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The intended use of the MX40 is to: The device is intended for monitoring and recording of and to generate alarms for, multiple physiological parameters of adults and pediatrics in a hospital environment and during patient transport inside hospitals. Not intended for home use. Intended for use by health care professionals.

Indications for Use: Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Intended for monitoring and recording of, and to generate alarms for, multiple physiological parameters of adults and pediatrics in hospital environments and during transport inside hospitals. Rx only.

The MX40 is a multi-parameter, battery operated patient monitor that is small enough to be worn by the patient. Its multi-radio design enables device operation within existing Philips telemetry networks while providing compatibility to the IntelliVue Information Center solutions. Along with infrastructure radios, the MX40 integrates a short-range radio (SRR), enabling wireless connectivity to the IntelliVue family of monitors. Acquired physiological data, along with physiological alarms and technical alert information are provided to multiple monitoring systems (local, bedsides and /or information systems).

The provided document is a 510(k) summary for the Philips MX40 Release B.07, a physiological patient monitor. It focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study of its own performance against pre-defined acceptance criteria for a novel AI/software component within the device.

Therefore, many of the requested details about acceptance criteria, specific studies proving performance, sample sizes, ground truth establishment, expert involvement, and MRMC studies are not present in this document as it pertains to a modification of an existing device rather than a new device with new clinical claims. Manufacturers typically demonstrate substantial equivalence through verification and validation testing, often against established standards and internal specifications, rather than large-scale clinical trials for minor modifications.

Here's an attempt to answer the questions based on the available information:

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

The document does not provide a table of specific acceptance criteria (e.g., sensitivity, specificity thresholds) or quantitative device performance metrics. Instead, it states:

• Acceptance Criteria (Implied): "Pass/Fail criteria were based on the specifications cleared for the predicate device." (Page 8)
• Reported Device Performance: "test results showed substantial equivalence." (Page 8) and "The MX40 Release B.07 meets all defined reliability requirements and performance claims." (Page 8)

The document lists changes to functionalities, such as:

• Heart Rate Alarm from Pulse, requiring PIC IX version C.01 or higher.
• Auto report battery inop (Technical Alert) when Standby is in low battery state.
• Support CI (Connection Indication) message transport using Unicast protocol for WLAN.
• Malfunc Inop for software license failure - Added self-test to MX40 to allow for confirmation of Software License version to assist users to identify compatibility to other systems.
• Claim IEC60601-1 3rd edition update IFU and Product Labeling appropriately.
• AAMI screen - A new portrait screen 1-wave and 2 numerics to support AAMI EC13 display, local control only & cannot default to this screen from PIC-iX.
• MCS PigTail and Block Adapter hardware to support Respiration measurements.

The "reported device performance" is broadly stated as meeting the specifications of the predicate device.

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

This information is not provided in the document. The document refers to "system level tests, performance tests, and safety testing from hazard analysis" (Page 8), but does not specify sample sizes or data provenance for these non-clinical tests.

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, as clinical performance testing, which would typically involve expert ground truth, was not performed.

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

This information is not provided in the document.

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 MRMC comparative effectiveness study was mentioned or performed, as the device is a physiological monitor and not an AI-assisted diagnostic tool.

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

The document states that "Clinical Performance testing for MX40 Release B.07 was not performed, as there were no new clinical applications that had hazards or risk mitigations that required a clinical performance testing to support equivalence." (Page 8). Therefore, no standalone clinical performance study was done for this particular submission. The device itself is a standalone physiological monitor, but the nature of the submission (modifications to an existing device) did not necessitate a new standalone performance study against clinical ground truth.

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

For the non-clinical testing performed, the ground truth would have been based on established specifications, standards, and expected performance characteristics of the predicate device and the new functionalities, rather than clinical ground truth like pathology or expert consensus. The changes relate to hardware and software updates, not new diagnostic algorithms requiring clinical validation.

8. The sample size for the training set

This information is not applicable as the document does not describe the development or testing of an AI/machine learning algorithm with a training set. The device is a physiological monitor with specified functionalities.

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

This information is not applicable as there is no mention of an AI/machine learning component or a training set.

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