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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 specialty 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 and sub-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 distinquished.

The software has been validated for data from Siemens Healthineers (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.

The subject device AI-Rad Companion (Pulmonary) is an image processing software that utilizes machine learning and deep learning algorithms to provide quantitative and qualitative analysis from previously acquired Computed Tomography DICOM images to support qualified clinicians in the evaluation and assessment of disease of the thorax. AI-Rad Companion (Pulmonary) builds on platform functionality provided by the AI-Rad Companion Engine and cloud/edge functionality provided by the Siemens Healthineers teamplay digital platform. AI-Rad Companion (Pulmonary) is an adjunct tool and does not replace the role of a qualified medical professional. AI-Rad Companion (Pulmonary) is also not designed to detect the presence of radiographic findings other than the prespecified list. Qualified medical professionals should review original images for all suspected pathologies.

AI-Rad Companion (Pulmonary) offers:

• Segmentation of lungs, ●
• Segmentation of lung lobes.
• Parenchyma evaluation, ●
• Parenchyma ranges,
• Pulmonary density,
• Visualization of segmentation and parenchyma results,
• Interface to LungCAD,
• Lesion segmentation, ●
• Visualization of lesion segmentation results, ●
• Lesion follow-up

AI-Rad Companion (Pulmonary) requires images of patients of 22 years and older.

AI-Rad Companion (Pulmonary) SW version VA40 is an enhancement to the previously cleared device AI-Rad Companion (Pulmonary) (K213713) 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:

• Sub-solid Lung Nodule Segmentation ●

This feature provides the ability to segment and measure all subtypes of lesions including solid and sub-solid lesions.

• . Modified Indications for Use Statement The indications for use statement was updated to include descriptive text for sub-solid lung nodule addition.
• Updated Subject Device Claims List The claims list was updated to reflect the new device functionality
• . Updated Limitations for Use Additional limitations for use has been added to the subject device.

Here's a breakdown of the acceptance criteria and the study proving the device's performance, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: 273 subjects from the United States and 254 subjects from Germany, for a total of 527 subjects.
• Data Provenance: The data originated from the United States (69% of cases) and Germany (31% of cases). The data was retrospective, as it refers to "previously acquired Computed Tomography DICOM images."
• Imaging Vendors: The test data included images from Canon/Toshiba (18%), GE (35%), Philips (15%), and Siemens (32%).

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

• Number of Experts: Two board-certified radiologists, with a third radiologist for adjudication.
• Qualifications:
  • Radiologist 1: 10 years of experience (board-certified)
  • Radiologist 2: 7 years of experience (board-certified)
  • Adjudicating Radiologist 3: 9 years of experience

4. Adjudication Method for the Test Set

• Method: 2+1 (Two experts independently established ground truth, and in case of disagreement, a third expert served as an adjudicator.)

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• Was it done?: No, a traditional MRMC comparative effectiveness study involving human readers was not performed in the context of this specific submission. The study focuses on the standalone performance of the AI algorithm in comparison to the predicate device's performance, particularly for the new sub-solid nodule segmentation feature. The device is described as an "adjunct tool," but the presented study validates the algorithm's performance against expert consensus, not against human readers with and without AI.

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

• Was it done?: Yes, the performance testing described directly evaluates the AI-Rad Companion (Pulmonary) lesion segmentation algorithm's accuracy (measured by DICE score, bias, and RMSE) against established ground truth. This is a standalone performance evaluation of the algorithm.

7. The Type of Ground Truth Used

• Type: Expert Consensus. The ground truth annotations for the test data were established independently by two board-certified radiologists, with a third radiologist serving as an adjudicator in cases of disagreement.

8. The Sample Size for the Training Set

• The sample size for the training set is not explicitly stated in the provided document. However, it is mentioned that "None of the clinical sites providing the test data provided data for training of any of the algorithms. Therefore there is a clear independence on site level between training and test data." This indicates that a distinct training set (or sets) was used.

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

• The document does not explicitly state how the ground truth for the training set was established. It only emphasizes the independence of the training and test data sites.

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