not specified in the provided text.

8. The sample size for the training set:

• Not specified.

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

• Not specified.

In summary, the provided FDA 510(k) clearance letter and summary primarily address regulatory aspects, device description, and comparison to a predicate device, rather than detailed technical study results with specific performance metrics for the deep learning algorithm. The mention of "deep learning-based segmentation algorithms" is a key difference from the predicate, but the specific validation of these algorithms in terms of detailed performance data is not included in this summary document.

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The VIDA Pulmonary Workstation 2 (PW2) software provides reproducible CT values for pulmonary tissue, which is essential for providing quantitative support for diagnosis and follow up examinations. The PW2 can be used to support the physician in the diagnosis and documentation of pulmonary tissue images (e.g., abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of sub-compartments, volumetric analysis, density evaluations, low density cluster analysis and reporting tools are combined with a dedicated workflow. The VIDA Pulmonary Workstation 2 (PW2) software package is also intended to be a real-time interactive evaluation in space and time for CT volume data sets that provides the reconstruction of two-dimensional images into a three-dimensional image format.

VIDA Pulmonary Workstation 2 (PW2) is a self-contained image analysis software package. This real-time interactive evaluation in space and time for CT volume data sets provides the reconstruction of two-dimensional images into a three-dimensional image format. VIDA Pulmonary Workstation 2 (PW2) can be used to support the physician in the diagnosis and documentation of chest diseases, e.g. when examining the pulmonary tissue (i.e. lung parenchyma) in CT thoracic datasets. Evaluation tools (3D seamentation & isolation of sub-compartments. volumetric analysis, density evaluations, and low density cluster analysis) and reporting tools are combined with a dedicated workflow.

The PW2 is designed to analyze pulmonary CT slice data and display analysis results. Each voxel of the scan is measured by Hounsfield units (HU), a measurement of x-ray attenuation that is applied to each volume element in threedimensional space ("voxel"). The HU are utilized to distinguish between air, water, tissue and bone, such distinction is common in the industry. Figure 1 shows the basic Input/ Output of the CT volume converting to a digitized, colorized picture.

PW2 provides computed tomography (CT) viewing, airway analysis, and parenchymal density analysis in one application. PW2 provides imaging of bronchial airways that can be used to assess therapy effectiveness based on CT scan data. PW2 reconstructs multiple cross-section images from CT data into a computer model displaying complex bronchial branches.

PW2 does not interface directly with any CT or data collection equipment; instead PW2 imports data files previously generated by such equipment.

PW2 provides quantitative measurements and tabulates quantitative properties. PW2 focuses on what is visible to the eye and applies volumetric methods that might otherwise be too tedious to use. The software does not perform any function which cannot be accomplished by a trained user utilizing manual tracing methods; the intent of the software is to save time and automate potential errorprone manual tasks.

The software has functions for loading, analyzing, saving datasets and will generate screen displays, computations and aggregate statistics.

Despite the extensive documentation provided, the 510(k) summary for the VIDA Pulmonary Workstation 2 (PW2) does not contain the specific details required to fully address all aspects of your request.

Here's a breakdown of what can be extracted and what is missing:

The 510(k) submission primarily focuses on establishing substantial equivalence to a predicate device (Siemens InSpace 4D, K071513) based on intended use, technology, and safety considerations, rather than providing detailed performance studies with acceptance criteria, sample sizes, and ground truth methodologies for the PW2's quantitative measurements.

Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific acceptance criteria for quantitative measurements (e.g., accuracy, precision for volumetric analysis or density evaluations) nor does it present a table of reported device performance against such criteria. The document states that the software provides "reproducible CT values for pulmonary tissue" and that its intent is to "save time and automate potential error-prone manual tasks," implying that its performance should be comparable to or better than manual methods in terms of consistency and efficiency, but without specific metrics.

Study Details (Missing from the Provided Document)

The document does not describe a standalone study with acceptance criteria, sample sizes, or ground truth establishment that would definitively "prove" the device meets specific performance metrics. The 510(k) process for this device focused on substantial equivalence to a predicate, and the provided text does not contain independent performance study results.

Therefore, for aspects 2-9, the information is largely not present in the provided 510(k) summary. I can only report what is not there:

2. Sample size used for the test set and the data provenance:

• Not provided. The document does not describe a test set or its sample size.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

• Not provided. The document does not describe the establishment of a ground truth for a test set.

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

• Not provided. No test set or adjudication method 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:

• Not provided. The document does not mention an MRMC study or any assessment of human reader improvement with AI assistance. The focus is on the software's ability to automate tasks that could otherwise be done manually, rather than an AI-assisted workflow improvement for human readers.

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

• Not explicitly described as a formal validation study with metrics. The document states, "The software does not perform any function which cannot be accomplished by a trained user utilizing manual tracing methods; the intent of the software is to save time and automate potential error-prone manual tasks." While this implies the algorithm performs these tasks, it doesn't detail a standalone quantitative performance study with specific outcomes.

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

• Not provided. The document does not describe the use of any specific ground truth for validating the quantitative measurements. The comparison is implicitly against manual methods performed by a "trained user."

8. The sample size for the training set:

• Not provided. The document does not discuss a training set, as it predates the widespread regulatory requirement for detailed AI/ML model training information.

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

• Not provided. As no training set is mentioned, the method for establishing its ground truth is also not.

Summary of available information related to performance/validation:

• Intended Use Statement: The device "provides reproducible CT values for pulmonary tissue" and can "support the physician in the diagnosis and documentation of pulmonary tissue images (e.g., abnormalities)."
• Device Description: The device measures Hounsfield units (HU) to distinguish between air, water, tissue, and bone. It performs 3D segmentation, volumetric analysis, density evaluations, and low-density cluster analysis.
• Functionality: "The software does not perform any function which cannot be accomplished by a trained user utilizing manual tracing methods; the intent of the software is to save time and automate potential error-prone manual tasks."
• Validation Context: The 510(k) mainly focuses on demonstrating substantial equivalence to a predicate device (Siemens InSpace 4D K071513) in terms of intended use and technological characteristics, not a detailed performance study with quantitative acceptance criteria for the new device itself.

In conclusion, the provided text from the 510(k) summary, while detailing the device's intended use and functionality, does not contain the specific quantitative performance metrics, acceptance criteria, study methodologies (including sample sizes, ground truth establishment, or expert involvement), or comparative effectiveness study results that you have requested. The submission strategy relied on demonstrating substantial equivalence rather than a detailed, independent performance validation study against predefined acceptance criteria for the PW2's measurements.

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