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The HealthCCSng device is intended for use as a non-invasive post-processing software to evaluate calcified plaques in the coronary arteries, which present a risk for coronary artery disease.

The HealthCCSng device analyzes routine non-gated, non-contrast CT studies that include the entire heart of adult patients of age 30-85.

The device generates an exact calcium soore and a four coronary artery calcium detection category output representing the estimated quantity of calcium detected together with preview axial images of the detected calcium meant for informational purposes only.

The device output will be available to the radiologist as part of their standard workflow. A list of studies that received a successful algorithm analysis result will also be available for further clinician assessment (such as by a cardiologist, general practitioner etc.).

The HealthCCSng results are not intended to be used on a stand-alone basis for risk attribution, clinical decision-making or otherwise preclude clinical assessment of CT studies.

HealthCCSng product is a software device that automatically estimates the coronary artery calcium category from non-cardiac-gated adult CT scans. The product is aimed to leverage the high utilization of CT scans in the medical care environment (both inpatient and outpatient), including lung cancer screening programs, in order to automatically detect calcification in the coronary arteries of patients in an opportunistic manner.

The HealthCCSng product analyzes cases using an artificial intelligence algorithm for the automated detection and estimation of coronary calcium and outputs a result for review by the clinician. The device works in parallel to and in conjunction with the standard of care workflow. The final diagnosis is made by the clinician after reviewing the scan independently of the software. The device is intended for use by the clinicians as a non-diagnostic analysis software in conjunction with additional patient information and professional judgment.

HealthCCSng receives a non-gated, non-contrast CT study from the storage application, Nanox AI's Imaging Analytics Platform (IMA)/ other platforms. For each CT study received, the software shall validate at least one compliant series in which the entire heart is present and performs an analysis. For each complaint study, the software shall output:

1. Estimated Coronary Calcium Detection, based on the measurement of calcium deposits in the coronary arteries.
2. A corresponding Estimated Coronary Calcium Detection Category, based on the Estimated Coronary Calcium measurements.

The software output will include the following calcium categories:

= 400 | High

For patients in which calcium was detected, the user will be presented with representative key images - all the slices containing the measured coronary calcifications (130 HU and above). On these images, the calcified areas will be annotated.

The following modules compose the HealthCCSng software:

1. Data input and validation: DICOM validation receives imaging study from hosting application and the validation feature assessed the input data (i.e. age, modality, view, etc.) to ensure compatibility for processing by the algorithm.
2. HealthCCSng algorithm: Once a study has been validated, the algorithm analyzes the CT for analysis and quantification.
3. IMA Integration feature: The results of a successful study analysis are provided to the hosting application.
4. Error codes feature: In the case of a study failure during data validation or the analysis by the algorithm, an error is provided to the system.

The product operates in the following manner:

1. A CT scan is sent to the Image Analytics Platform (IMA).
2. The Image Analytics Platform (IMA) forwards the scan to the HealthCCSng algorithm for analysis.
3. The scan is analyzed, with the following possible results sent back to IMA:
   a. Non-compliant: the scan is not compliant with the input criteria of the product and is therefore not analyzed.
   b. Success -
4. Success A Zero calcium, Low, Medium or High Coronary Calcium Detection has been identified.
5. Success No Category Cause: metal artifact(s) suggestive of known cardiac disease is suspected.
   c. Failure: the scan has been analyzed. An error prevented the device from completing the analysis and therefore there is no output available.

In all cases of success. HealthCCSng will provide key images of visualization of the detected coronary calcium (except for zero category), the category name and the exact score.

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

Acceptance Criteria and Device Performance

Additional Performance Metric:

• Pearson's Correlation Coefficient (GT vs. HealthCCSng scores): 0.959 (95% CI: [0.9499, 0.9655], p

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The HealthCCSng device is intended for use as a non-invasive post-processing software to evaluate calcified plaques in the coronary arteries, which present a risk for coronary artery disease. The software generates an estimated coronary artery calcium detection category.

The HealthCCSng device analyzes existing non-cardiac-gated CT studies that include the heart of adult patients above the age of 30. The device generates a three-category output representing the estimated quantity of calcium detected together with preview axial images of the detected calcium meant for informational purposes only. The device output will be available to the radiologist as part of their standard workflow. The HealthCCSng results are not intended to be used on a stand-alone basis for risk attribution, clinical decision-making or otherwise preclude clinical assessment of CT studies.

HealthCCSng product is a software device that automatically estimates the coronary arterv calcium category from non-cardiac-gated adult CT scans. The product is aimed to leverage the high utilization of CT scans in the medical care environment (both inpatient and outpatient), including lung cancer screening programs, in order to automatically detect calcification in the coronary arteries of patients in an opportunistic manner.

Zebra's HealthCCSng product analyzes cases using an artificial intelligence algorithm for the automated detection and estimation of coronary calcium and outputs a result for review by the radiologist. The device works in parallel to and in conjunction with the standard of care workflow. The final diagnosis is made by the radiologist after reviewing the scan independently of the software. The device is intended for use by the radiologists as a non-diagnostic analysis software in conjunction with additional patient information and professional judgment.

HealthCCSng receives a non-cardiac-gated CT study from the storage application, Zebra's Imaging Analytics Platform (IMA). For each CT study received, the software shall validate there is at least one compliant series in which the entire heart is present, and perform an analysis. For each compliant study, the software shall output:

1.Estimated Coronary Calcium Detection, based on the measurement of calcium deposits in the coronary arteries.
2. A corresponding Estimated Coronary Calcium Detection Category, based on the Estimated Coronary Calcium measurement.

The software output will include the following calcium categories:

| Estimated Coronary Calcium
Detection | Corresponding Estimated Coronary Calcium
Detection Category |
|-----------------------------------------|----------------------------------------------------------------|
| 0-99 | Low |
| 100-399 | Medium |
| ≥400 | High |

For patients in which calcium was detected, the user will be presented with representative images - all the slices containing the measured coronary calcifications (130 HU and above). On these images, the calcified areas will be annotated (with an option for the user to toggle on and off the annotation).

The following modules compose the HealthCCSng software:

Data input and validation: Following retrieval of a study, the validation feature assessed the input data (i.e. age, modality, view, etc.) to ensure compatibility for processing by the algorithm.

HealthCCSng algorithm: Once a study has been validated, the algorithm analyzes the CT for analysis and quantification.

IMA Integration feature: The study analysis and the results of a successful study analysis is provided to IMA.

Error codes feature: In the case of a study failure during data validation or the analysis by the algorithm, an error is provided to the system.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided FDA 510(k) summary for HealthCCSng:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: 447 anonymized CT chest cases.
• Data Provenance: Retrospective study from two healthcare institutions, composed of multiple clinical sites. The specific country of origin is not mentioned.

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

• Number of Experts: Three radiologists.
• Qualifications: The document does not specify the qualifications (e.g., years of experience, subspecialty) of these radiologists.

4. Adjudication Method for the Test Set

• Adjudication Method: "Majority agreement of two of three radiologists" was used to determine the ground truth category. This is a 2-out-of-3 majority consensus method.

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

• Was an MRMC study done? The document describes a standalone performance study comparing the device's output to ground truth. It does not mention a comparative effectiveness study involving human readers with and without AI assistance to measure an effect size.

6. Standalone (Algorithm Only) Performance

• Was a standalone study done? Yes, the document explicitly states: "The HealthCCSng software device performance was validated in a stand-alone retrospective study for its overall agreement compared to the established ground truth..."

7. Type of Ground Truth Used

• Type of Ground Truth: Expert consensus. Specifically, the "ground truth category was determined by the majority agreement of two of three radiologists."

8. Sample Size for the Training Set

• The document does not specify the sample size for the training set. It only details the validation (test) set.

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

• The document does not specify how the ground truth for the training set was established. It only describes the ground truth establishment for the validation (test) set. It's common for training data ground truth to be established through expert annotations, but the method is not stated here.

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