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The HeartFlow Analysis is an AI-based medical device software for the clinical quantitative and qualitative analysis of previously acquired Computed Tomography DICOM data for patients with suspected coronary artery disease. It provides anatomic data, plaque identification and characterization, as well as the calculations of FFRCT, a coronary physiological simulation, computed from simulated pressure, velocity and blood flow information obtained from a 3D computer model generated from static coronary CT images. The HeartFlow Analysis is intended to support the risk assessment and functional evaluation of coronary artery disease.

The HeartFlow Analysis is provided to support qualified clinicians to aid in the evaluation and risk assessment of coronary artery disease. The HeartFlow Analysis is intended to be used by qualified clinicians in conjunction with the patient's clinical history, symptoms, and other diagnostic tests, as well as the clinician's professional judgment.

The HeartFlow Analysis is an Al-based medical device software developed for the clinical quantitative and qualitative analysis of CT DICOM data. It is a tool for the analysis of CT DICOM-compliant cardiac images and data, to assess the anatomy and function of the coronary arteries in the risk stratification and evaluation of coronary artery disease.

The software displays coronary and functional information using graphics and text, including computed and derived quantities of percent stenosis, plaque volumes, blood flow, pressure and velocity, to aid the clinician in the assessment and treatment planning of coronary artery disease.

The HeartFlow Analysis is performed on previously physician-acquired image data and is unrelated to acquisition equipment and clinical workstations.

Here's a breakdown of the acceptance criteria and study information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific acceptance criteria with numerical targets or thresholds. It generally discusses "validation studies including stress testing, and repeatability testing to ensure the safety and effectiveness of the device" and that "results concluded the device was acceptable for use."

However, based on the context of the device and its intended use, we can infer general performance areas. Since no specific acceptance criteria are given, the "Reported Device Performance" column will reflect the general conclusion from the document.

2. Sample Size for Test Set and Data Provenance

• Sample Size for Test Set: The document states that testing and evaluation used "previously acquired diagnostic images received through HeartFlow sponsored clinical trials." However, a specific number for the test set sample size is not provided.
• Data Provenance: The data was "previously acquired diagnostic images received through HeartFlow sponsored clinical trials." This suggests the data is retrospective (already acquired) and likely originates from various clinical trial sites, but specific countries are not mentioned.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not specify the number of experts used or their qualifications for establishing ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method (e.g., 2+1, 3+1).

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

The provided text does not mention if an MRMC comparative effectiveness study was done, nor does it specify an effect size of human readers' improvement with AI vs. without AI assistance. The focus is on the device's standalone validation.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (algorithm only) performance assessment was done. The document states:

• "The software was designed, developed, tested and validated according to written procedures."
• "Validation studies included stress testing, and repeatability testing to ensure the safety and effectiveness of the device."
• "Medical device design validation has been completed. Medical device design included testing and evaluation using previously acquired diagnostic images received through HeartFlow sponsored clinical trials."
• "Summaries of pre-clinical studies were reviewed as part of a prior predicate review (K161772, the original predicate of K182035/K190925/K203329). The results concluded the device was acceptable for use."

This indicates that the device's performance was evaluated independently without human intervention during the "testing and evaluation" phase described, proving its standalone capabilities.

7. Type of Ground Truth Used

The document implies that the ground truth was established through clinical diagnosis and evaluation of the "previously acquired diagnostic images" from HeartFlow-sponsored clinical trials. While it doesn't explicitly state "expert consensus," this is the most likely method for establishing ground truth in clinical trials concerning coronary artery disease diagnoses from images. No mention of pathology or outcomes data as direct ground truth is made in this specific excerpt for the validation studies mentioned.

8. Sample Size for the Training Set

The document states: "The core technology remains unchanged from the primary predicate and continues to be trained using deep learning (AI and machine learning) since 2015, to incorporate learnings from the volumes of CT data and studies."

However, a specific sample size for the training set is not provided. It only mentions "volumes of CT data and studies."

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

The document implies that the ground truth for the training set was established through "learnings from the volumes of CT data and studies." Similar to the test set, this would likely involve expert interpretation and analysis of the CT data used for training the deep learning algorithms, reflecting accepted clinical diagnoses and findings within those studies. No further details are given.

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