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For the ASTRA TEE® -

The ASTRA TEE® automated reprocessor facilitates high-level disinfection and rinsing of 1 or 2 transesophageal ultrasound probes using FDA cleared and Civco Medical Solutions approved high-level liquid disinfectants.

For the ASTRA VR™ -

The ASTRA VR™ automated reprocessor facilitates high-level disinfection and rinsing of 1 or 2 endovaginal/endorectal ultrasound probes using FDA cleared and Civco Medical Solutions approved high-level liquid disinfectants.

The ASTRA Product Family of automated reprocessors are floor standing electromechanical lab equipment that facilitate high-level liquid disinfection of ultrasound probes that have been enzymatically pre-cleaned.

The provided document is a 510(k) Pre-Market Notification for medical devices (ASTRA TEE® Transesophageal Probe Reprocessor and ASTRA VR™ Endovaginal/Endorectal Probe Reprocessor). It outlines the device description, intended use, and a comparison to predicate devices, focusing on demonstrating substantial equivalence. The document primarily discusses the device's technical specifications and the testing conducted to ensure its safety and effectiveness in controlling high-level disinfection processes.

Unfortunately, the provided document does not contain the specific details required to answer all parts of your request about acceptance criteria met by a "device" in the context of an AI/human-in-the-loop study. The device in question here is an automated reprocessor for ultrasound probes, not an AI-powered diagnostic tool, and the performance criteria are related to disinfection efficacy, not diagnostic accuracy.

Therefore, I cannot populate most of the requested table and details, as they pertain to performance metrics and study designs common for AI-based diagnostic devices (e.g., sensitivity, specificity, reader performance studies, expert adjudication for ground truth).

However, I can extract the information relevant to the device's operational performance and the studies conducted to support its substantial equivalence.

Here's what can be extracted and what cannot be from the provided text:

What can be extracted:

• Acceptance Criteria (Performance Attributes): The core performance attribute for this device is its ability to "control the HLD temperature, soak time, and rinse cycle to facilitate ≥6 Log reduction of M. terrae using Metricide OPA Plus, Cidex OPA, and Resert XL." This is the primary functional performance claim.
• Study That Proves the Device Meets Acceptance Criteria:
  • Performance Testing Summary:
    • "The ASTRA system maintains time and temperature required for disinfection as per HLD manufacturers' specifications."
    • "The ASTRA system passed Safety and EMC testing."
    • "The ASTRA system passed Simulated use and Residual testing."
    • "The ASTRA system passed In-use testing."
  • Efficacy (Disinfection and Residual testing): "Pass as per simulated use (6log reduction of M. terrae), In-use Testing (complete kill) and residual testing (ISO10993-5 cytotoxic effect of <= 2)."
  • Ground Truth Type for Efficacy: The efficacy is measured against a "6 log reduction of M. terrae" (a microbial standard) and "complete kill" for in-use testing. "Residual testing" uses "ISO10993-5 cytotoxic effect of <= 2." This indicates microbiological testing standards and biocompatibility standards are used as ground truth.
  • Ground Truth for Functionality: The device controls time, temperature, and rinse cycles "as per HLD manufacturers' specifications." This implies that the ground truth for these parameters is the specified parameters from the high-level disinfectant manufacturers.

What cannot be extracted (and why, given the nature of the device):

• Reported Device Performance (in the context of AI metrics): The document reports passing tests, but not specific percentages for sensitivity, specificity, or AUC, as these are not relevant metrics for a disinfection reprocessor.
• Sample size used for the test set and data provenance (e.g., country of origin, retrospective/prospective): The document mentions "Simulated use," "In-use testing," "Safety and EMC testing," "Residual testing," but does not provide specific sample sizes (e.g., number of probes, cycles, test samples) or the data provenance details (country, retrospective/prospective).
• Number of experts used to establish ground truth and qualifications: This is irrelevant for a device that performs automated disinfection based on physical parameters and microbiological efficacy. There are no human experts "reading" or adjudicating the output for diagnostic purposes.
• Adjudication method for the test set: Not applicable.
• Multi-Reader Multi-Case (MRMC) comparative effectiveness study: Not applicable. This device does not involve human readers interpreting images.
• Standalone (algorithm only without human-in-the-loop performance) was done: The device itself is an automated system; its performance is "standalone" in the sense that it operates without continuous human interaction during the disinfection cycle. However, this is not in the context of an AI algorithm's diagnostic performance.
• Sample size for the training set: Not applicable, as this is not an AI/machine learning model that undergoes a "training" phase. The device is a fixed-function electro-mechanical system.
• How the ground truth for the training set was established: Not applicable.

Based on the available information, here is the breakdown:

1. A table of acceptance criteria and the reported device performance

2. Sample size used for the test set and the data provenance
The document does not specify the sample sizes for the "Simulated use," "In-use testing," "Safety and EMC testing," or "Residual testing." It does not provide information on data provenance (e.g., country of origin, retrospective/prospective). These types of details are less common for an electro-mechanical device validation compared to a clinical study for a diagnostic AI.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This is not applicable. The ground truth for this device's performance is based on established engineering, microbiological, and biocompatibility standards, not human expert interpretation of data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. There is no human adjudication process involved in assessing the device's disinfection performance.

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
Not applicable. This device is not an AI diagnostic tool and does not involve human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device itself is an automated system for high-level disinfection. Its performance metrics (time control, temperature control, log reduction of M. terrae, residual levels) are inherently "standalone" in function, as it performs its designated tasks without continuous human intervention. This is not in the context of an AI algorithm's diagnostic performance without human input.

7. The type of ground truth used
The ground truth used for this device's performance validation includes:
* Microbiological standards: Achieving a "≥6 Log reduction of M. terrae" and "complete kill" in simulated and in-use testing for disinfection efficacy.
* Biocompatibility standards: ISO10993-5 for cytotoxic effect.
* Manufacturer specifications: Adherence to temperature, time, and rinse parameters defined by the High-Level Disinfectant (HLD) manufacturers.
* Safety and Electrical standards: UL 61010-1/CSA C22.2 No. 61010-1, EN 60601-1-2 / EN 61326-1 for electrical safety and electromagnetic compatibility.
* Quality System and Risk Management Standards: 21 CFR Part 820.30 Design Controls, ISO 14971, ANSI/AAMI/IEC 62304 for software, ANSI/AAMI HE75 for human factors.

8. The sample size for the training set
Not applicable. This is not an AI/machine learning model.

9. How the ground truth for the training set was established
Not applicable.

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