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AI-Rad Companion (Pulmonary) is image processing software that provides quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support radiologists and physicians from emergency medicine, specialty care, urgent care, and general practice in the evaluation and assessment of disease of the lungs.

It provides the following functionality:

· Segmentation and measurements of complete lung and lung lobes

• · Identification of areas with lower Hounsfield values in comparison to a predefined threshold for complete lung and lung lobes
• · Providing an interface to external Medical Device syngo.CT Lung CAD
• · Segmentation and measurements of solid lung nodules
• · Dedication of found lung nodules to corresponding lung lobe
• · Correlation of segmented lung nodules of current scan with known priors and quantitative assessment of changes of the correlated data.
• · Identification of areas with elevated Hounsfield values, where areas with elevated versus high opacities are distinguished.

The software has been validated for data from Siemens (filtered backprojection and iterative reconstruction), GE Healthcare (filtered backprojection reconstruction), and Philips (filtered backprojection reconstruction).

Only DICOM images of adult patients are considered to be valid input.

AI-Rad Companion (Pulmonary) SW version VA20 is an enhancement to the previously cleared device AI-Rad Companion (Pulmonary) K183271 that utilizes machine and deep learning algorithms to provide quantitative and qualitative analysis to computed tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax.

As an update to the previously cleared device, the following modifications have been made:

• Modified Indications for Use Statement The indications for use statement was updated to include descriptive text for the lung lesion follow feature.
• Updated Subject Device Claims List The claims list was updated to include claim pertaining to the lung lesion follow up feature.
• Lung Lesion Follow-up Assessment of current and prior lesions This feature provides the possibility to compare currently segmented lung lesions with corresponding priors and changes to the correlated data are assessed quantitatively.
• Pulmonary Density Assessment

This feature provides the possibility to segment opacity regions inside the lung using an AI algorithm. AI-Rad Companion (Pulmonary) counts image voxels inside opacity regions and calculates the percentages of these voxels relative to the total number of voxels per lobe. lung and in total. Afterwards, each of the five lung lobes is assigned a score ranging from 0 to 4 based on the percentage of opacity as follows: 0 (0%), 1 (1%-25%), 2 (26%-50%), 3 (51%-75%), or 4 (76%-100%). Then a summation of the five lobe scores (range of possible scores, 0-20) are generated in the device outputs. This functionality is commercially available on the Siemens syngo.CT Extended Functionality (K203699).

• . Bi-Directional Lesion Diameter
  This feature provides an additional measurement derived from the existing segmentation contour of a lung lesion. The existing list of measurements is extended with the maximum orthogonal diameter in 2D (short axis diameter) which is orthogonal to the lesion's maximum 2D diameter (2D diameter, long axis diameter). This functionality is commercially available on the Siemens syngo.CT Extended Functionality (K203699).

• . Architecture Enhancement for on premise Edge deployment

  • The system supports the existing cloud deployment as well as an on premise "edge" deployment. The system remains hosted in the teamplay digital health platform and remains driven by the AI-Rad Companion Engine. Now the edge deployment implies that the processing of clinical data and the generation of results can be performed onpremises within the customer network. The edge system is fully connected to the cloud for monitoring and maintenance of the system from remote.

The provided document, a 510(k) summary for Siemens Healthcare GmBh's AI-Rad Companion (Pulmonary) SW version VA20, describes the device, its intended use, and the non-clinical tests performed to demonstrate its safety and effectiveness.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based solely on the provided text:

Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics for all functionalities. However, it does state some performance metrics for one specific feature:

Missing Information: The document does not provide acceptance criteria or performance results for other key functionalities of the AI-Rad Companion (Pulmonary), such as:

• Segmentation and measurements of complete lung and lung lobes.
• Identification of areas with lower Hounsfield values.
• Segmentation and measurements of solid lung nodules.
• Dedication of found lung nodules to corresponding lung lobe.
• Identification of areas with elevated Hounsfield values (Pulmonary Density Assessment).
• Bi-directional lesion diameter measurements.

Study Details:

The provided document describes a non-clinical bench test specifically for the lesion follow-up feature. It explicitly states: "No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion (Pulmonary)." This implies that the reported performance metrics are from an algorithm-only (standalone) performance evaluation, without human-in-the-loop.

Here's what can be extracted about the study:

1. Sample Size and Data Provenance:

   • Test Set Sample Size: 200 cases were used to evaluate the lesion follow-up feature.
   • Data Provenance: Not explicitly stated. The document mentions validation for data from Siemens, GE Healthcare, and Philips (reconstruction types specified), but it does not specify the country of origin of the data or whether it was retrospective or prospective.

2. Number of Experts and Qualifications:

   • The document does not provide information on the number of experts used to establish ground truth or their specific qualifications for the test set.

3. Adjudication Method:

   • The document does not specify any adjudication method (e.g., 2+1, 3+1, none) for the test set. Since it's a non-clinical bench test of the algorithm's ability to identify lesion pairs, it's possible that a different form of ground truth establishment (e.g., based on established physical measurements or derived from existing clinical reports) was used rather than direct expert consensus on each case.

4. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

   • No MRMC study was done. The document explicitly states: "No clinical tests were conducted to test the performance and functionality of the modifications introduced within AI-Rad Companion (Pulmonary)." Therefore, there is no effect size reported for human readers improving with AI vs. without AI assistance.

5. Standalone (Algorithm Only) Performance:

   • Yes, a standalone study was done for the lesion follow-up feature. The reported sensitivity and PPV are for the algorithm's performance in identifying lesion pairs.

6. Type of Ground Truth Used:

   • The document does not explicitly state the type of ground truth used for the lesion follow-up test. It mentions "evaluation of 200 cases to identify lesion pairs," which suggests that a definitive ground truth for paired lesions was available for these 200 cases. This could range from expert consensus, to prior established measurements, or structured clinical reports that define lesion pairs.

7. Training Set Sample Size:

   • The document does not specify the sample size used for the training set. It only mentions the use of "machine and deep learning algorithms."

8. How Ground Truth for Training Set Was Established:

   • The document does not describe how the ground truth for the training set was established.

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