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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Intended for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in healthcare facilities. The MP40 and MP50 are additionally intended for use in transport situations within healthcare facilities.

ST Segment monitoring is restricted to adult patients only.

The transcutaneous gas measurement (tcp02) is restricted to neonatal patients only.

The Philips MP40, MP50, MP60, MP70, MP80 and MP90 IntelliVue Patient Monitors. The modification is the introduction of Release D.00 software for the IntelliVue patient monitor devices, MP40, MP50, MP60, MP70, MP80, and MP90.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Philips IntelliVue Patient Monitors, Release D.00:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not contain a specific table of numerical acceptance criteria or detailed performance metrics. Instead, it offers a general statement about meeting "specifications cleared for the predicate device." Therefore, the table below reflects this general statement.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify the sample size used for the test set. It also does not specify the data provenance (e.g., country of origin, retrospective or prospective nature) for any of the testing. The testing described appears to be internal validation by the manufacturer.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

The document does not provide any information regarding the number of experts used, nor their qualifications, for establishing ground truth. Given the nature of a patient monitor (measuring physiological parameters rather than diagnostic interpretation of images), the "ground truth" would likely be derived from established reference measurement devices rather than expert consensus on subjective data.

4. Adjudication Method for the Test Set

The document does not mention any adjudication method for the test set.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size

The document does not indicate that a multi-reader multi-case (MRMC) comparative effectiveness study was done. The device is a patient monitor, not an AI-assisted diagnostic tool, so such a study would not be applicable in this context. There is no mention of AI assistance in the context of human readers.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The device itself is a standalone patient monitor, designed to measure and display physiological parameters directly. The testing described (system level tests, performance tests, safety testing) directly relates to the standalone performance of the monitor. The document describes a standalone performance evaluation of the device's functionality.

7. The Type of Ground Truth Used

The document does not explicitly state the type of ground truth used. However, for a patient monitor measuring physiological parameters, the ground truth would most typically be established through:

• Reference Measurement Devices: Using highly accurate and calibrated reference instruments (e.g., dedicated ECG machines for cardiac signals, precise pressure transducers for blood pressure, gas analyzers for gas measurements) to establish the true physiological values against which the monitor's readings are compared.
• Physical Simulators/Phantoms: Using controlled simulators that generate known physiological signals or conditions to test the monitor's accuracy and response.

It is highly unlikely that "expert consensus," "pathology," or "outcomes data" would be the primary ground truth for validating the fundamental performance of a patient monitor in the way it is for diagnostic imaging or clinical decision support AI.

8. The Sample Size for the Training Set

The document does not provide any information about a "training set." The device is a patient monitor with software (Release D.00) that likely incorporates established signal processing algorithms and control logic, rather than a machine learning model that requires a distinct training set in the modern sense. The text refers to "specifications cleared for the predicate device" as the basis for evaluation, implying a comparison to known benchmarks rather than an iterative learning process from a large data set.

9. How the Ground Truth for the Training Set Was Established

Since no training set is mentioned or implied for a machine learning context, this question is not applicable based on the provided document. The "ground truth" for the device's development and validation would be the physical and physiological accuracy standards it must meet, as defined by its intended use and regulatory requirements.

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The AneFin 100 is intended to speed emergence from the effects of volatile inhaled anesthetics by removing unwanted anesthetic gas and generating through partial rebreathing. It is intended for use with only Isoflurane, Sevoflurane and Desflurane.

The AneFin combines an anesthetic gas absorber to remove anesthetic gas from the breathing circuit and an anesthetic gas CO₂-sensor which allows increased patient ventilation while preventing hypocapnia during emergence from volatile inhaled anesthesia.

The provided document, K033028 for the Axon Medical AneFin 100, is a 510(k) premarket notification. This type of submission by the FDA focuses on demonstrating substantial equivalence to existing legally marketed devices, rather than establishing new safety and effectiveness through clinical trials with defined acceptance criteria and human performance studies common for novel AI/ML medical devices.

Therefore, the requested information regarding acceptance criteria, device performance against those criteria, study specifics (sample size, ground truth, expert involvement, MRMC studies, standalone performance), and training set details is not present in this document.

The document mainly demonstrates equivalence through a comparison table of attributes between the AneFin 100 and predicate devices, as well as descriptive information about the device and its intended use. Here's a breakdown of what is available:

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

• Not applicable / Not provided. This document does not establish specific performance acceptance criteria for the AneFin 100 against which its performance is then measured. The evaluation is based on demonstrating substantial equivalence to predicate devices, which implies meeting similar safety and performance profiles. The comparison table (labeled "Comparison to Predicate Devices to demonstrate substantial equivalence") highlights physical and functional attributes but not quantitative performance metrics against acceptance criteria.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not provided. No test set or associated sample sizes are mentioned. The substantial equivalence pathway typically relies on bench testing, engineering analysis, and literature review of predicate devices, rather than new clinical testing on a specific "test set" for performance evaluation in the same way an AI device would be assessed.

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 provided. Since no specific test set is described, there's no mention of experts establishing ground truth. The "ground truth" for substantial equivalence is primarily defined by the established safety and effectiveness of the predicate devices.

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

• Not provided. No adjudication method is described as no test set requiring such expert review is mentioned.

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 / Not provided. The AneFin 100 is a physical medical device (anesthetic gas absorber/rebreathing device), not an Artificial Intelligence (AI) or Machine Learning (ML) driven product. Therefore, an MRMC study or assessment of human reader improvement with AI assistance is entirely irrelevant to this device and its regulatory submission.

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

• Not applicable / Not provided. As the AneFin 100 is a physical device and not an algorithm, the concept of "standalone performance" in the context of AI without human interaction does not apply.

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

• Not applicable / Not provided. As noted, the "ground truth" in this context is the established safety and effectiveness of predicate devices, which would have been determined through their own regulatory pathways (e.g., historical use, clinical studies at their time of clearance). There's no new "ground truth" derivation described for this specific submission.

8. The sample size for the training set

• Not applicable / Not provided. As this is not an AI/ML device, there is no "training set."

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

• Not applicable / Not provided. As this is not an AI/ML device, there is no "training set" or ground truth for it.

Summary of the K033028 Submission:

The K033028 submission for the Axon Medical AneFin 100 is a 510(k) premarket notification to demonstrate substantial equivalence to legally marketed predicate devices. The device is an anesthetic gas absorber with rebreathing capabilities intended to speed emergence from volatile inhaled anesthetics.

The method used to "prove" the device meets the regulatory requirements for clearance is through comparison to predicate devices already cleared by the FDA. The application argues that the AneFin 100 shares the same intended use, technological characteristics, and safety profile as the identified predicates, or that any differences do not raise new questions of safety and effectiveness.

• Predicate Devices:
  • RFS Vacuum gauge scavenging circuit, K033503
  • "Protect-OR" filter, Charcoal based scavenging device, Pre-Amendment (Foregger)
  • Model A100 CO2 absorber with bypass valve (Penlon), 510(k) exempt
  • Non invasive cardiac output monitor, NICO (Novametrix), K030886 - This appears to be used as a predicate just for the mechanism of increasing CO2 via dead space tubing and rebreathing volume.

The document explicitly states: "There are no significant differences between the intended device and the identified predicates." This statement is the core of the substantial equivalence argument.

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