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The A5 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A5 is intended for use by licensed clinicians, for patients requiring anesthesia within a health care facility, and can be used in adult and pediatric (including neonate, infant, child and adolescent) populations.

The A5 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A5 incorporates 02, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Enflurane, Sevoflurane and Halothane).

This document is a 510(k) summary for the A5 Anesthesia System. It describes the device, its intended use, and compares it to a predicate device and several reference devices to demonstrate substantial equivalence.

Here's a breakdown of the requested information based on the provided text:

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

The document does not explicitly present acceptance criteria in a table format with specific quantitative thresholds. Instead, it lists various "Nonclinical testing and Performance" areas that were tested and states that "The functional and system level testing showed that the device continues to meet specifications and the performance of the device is equivalent to the predicate."

However, the "Device Comparison Table" on pages 7 and 8 provides a comparison of technical characteristics between the subject device (A5 Anesthesia System) and the predicate device (A5 Anesthesia Delivery System (K123211)). This table implies that the performance of the subject device in these listed characteristics meets or is equivalent to the predicate device, which can be interpreted as the acceptance criteria being parity with the predicate.

Here's an attempt to construct a table based on the provided information, interpreting the predicate device's specifications as the acceptance criteria for the subject device:

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 lists "Nonclinical testing and Performance" areas like:

• Software (Unit testing, Integration testing, System testing)
• Performance
• Power Supply
• Thermal
• Cleaning and Disinfection
• Fresh Flow Optimizer
• AG Module
• Heating Module
• Waveform Comparison
• Biocompatibility (Volatile Organic Compounds, Particulate Testing, Cytotoxicity, Sensitization, Irritation / intracutaneous reactivity, Extractables and leachables (E&L) testing, Inorganic gases testing)
• Human Factors Validation Testing
• Testing as per consensus standards (AAMI/ANSI ES60601-1, IEC 60601-1-2, ISO 80601-2-13, ISO 80601-2-55, ASTM F1101-90, AIM 7351731)

However, the document does not specify:

• The sample size used for any of these tests.
• The data provenance (e.g., country of origin, retrospective or prospective nature of the data if any clinical data was implied).

The testing appears to be mostly focused on hardware and software functionality, performance, and safety against specified standards, rather than clinical trial data involving patient samples or expert interpretations.

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)

This information is not provided in the document. The testing described is primarily non-clinical (engineering, software, biocompatibility, standards compliance). There is no mention of "ground truth" being established by experts in the context of clinical interpretation, as might be found in studies for diagnostic AI devices.

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

This information is not provided in the document. Given the nature of the tests (non-clinical performance, safety, and standards compliance), an adjudication method as typically used for clinical endpoints or image interpretation is not applicable or 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

A multi-reader multi-case (MRMC) comparative effectiveness study was not conducted or described. This type of study is relevant for AI-powered diagnostic or decision support tools that assist human readers, which is not the primary function of this anesthesia system.

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

The device is an "Anesthesia System" which delivers anesthetic vapor and provides ventilation. It includes an "Anesthetic Gas Module" for monitoring, but it is not an "algorithm only" device operating without human-in-the-loop. Its performance, as described by the parameters in the comparison table, would be standalone in terms of its ability to measure and deliver gases accurately, but it functions as part of a system used by clinicians. There is no mention of a standalone algorithm's performance in isolation from the hardware components.

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

For the non-clinical tests (software, performance, electrical safety, biocompatibility, etc.), the "ground truth" would be defined by engineering specifications, international consensus standards, and predicate device performance. For example, the accuracy of gas measurement (e.g., CO2 accuracy 0 to 1%: +/-.1%) would be tested against calibrated references, where the reference measurement itself serves as the ground truth. There is no mention of clinical ground truth types like pathology or outcomes data.

8. The sample size for the training set

This information is not applicable and not provided. The A5 Anesthesia System is a traditional medical device (hardware and software for life support functions and monitoring), not an AI/Machine Learning model that requires a "training set" in the computational sense. The software testing mentioned (Unit, Integration, System) refers to traditional software development and verification, not machine learning model training.

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

This information is not applicable and not provided for the same reasons as point 8.

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The A7 Anesthesia System is a device used to administer to a patient, continuously or intermittently, a general inhalation anesthetic and to maintain a patient's ventilation.

The A7 is intended for use by licensed clinicians, for patients requiring anesthesia within a health care facility, and can be used in adult and pediatric (including neonate, infant, child and adolescent) populations.

The A7 Anesthesia System is a continuous flow inhalation gas anesthesia system that delivers anesthetic vapor and provides for automatic and manual modes of ventilation. The A7 incorporates 02, CO2, N2O and Agent concentration monitoring (Desflurane, Isoflurane, Enflurane, Sevoflurane and Halothane).

This document refers to the Mindray A7 Anesthesia System (K171292). It outlines its indications for use, device description, and a comparison to a predicate device (K151954), along with reference devices, to demonstrate substantial equivalence.

Here's an analysis of the acceptance criteria and study information based on the provided text:

Summary of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the Mindray A7 Anesthesia System are implicitly derived from its comparison to a predicate device (also an A7 Anesthesia System, K151954) and several reference devices. The core principle is "substantial equivalence," meaning the new device performs at least as well as, or comparably to, the established devices.

The table below summarizes the key technical characteristics and their reported performance/specifications for both the subject device and its direct predicate. Since the document states "The functional and system level testing showed that the device continues to meet specifications and the performance of the device is equivalent to the predicate," the performance of the subject device is presented as matching the predicate.

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