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The GUS ASTRA TEE™ automated reprocessor facilitates high-level disinfection and rinsing of 1 or 2 transsophageal ultrasound probes using FDA cleared and PCI Medical approved high-level liquid disinfectants.

The GUS ASTRA VR™ automated reprocessor facilitates high-level disinfection and rinsing of 1 or 2 endovaginal and/or endorectal ultrasound probes using FDA cleared and PCI Medical 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 GUS ASTRA TEE and GUS ASTRA VR are automated reprocessors which are identical with the exception of the disinfection chamber configuration necessary to compensate for two different types of ultrasound probes. The GUS ASTRA TEE is an automated reprocessor which enables high-level disinfection and rinsing for transesophageal (TEE) ultrasound probes. The GUS ASTRA VR is an automated reprocessor which enables high-level disinfection and rinsing for endovaginal and/or endorectal ultrasound probes.

Both ASTRA systems utilize predefined programs based upon the high-level liquid disinfectant (HLD) used during the disinfection and potable water rinsing cycles. The system controls the HLD temperature and cycle times which are fixed, no user configuration is available. The device will perform the defined automated disinfection and rinsing cycles based upon the HLD specifications. The microprocessor controlled interface monitors temperature and liquid levels of the HLD in the disinfection chamber and the reservoir. Optical sensors are used for HLD reservoir presence and probe identification. Additional user inputs include unique probe identification (for disinfection records) and the results entry of the Minimum Recommended Concentration (MRC) test using test strips and minimum concentration levels as defined by the HLD manufacturer. A sensor located on the disinfection chamber door is used to initiate the cycle after the user has input the required information. User retrievable data/information regarding reprocessing is accessible through a USB port.

This appears to be a 510(k) premarket notification for a medical device, specifically automated reprocessors for ultrasound probes. The document focuses on demonstrating substantial equivalence to a predicate device ("TD-100 Transesophageal Probe Disinfector").

The information provided does not describe an AI/ML-based device, a comparative effectiveness study with human readers, or an algorithm-only standalone study. Therefore, sections pertaining to these aspects (2, 3, 4, 5, 6, 7, 8, 9) cannot be answered from the provided text.

The acceptance criteria relate to the physical and functional performance of the device, focusing on its ability to control the high-level disinfectant (HLD) process and user safety. The "study" mentioned refers to verification and validation testing to ensure the device meets these performance specifications and is substantially equivalent to the predicate.

Here's the breakdown of the information that can be extracted:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document
does not specify a "sample size" in the context of typical AI/ML testing (e.g., number of cases or images). The testing appears to be primarily laboratory-based and simulated use testing of the device itself.
Data provenance is not explicitly stated as country of origin, but the testing was conducted to U.S. and international standards (e.g., UL, ISO, EN, ANSI/AAMI/IEC) for medical devices. The nature of the testing suggests it is prospective in the sense of verifying device performance against established standards.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable. Ground truth in this context relates to the validated performance criteria of the disinfection process, not expert-labeled medical data.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable. This is not a study involving human reader interpretation or adjudication.

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
No, this type of study was not done. The device is an automated reprocessor, not an AI diagnostic aid.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
Not applicable. The device is a physical reprocessor, not an algorithm. The device's "standalone" performance is assessed through its ability to independently control the disinfection process parameters.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)
The "ground truth" for this device's performance is derived from:

• HLD (High-Level Disinfectant) manufacturer specifications: These define the required time and temperature for effective disinfection.
• Industry standards: Such as ISO 10993-5 (cytotoxicity), UL 61010-1/CSA C22.2 No. 61010-1 (electrical safety), EN 61326-1 (EMC), ANSI/AAMI/IEC 62304 (Medical Device Software).
• Microbiological performance standards: e.g., achieving ≥6 Log reduction of M. terrae.

8. The sample size for the training set
Not applicable. This is not an AI/ML device that requires a training set.

9. How the ground truth for the training set was established
Not applicable, as there is no training set for an AI/ML model for this device.

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