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Veolity is intended to:

• display a composite view of 2D cross-sections, and 3D volumes of chest CT images,

• allow comparison between new and previous acquisitions as well as abnormal thoracic regions of interest, such as pulmonary nodules,

• provide Computer-Aided Detection ("CAD") findings, which assist radiologists in the detection of solid pulmonary nodules between 4-30 mm in size in CT images with or without intravenous contrast. CAD is intended to be used as an adjunct, alerting the radiologist - after his or her initial reading of the scan - to regions of interest that may have been initially overlooked.

The system can be used with any combination of these features. Enabling is handled via licensing or configuration options.

Veolity is a medical imaging software platform that allows processing, review, and analysis of multi-dimensional digital images.

The system integrates within typical clinical workflow patterns through receiving and transferring medical images over a computer network. The software can be loaded on a standard off-theshelf personal computer (PC). It can operate as a stand-alone workstation or in a distributed server-client configuration across a computer network.

Veolity is intended to support the radiologist in the review and analysis of chest CT data. Automated image registration facilitates the synchronous display and navigation of current and previous CT images for follow-up comparison

The software enables the user to determine quantitative and characterizing information about nodules in the lung in a single study, or over the time course of several thoracic studies. Veolity automatically performs the measurements for segmented nodules, allowing lung nodules and measurements to be displayed. Afterwards nodule segmentation contour lines can be edited by the user manually with automatic recalculation of geometric measurements post-editing. Further, the application provides a range of interactive tools specifically designed for segmentation and volumetric analysis of findings in order to determine growth patterns and compose comparative reviews.

Veolity requires the user to identify a nodule and to determine the type of nodule in order to use the appropriate characterization tools. Additionally, the software provides an optional/licensable CAD package that analyzes the CT images to identify findings with features suggestive of solid pulmonary nodules between 4-30 mm in size. The CAD is not intended as a detection aid for either part-solid or non-solid lung nodules. The CAD is intended to be used as an adjunct, alerting the radiologist – after his or her initial reading of the scan – to regions of interest that may have been initially overlooked.

The provided text describes the MeVis Medical Solutions AG's Veolity device and its 510(k) submission (K201501). However, the document states: "N/A - No clinical testing has been conducted to demonstrate substantial equivalence." This means that detailed acceptance criteria tables, sample sizes for test sets, expert qualifications, etc., as requested in your prompt, are not explicitly provided in the document for a new clinical study.

The submission claims substantial equivalence based on the device being a combination of previously cleared predicate and reference devices. It asserts that the individual functionalities remain technically unchanged. The performance assessment for the CAD system relies on prior panel review results from the initial submission of the predicate device and a re-evaluation with a multi-center dataset designed to be comparable to the predicate device's clinical study.

Therefore, I cannot fully complete all sections of your request with specific details from this document regarding a new study demonstrating the device meets acceptance criteria. I will instead extract the information that is present and highlight the limitations.

Here's a breakdown of the requested information based on the provided text:

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

The document does not provide a specific table of quantitative acceptance criteria for the current submission's performance study. It states that the subject device's CAD system performance "provides equal results in terms of sensitivity and false positive rates compared to the primary predicate device."

It re-evaluated performance "in terms of sensitivity and false positive rate per case" and found it "equivalent to the primary predicate device."

• Acceptance Criteria (Implied): Equivalence in sensitivity and false positive rates per case compared to the primary predicate device (ImageChecker CT CAD Software System K043617).
• Reported Device Performance: Stated as "equivalent" to the primary predicate device's performance, which was based on its own initial submission. No specific numerical values for sensitivity or false positive rates are provided for Veolity.

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

• Test Set Sample Size: Not explicitly stated for the "re-evaluated" performance study. The text mentions it was conducted with "a multi-center dataset."
• Data Provenance: "modern and multivendor CT data." The document does not specify the country of origin or whether it was retrospective or prospective. Given it's a re-evaluation designed to be comparable to a predicate's clinical study, it's likely retrospective.

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

Not explicitly stated for the "re-evaluated" performance study. The document refers to "panel review results" from the initial submission of the predicate device for its performance assessment. It does not provide details on the number or qualifications of experts for Veolity's re-evaluation.

4. Adjudication method for the test set

Not explicitly stated. Given the reliance on prior predicate studies, this information would likely be found in the predicate's 510(k) submission.

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

• MRMC Study: The document does not describe an MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated for this submission.
• Effect Size: Not applicable, as no such study is described. The CAD's indication for use states it is "intended to be used as an adjunct, alerting the radiologist - after his or her initial reading of the scan - to regions of interest that may have been initially overlooked." This implies an assistive role, but no data on human-AI collaboration improvement is presented here.

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

Yes, a standalone performance evaluation of the CAD algorithm itself was done. The document states: "the subject device's CAD system performance provides equal results... in terms of sensitivity and false positive rates." This implies an algorithm-only evaluation against ground truth.

7. The type of ground truth used

The document does not explicitly state the method for establishing ground truth for the re-evaluated dataset. However, given that it's comparing against "sensitivity and false positive rates" for solid pulmonary nodules, the ground truth would typically be established by expert consensus (e.g., highly experienced radiologists, often with follow-up or pathology correlation if available within the original study design of the predicate).

For the predicate device, it mentions "panel review results," which strongly suggests expert consensus.

8. The sample size for the training set

The document does not provide any information about the training set used for the Veolity CAD algorithm. It only discusses performance evaluations.

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

Not provided in the document.

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AVIEW LCS is intended for the review and analysis and reporting of thoracic CT images for the purpose of characterizing nodules in the lung in a single study, or over the time course of several thoracic studies. Characterizations include nodule type, location of the nodule and measurements such as size (major axis), estimated effective diameter from the volume of the volume of the nodule, Mean HU (the average value of the CT pixel inside the nodule in HU), Minimum HU, Max HU, mass (mass calculated from the CT pixel value), and volumetric measures (Solid Major, length of the longest diameter measured in 3D for a solid portion of the nodule. Solid 2nd Major: The length of the longest diameter of the solid part, measured in sections perpendicular to the Major axis of the nodule), VDT (Volume doubling time), Lung-RADS (classification proposed to aid with findings) and CAC score and LAA analysis. The system automatically performs the measurement, allowing lung nodules and measurements to be displayed and, also integrate with FDA certified Mevis CAD (Computer-aided detection) (K043617).

AVIEW LCS is intended for use as diagnostic patient imaging which is intended for the review and analysis of thoracic CT images. Provides following features as semi-automatic nodule measurement (segmentation), maximal plane measure, 3D measure and volumetric measures, automatic nodules detection by integration with 3th party CAD. Also provides cancer risk based on PANCAN risk model which calculates the malignancy score based on numerical or Boolean inputs. Follow up support with automated nodule matching and automatically categorize Lung-RADS score which is a quality assurance tool designed to standardize lung cancer screening CT reporting and management recommendations that is based on type, size, size change and other findings that is reported.

The provided text does not contain detailed acceptance criteria for specific performance metrics of the AVIEW LCS device, nor does it describe a study proving the device meets particular acceptance criteria with quantitative results.

The document is a 510(k) premarket notification summary, which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed performance study like a clinical trial.

However, based on the information provided, here's what can be extracted and inferred regarding performance and testing:

1. Table of Acceptance Criteria and Reported Device Performance

As specific quantitative acceptance criteria and detailed performance metrics are not explicitly stated in the provided text for AVIEW LCS, I cannot create a table of acceptance criteria and reported device performance. The document generally states that "the modified device passed all of the tests based on pre-determined Pass/Fail criteria" for software validation.

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

The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective). The described "Unit Test" and "System Test" are internal software validation tests rather than clinical performance studies involving patient data.

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

The document does not mention using experts to establish ground truth for a test set. This type of information would typically be found in a clinical performance study.

4. Adjudication Method for the Test Set

The document does not describe any adjudication method for a test set. This is relevant for clinical studies where multiple readers assess cases.

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

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was performed. Therefore, no effect size of human readers improving with AI vs. without AI assistance is mentioned.

6. Standalone (Algorithm Only) Performance Study

The document does not explicitly state that a standalone (algorithm only without human-in-the-loop performance) study was conducted. The "Performance Test" section refers to DICOM, integration, and thin client server compatibility reports, which are software performance tests, not clinical efficacy or diagnostic accuracy studies for the algorithm itself. The device description mentions "automatic nodules detection by integration with 3rd party CAD (Mevis Visia FDA 510k Cleared)", suggesting it leverages an already cleared CAD system for detection rather than having a new, independently evaluated detection algorithm as part of this submission.

7. Type of Ground Truth Used

The document does not specify the type of ground truth used for any performance evaluation. Again, this would be characteristic of a clinical performance study.

8. Sample Size for the Training Set

The document does not provide the sample size for any training set. This is typically relevant for AI/ML-based algorithms. The mention of "deep-learning algorithms" for lung and lobe segmentation suggests a training set was used, but its size is not disclosed.

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

The document does not explain how ground truth for any potential training set was established.

Summary of available information regarding testing:

The "Performance Data" section (8) of the 510(k) summary focuses on nonclinical performance testing and software verification and validation activities.

• Nonclinical Performance Testing: The document states, "This Medical device 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. The substantial equivalence of the device is supported by the nonclinical testing." This indicates the submission relies on the substantial equivalence argument and internal software testing, not new clinical performance data for efficacy.
• Software Verification and Validation:
  • Unit Test: Conducted using Google C++ Unit Test Framework on major software components for functional, performance, and algorithm analysis.
  • System Test: Conducted based on "integration Test Cases" and "Exploratory Test" to identify defects.
    • Acceptance Criteria for System Test: "Success standard of System Test is not finding 'Major', 'Moderate' defect."
    • Defect Classification:
      • Major: Impacting intended use, no workaround.
      • Moderate: UI/general quality, workaround available.
      • Minor: Not impacting intended use, not significant.
  • Performance Test Reports: DICOM Test Report, Performance Test Report, Integration Test Report, Thin Client Server Compatibility Test Report.

In conclusion, the provided 510(k) summary primarily addresses software validation and verification to demonstrate substantial equivalence, rather than a clinical performance study with specific acceptance criteria related to diagnostic accuracy, reader performance, or a detailed description of ground truth establishment.

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