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The Thirona LungQ software provides reproducible CT values for pulmonary tissue which is essential for providing quantitative support for diagnosis and follow up examination. The LungQ software can be used to support physician in the diagnosis and documentation of pulmonary tissues images (e.g. abnormalities) from CT thoracic datasets. Three-D segmentation and isolation of sub-compartments, volumetric analysis, density evaluation, and reporting tools are provided.

The LungQ software is designed to aid in the interpretation of Computed Tomography (CT) scans of the thorax that may contain pulmonary abnormalities. LungQ is stand-alone command-line software which must be run from a command-line interpreter and does not have a graphical user interface.

This document describes the acceptance criteria and the study conducted to prove that the device, LungQ v3.0.0, meets these criteria, as presented in the FDA 510(k) summary.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not explicitly stated in the provided text. The document mentions "scans were taken with a wide variety of scanner brands and models," and lists various scanner types but does not specify the number of cases.
• Data Provenance:
  • Country of Origin: Not specified in the provided text.
  • Retrospective or Prospective: Not specified in the provided text, but the nature of comparing to a predicate device and expert corrected segmentations suggests it was likely retrospective given the need for ground truth.

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

• Number of Experts: Not explicitly stated beyond "human experts."
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The document mentions "segmentation which were corrected by human experts," implying that human experts refined or established the ground truth for (sub)segmental volumes and density scores. However, the process for this correction (e.g., consensus among multiple experts or a single expert's adjudication) is not detailed. Comparisons between the two devices were done using Bland-Altman plots and ROC analysis.

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

• Was an MRMC study done? No. The study was a "head-to-head performance testing" between the subject device (LungQ v3.0.0) and the predicate device (LungQ v1.1.0), and a comparison of LungQ v3.0.0 to human experts for (sub)segmental measurements. There is no mention of human readers evaluating cases with and without AI assistance to determine an effect size of AI on human performance.
• Effect Size of Human Readers with AI vs. without AI assistance: Not applicable, as an MRMC comparative effectiveness study was not performed.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Performance

• Was a standalone study done? Yes. The entire head-to-head performance study comparing LungQ v3.0.0 to LungQ v1.1.0, and the comparison of LungQ v3.0.0 outputs for (sub)segmental analysis against human expert corrections, represents standalone performance evaluation of the algorithm. The device itself is described as "stand-alone command-line software which must be run from a command-line interpreter and does not have a graphical user interface," further reinforcing its standalone nature.

7. Type of Ground Truth Used

• Ground Truth Type:
  • Predicate Device Output: For lung and lobar volumes, LAA-950HU, LAA-910HU, PD15, and fissure completeness, the outputs of the predicate device (LungQ v1.1.0) served as a comparative reference.
  • Expert Consensus/Correction: For (sub)segmental volumes and density scores, the ground truth was established by "segmentation which were corrected by human experts." This implies expert-adjudicated or expert-derived ground truth.

8. Sample Size for the Training Set

• The document does not provide information regarding the sample size used for the training set. The descriptions focus solely on the performance evaluation of the device in comparison to a predicate and human experts.

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

• The document does not provide information on how the ground truth for the training set was established, as details about the training set itself are absent.

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ClariPulmo is a non-invasive image analysis software for use with CT images which is intended to support the quantification of lung CT images. The software is designed to support the physician in the diagnosis and documentation of pulmonary tissue images (e.g., abnormalities) from the CT thoracic datasets. (The software is not intended for the diagnosis of pneumonia or COVID-19). The software provides automated segmentation of the lungs and quantification of low-attenuation and high-attenuation areas within the segmented lungs by using predefined Hounsfield unit thresholds. The software displays by color the segmented lungs and analysis results. ClariPulmo provides optional denoising and kernel normalization functions for improved quantification of lung CT images in cases when CT images were taken at low-dose conditions or with sharp reconstruction kernels.

ClariPulmo is a standalone software application analyzing lung CT images that can be used to support the physician in the quantification of lung CT image when examining pulmonary tissues. ClariPulmo provides two main and optional functions: LAA Analysis provides quantitative measurement of pulmonary tissue image with low attenuation areas (LAA). LAA are measured by counting those voxels with low attenuation values under the user-predefined thresholds within the segmented lungs. This feature supports the physician in quantifying lung tissue image with low attenuation area. HAA Analysis provides quantitative measurement of pulmonary tissue image with high attenuation areas (HAA). HAA are measured by counting those voxels with high attenuation values using the user-predefined thresholds within the segmented lungs. This feature supports the physician in quantifying lung tissue image with high attenuation area. Lungs are automatically segmented using a pre-trained deep learning model. The optional Kernel Normalization function provides an image-to-image translation from a sharp kernel image to a smooth kernel image for improved quantification of lung CT images. The Kernel Normalization algorithm was constructed based on the U-Net architecture. The optional Denoising function provides an image-to-image translation from a noisy low-dose image to a noise-reduced enhanced quality image of LDCT for improved quantification of lung LDCT images. The Denoising algorithm was constructed based on the U-Net architecture. The ClariPulmo software provides summary reports for measurement results that contains color overlay images for the lungs tissues as well as table and charts displaying analysis results.

The provided text is specifically from a 510(k) summary for the ClariPulmo device. It details non-clinical performance testing rather than a large-scale clinical study with human-in-the-loop performance. Therefore, some of the requested information (e.g., MRMC study, effect size of human reader improvement) may not be present in this document because it was not a requirement for this specific type of submission.

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

Acceptance Criteria and Device Performance Study for ClariPulmo

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the "excellent agreement" reported for the different functions, measured by Pearson Correlation Coefficient (PCC) and Dice Coefficient.

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

• HAA Analysis Test Set: The specific number of cases for the test set is not explicitly stated. It mentions "patients with pneumonia and COVID-19."
• LAA Analysis Test Set: The specific number of cases for the test set is not explicitly stated. It mentions "both health and diseased patients."
• AI-based Lung Segmentation Test Set: The specific number of cases is not explicitly stated. It used "one internal and two external datasets."
• Data Provenance: The document does not specify the country of origin for the data. The studies were retrospective as they involved testing against existing datasets with established ground truth.

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

• The document states "expert-established segmentations" and "expert radiologist's imageJ based segmentation."
• The number of experts is not explicitly stated, nor are their specific qualifications (e.g., years of experience), beyond being referred to as "expert."

4. Adjudication Method for the Test Set

• The document does not specify an explicit adjudication method (e.g., 2+1, 3+1). It implies a single "expert-established" ground truth was used for comparison, suggesting either a single expert or a pre-adjudicated consensus.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and its effect size

• No, an MRMC comparative effectiveness study was not done or reported in this document. The performance testing described is focused on the standalone agreement of the AI with expert-established ground truth, not how human readers improve with AI assistance. The document explicitly states: "ClariPulmo does not require clinical studies to demonstrate substantial equivalence to the predicate device."

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

• Yes, standalone performance testing was done. The entire "Performance Testing" section describes the algorithm's performance against expert-established ground truth ("Al-based lung segmentation demonstrated excellent agreements with that by expert radiologist's imageJ based segmentation").

7. The Type of Ground Truth Used

• The ground truth used was expert consensus / expert-established segmentations. Specifically, for lung segmentation, it explicitly mentions "expert radiologist's imageJ based segmentation." For HAA and LAA, it refers to "expert-established segmentations."

8. The Sample Size for the Training Set

• The document does not specify the sample size for the training set. It only mentions that the lung segmentation used a "pre-trained deep learning model" and the Kernel Normalization and Denoising algorithms were "constructed based on the U-Net architecture."

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

• The document does not explicitly describe how the ground truth for the training set was established. It only refers to the test set ground truth as "expert-established segmentations." It is implied that the training data would also have been expertly annotated, given the nature of deep learning model training.

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