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The M1026A Anesthesia gas Module is intended to measure and monitor anesthesia gas contents in the ventilation circuitry of a patient and to provide this data to health care professionals in form readings, waves and alarms, via the Component Monitoring System, for the support of clinical decision making.

The device is indicated for use in health care facilities by health care professionals whenever there is a need for adult, pediatric and neonate patient anesthesia gas monitoring.

The above device operates with Agilent Technologies Anesthesia Component Monitoring System (ACMS) and the Viridia 24 Agilent Technologies Model 1204A through a digital interface (RS232). The monitoring system is known as the Model M1166A Component Monitoring System (CMS). When coupled with the above ACMS, the device will measure and display respiratory gases and anesthetic agents of ventilated patients. The device will signal physiological alarms and document deviations when preset limits are exceeded. An INOP ("inoperative") alarm is triggered and a message is displayed in the event of malfunction, lack of detectable breath, power disconnects, and other inoperative states.

This 510(k) summary does not contain the kind of detailed information typically found in a study proving a device meets acceptance criteria, particularly for AI/Machine Learning devices. This document is for a medical device that measures physiological parameters. Therefore, many of the requested fields are not applicable or cannot be extracted from the provided text.

Here is an analysis based on the provided text, indicating where information is not present:

Acceptance Criteria and Device Performance Study for Agilent Technologies M1026A Anesthetic Gas Monitor Option C05

This 510(k) submission describes the Agilent Technologies M1026A Anesthetic Gas Monitor Option C05, intended for measuring and displaying respiratory gases and anesthetic agents. The submission primarily focuses on demonstrating substantial equivalence to previously cleared devices (K951127 and K982619) rather than a de novo study against specific acceptance criteria for performance metrics like sensitivity, specificity, or agreement.

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not include a table of acceptance criteria or specific performance metrics (e.g., accuracy, precision, bias) for the M1026A Anesthetic Gas Monitor. Such performance data would typically be found in test reports or validation studies, which are not part of this 510(k) summary. The submission asserts that the device operates with similar technological characteristics to its predicate device. It states concentrations are calculated by non-dispersive infrared analysis and O2 measurement by a paramagnetic transducer.

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

The document does not mention a specific test set, its sample size, or data provenance (e.g., country of origin, retrospective/prospective). Substantial equivalence claims for physiological monitoring devices typically rely on bench testing, comparison to predicate devices, and sometimes animal or human volunteer studies, but details are not included here.

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

This information is not applicable and not provided. This device is a measurement device for gas concentrations, not a diagnostic imaging or AI-based system requiring expert interpretation or ground truth establishment in the traditional sense for diagnostic accuracy studies. Ground truth for such devices is usually established through validated reference gas mixtures or laboratory equipment.

4. Adjudication method for the test set

This information is not applicable and not provided for the same reasons as point 3.

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

An MRMC study is not applicable and not mentioned. This device is a gas monitor, 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

This concept is not applicable in the context of this device. The device itself is a "standalone" monitor in that it measures and displays physiological data; it is not an algorithm that requires separate standalone performance testing in the way an AI diagnostic algorithm would. Its performance characteristics are inherent to its sensors and processing.

7. The type of ground truth used

The document does not explicitly state the type of ground truth used. For gas analyzers, ground truth would typically be established using:

• Certified reference gas mixtures (NIST-traceable): Precisely known concentrations of the target gases used for calibration and accuracy verification.
• Validated laboratory reference instruments: High-precision gas analyzers used to establish true concentration values.

8. The sample size for the training set

This information is not applicable and not provided. This device is a physiological monitor based on established measurement principles (non-dispersive infrared analysis, paramagnetic transduction), not a machine learning or AI algorithm that requires a "training set" in the computational sense.

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

This information is not applicable and not provided for the reasons stated in point 8.

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