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DEEPVESSEL FFR is a coronary physiological simulation software for the clinical quantitative analysis of previously acquired Computed Tomography DICOM data for clinically stable symptomatic patients with coronary artery disease. It provides DVFFR (a CT-derived FFR measurement) computed from static coronary CTA images using deep learning neural networks that encode imaging, structural, and functional characteristics of coronary arteries through learning.

DEEPVESSEL FFR analysis is intended to support the functional evaluation of coronary artery disease.

The results of the analysis are provided to support qualified clinicians to aid in the evaluation and assessment of coronary arteries. DEEPVESSEL FFR results are intended to be used by qualified clinicians in conunction with the patient's clinical history, symptoms, and other diagnostic tests, as well as the clinician's professional judgment.

DEEPVESSEL FFR is a coronary physiological simulation software for the clinical quantitative and qualitative analysis of previously acquired Computed Tomography DICOM data for clinically stable symptomatic patients with coronary artery disease. It estimates FFR values from static coronary CTA images with extracted coronary tree structures using deep learning neural networks. DEEPVESSEL FFR analysis is intended to support the functional evaluation of CAD.

The software processes these images semi-automatically, and it generates a 3D model of the coronay artery tree and computes DVFFR (CT-derived FFR) values. Qualified image analysts interact with the software by providing manual edits to the 3D coronary artery tree segmentations when needed, and oversees outputs along the processing steps. DVFFR analysis results are sent electronically to the physicians via a third-party service portal application.

DVFFR software is independent of imaging equipment, imaging protocols and equipment vendors; the clinical validation study report includes the specific imaging scanner types and imaging acquisition parameters used in the clinical validation of the product.

Here's a summary of the acceptance criteria and study details for the DEEPVESSEL FFR device, based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

Additional Reported Performance (Per-vessel):

• Accuracy: 86.8% (95% CI: 83.0%-90.4%)
• PPV: 79.4% (95% CI: 71.8%-86.2%)
• NPV: 91.9% (95% CI: 87.7%–95.6%)

Patient-level Diagnostic Performance:

• Sensitivity: 87.4% (95% CI: 79.4%-93.1%)
• Specificity: 83.7% (95% CI: 76.5%-89.4%)
• Accuracy: 85.2% (95% CI: 80.2%-89.4%)
• PPV: 79.6% (95% CI: 71.0%-86.6%)
• NPV: 90.1% (95% CI: 83.6%-94.6%)

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

• Sample Size: 244 patients with 311 target vessels.
• Data Provenance: Multi-national (US and EU), multi-center clinical validation study, conducted at 8 clinical sites (4 from EU and 4 from US). The study was prospective in its design to evaluate the device against an invasive reference standard, suggesting the data used for validation was gathered for this specific purpose.

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

The document does not explicitly state the number of experts or their specific qualifications for establishing the ground truth. However, the ground truth was "invasive FFR measurement." Invasive FFR procedures are typically performed and interpreted by interventional cardiologists.

4. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method (e.g., 2+1, 3+1). The ground truth was established by "invasive FFR measurement," which is a direct physiological measurement, lessening the need for expert adjudication of the ground truth itself, though interpretation of the invasive FFR values might have involved standard clinical protocols.

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

No, an MRMC comparative effectiveness study that demonstrates how much human readers improve with AI vs. without AI assistance was not reported in this summary. The clinical study focused on the standalone performance of the DEEPVESSEL FFR device compared to invasive FFR. The device is described as supporting qualified clinicians and being used in conjunction with their judgment and other tests, but no study on human reader improvement with assistance was included.

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

Yes, a standalone performance study was done. The reported sensitivity, specificity, accuracy, PPV, and NPV values reflect the performance of the DEEPVESSEL FFR software (DVFFR) itself in detecting ischemic conditions compared to invasive FFR. While image analysts perform semi-automatic processing and manual edits to 3D coronary artery tree segmentations, the diagnostic performance metrics provided represent the device's output.

7. The Type of Ground Truth Used

The ground truth used was invasive FFR measurement. This is considered a direct physiological measurement and a gold standard for assessing the hemodynamic significance of coronary artery stenoses.

8. The Sample Size for the Training Set

The document does not specify the sample size for the training set. It mentions that the device uses "deep learning neural networks that encode imaging, structural, and functional characteristics of coronary arteries through learning," but provides no details on the data used for this learning phase.

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 mentions the use of deep learning neural networks. Typically, for such devices, the training data's ground truth would also be established by invasive FFR measurements or a similar reference standard.

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