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Vitrea CT Brain Perfusion is a non-invasive post-processing application designed to evaluate areas of brain perfusion. The software can calculate cerebral blood flow (CBF), cerebral blood volume (CBV), local bolus timing (i.e., delay of tissue response, time to peak), and mean transit time (MTT) from dynamic CT image data acquired after the injection of contrast media. The package also allows the calculation of regions of interest and mirrored regions, as well as the visual inspection of time density curves. Vitrea CT Brain Perfusion supports the physician in visualizing the apparent blood perfusion in brain tissue affected by acute stroke. Areas of decreased perfusion, as is observed in acute cerebral infarcts, appear as areas of changed signal intensity (lower for both CBF and CBV and higher for time to peak and MTT).

Vitrea CT Brain Perfusion is a noninvasive post-processing software that calculates cerebral blood flow (CBF), cerebral blood volume (CBV), local bolus timing (i.e., delay of tissue response, time to peak), and mean transit time (MTT) from dynamic CT image data. It displays time density curves, perfusion characteristics in perfusion and summary maps, as well as regions of interest and mirrored regions.

Here's an analysis of the acceptance criteria and study information for the Vitrea CT Brain Perfusion device, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state quantitative acceptance criteria or a direct performance table for the Vitrea CT Brain Perfusion device with the Bayesian algorithm. Instead, the document focuses on demonstrating substantial equivalence to a previously cleared predicate device (Vitrea CT Brain Perfusion with SVD+ algorithm, K121213) and reference device (Olea Sphere V3.0, K152602) due to the addition of a Bayesian algorithm.

The core "acceptance" is based on the conclusion that the new device is "as safe and effective" as the predicate. This is supported by:

• "Algorithm Testing": "The Vitrea CT Brain Perfusion Bayesian algorithm has passed all the verification and validation and is therefore considered validated and acceptable."
• "External Validation": "Based on the scores provided by the physicians, Vital concluded the Brain Perfusion with Bayesian algorithm is as safe and effective as the already cleared Brain Perfusion with SVD+ algorithm and fulfills its intended use."

While direct numerical performance metrics are not given, the implicit acceptance criteria are that the device's output (CBF, CBV, TTP, MTT maps and calculations) is comparable and clinically acceptable to that generated by the predicate device, especially in its ability to support the physician in visualizing perfusion in acute stroke.

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

The document mentions "External Validation" where "physicians evaluated if the Brain Perfusion with Bayesian algorithm (subject device) was substantially equivalent with the Brain Perfusion with SVD+ algorithm (K121213, predicate device)." However, the specifics of the test set, including:

• Sample size: Not explicitly stated (e.g., number of patients/cases).
• Data provenance (country of origin, retrospective/prospective): Not explicitly stated.

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

• Number of experts: Not explicitly stated how many "physicians" were involved in the "External Validation."
• Qualifications of experts: The document only refers to them as "physicians." Specific qualifications (e.g., "radiologist with 10 years of experience") are not provided.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1) for establishing ground truth or evaluating the physician scores in the "External Validation." It simply states that "Based on the scores provided by the physicians, Vital concluded..."

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

• Was an MRMC study done? While an "External Validation" involving "physicians" was performed, the document does not explicitly label it as a formal MRMC comparative effectiveness study in the way this term is typically used for AI-assisted workflows (i.e., comparing human readers with and without AI assistance). The focus was on comparing the Bayesian algorithm's outputs to the predicate's SVD+ algorithm's outputs, as judged by physicians.
• Effect size of human improvement with AI vs. without AI assistance: Not reported, as the study was not framed as a human-in-the-loop improvement study.

6. Standalone (Algorithm Only) Performance

• Was a standalone study done? Yes, the document heavily implies that the "Algorithm Testing" and subsequent "External Validation" primarily focused on the standalone performance of the Bayesian algorithm in generating perfusion maps and calculations. The "External Validation" specifically assessed if the "Brain Perfusion with Bayesian algorithm (subject device) was substantially equivalent with the Brain Perfusion with SVD+ algorithm (K121213, predicate device)." This indicates an evaluation of the algorithm's output itself.

7. Type of Ground Truth Used

The "ground truth" for the external validation appears to be the expert consensus/clinical judgment of the participating "physicians" who evaluated the outputs of the Bayesian algorithm compared to the SVD+ algorithm of the predicate device. There is no mention of pathology or outcomes data being used as ground truth for this particular evaluation.

8. Sample Size for the Training Set

The document does not provide any information regarding the sample size or characteristics of the training set used for the Bayesian algorithm. As this is a 510(k) for a software update (addition of a new algorithm) to an already cleared device, the submission focuses on demonstrating the safety and effectiveness of the change relative to the predicate, rather than fully detailing the original algorithm development.

9. How Ground Truth for the Training Set Was Established

Since information on the training set itself is not provided, the method for establishing its ground truth is also not described in this document.

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