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Varia-NCI is a stand-alone software accessory to a Transcranial Doppler Ultrasound device (TCD) that retrieves, analyzes, and displays Cerebral Blood Flow velocity (CBFv) data from a Transcranial Doppler Ultrasound device. Varia-NCI uses CBFv data to measure the variability of a patient's cerebral blood flow velocity.

Varia-NCI is to be used by clinicians managing head trauma in the ICU, Surgical Unit, Emergency Department, and Clinical and Sports Medicine Settings.

Varia-NCI is a stand-alone software accessory to a Transcranial Doppler Ultrasound device (TCD) that retrieves, analyzes, stores, and displays Cerebral Blood Flow velocity (CBFv) data from a Transcranial Doppler Ultrasound device. Varia-NCI uses CBFv data to measure the variability of a patient's cerebral blood flow velocity. Varia-NCI accesses data from Compumedics Germany QL 3.0 software. QL 3.0 is a trade mark of PC-based software supplied by Compumedics Germany, GmbH and included with their digital Transcranial Doppler (TCD) Ultrasound device.

The provided text describes a 510(k) premarket notification for a device named Varia-NCI. However, it does not include a detailed acceptance criteria table, nor a study proving the device meets specific performance criteria in the way typically required for AI/ML-based diagnostic devices (e.g., sensitivity, specificity, AUC).

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device (Compumedics Germany – Doppler-Box X) largely through non-clinical software testing and the absence of clinical testing requirements given its role as a software accessory and the established safety and effectiveness of the predicate.

Based on the provided information, here's a breakdown of what can be extracted and what is missing concerning your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document provides a list of non-clinical software tests performed and their outcomes, indicating that the device "passed" each test. It does not provide quantitative performance metrics (e.g., accuracy, sensitivity, specificity) against specific numerical acceptance criteria.

2. Sample size used for the test set and the data provenance

The document details non-clinical software testing. It does not specify a "test set" in the context of clinical data or patient samples. The testing appears to have been performed using simulated or representative data relevant to software functions (e.g., "CBFv exp files were entered into the database"). Therefore, information regarding data provenance (country of origin, retrospective/prospective) is not applicable as described for a clinical test set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

This information is not provided. The testing described is software functionality testing, not a clinical study requiring expert-established ground truth on patient data.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set

Not applicable, as no clinical test set requiring expert adjudication is described.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

There is no mention of an MRMC study or any study involving human readers and AI assistance. The device is a software accessory that processes and displays data, not an AI-assisted diagnostic tool for human readers.

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

The software testing described is a form of standalone performance evaluation for the algorithm's functions. The "Test Passed" outcomes for "Calculation Testing and Display results" and "System Verification/Validation Performance Testing" demonstrate the algorithm's ability to accurately calculate and display CBFv variability. However, these are functional tests, not a clinical performance study using patient outcomes.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

For the software testing, the "ground truth" implicitly refers to the expected behavior and correct outputs of the software's functions, as defined by its specifications. For instance, in "Calculation Testing," the ground truth would be the accurately pre-computed or theoretically expected variability values against which the software's calculations were validated. It is not expert consensus, pathology, or outcomes data.

8. The sample size for the training set

Not applicable. The document does not describe an AI/ML model that requires a training set in the conventional sense. Varia-NCI is described as software that "retrieves, analyzes, and displays Cerebral Blood Flow velocity (CBFv) data" and "uses CBFv data to measure the variability of a patient's cerebral blood flow velocity." This implies deterministic algorithms rather than a machine learning model that would be "trained."

9. How the ground truth for the training set was established

Not applicable, as there is no mention of a training set for an AI/ML model.

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