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The SureSigns VM4, VM6 and VM8 Patient Monitors are for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics, and neonates in healthcare environments. Additionally, the monitor is intended for use in transport situations within a healthcare facility. Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Standard and optional parameters include: ECG, Respiration, NBP, SpO2, IBP, CO2, Temperature.

The subject devices are the Philips SureSigns Series Patient Monitors, SureSigns VM4, VM6, and VM8 Patient Monitors. They are multi-parameter patient monitors. Modifications include adding arrhythmia analysis to the VM4, adding standby mode into the CO2 menu, changing the LCD display backlight to LED, replacing the current Oridion CO2 module with a RoHS compliant module, and several enhancement requests related to display, alarms, labeling, NBP measurement, patient demographics, heart rate and pulse display, trend database size, and software hooks for connection to a central station.

The provided text does not contain the detailed information required to fill out a table of acceptance criteria and reported device performance for a modern AI/ML medical device submission. This document describes a traditional patient monitor (Philips SureSigns Series Patient Monitors, SureSigns VM4, VM6, and VM8) and its 510(k) submission from 2012-2013, which predates the widespread use of sophisticated AI/ML algorithms in medical devices in the way your prompt implies.

The 510(k) in the input describes incremental changes to an existing patient monitor, primarily focusing on:

• Adding arrhythmia analysis to a new model (VM4) using an existing software algorithm from other models (VM6, VM8). This is not a description of a novel AI/ML algorithm requiring extensive validation as commonly discussed today.
• Minor user interface enhancements and technical component changes (e.g., LED backlight, RoHS compliant CO2 module).
• Adding software hooks for future central station connection.

Therefore, the specific criteria for AI/ML performance (e.g., sensitivity, specificity, AUC) and detailed study methodologies (like sample size for test/training sets, number of experts, adjudication methods, MRMC studies, specific ground truth types) are not present in this document.

The document only states general verification and validation activities:

• "Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the subject devices with respect to the predicates."
• "Testing involved system level tests, performance tests, and safety testing from hazard analysis."
• "Pass/Fail criteria were based on the specifications cleared for the predicate device, the specifications of the subject device and test results showed substantial equivalence."

This is typical for traditional hardware/software modifications.

In summary, I cannot extract the requested information because the provided document is for a traditional patient monitor 510(k) submission from 2013, not an AI/ML device, and thus does not contain the detailed performance metrics and study design methodologies specifically relevant to validating AI/ML algorithms.

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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients.

The Philips SureSigns Series Patient Monitors, SureSigns VM4, VM6, and VM8 Patient Monitors are multi-parameter patient monitors. The modifications include a new OEM CO2 module, addition of an apnea alarm, a new main board, modified front end board, alternate internal component speaker, optional bar code reader, optional RS232 serial port adaptor, several new accessories, and software enhancements. Impedance respiration is added to SureSigns VM4.

1. Acceptance Criteria and Reported Device Performance

The provided text only states that "Pass/Fail criteria were based on the specifications cleared for the predicate device, the specifications of the subject device and test results showed substantial equivalence." It does not explicitly list specific acceptance criteria or quantitative performance metrics. It generally asserts that the device "meet all reliability requirements and performance claims."

2. Sample Size and Data Provenance

The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective or prospective nature of the data). It broadly mentions "system level tests, performance tests, and safety testing from hazard analysis."

3. Number and Qualifications of Experts for Ground Truth

This information is not provided in the document. The type of device (patient monitor) and the nature of modifications (hardware and software enhancements to existing parameters, addition of alarms) suggest that "ground truth" might be established through comparisons with reference measurement devices or established clinical standards, rather than expert consensus on image interpretation, for example.

4. Adjudication Method

The document does not mention any adjudication method.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No multi-reader multi-case (MRMC) comparative effectiveness study is mentioned. The device is a patient monitor, not an AI-assisted diagnostic tool that would typically involve human readers interpreting output. The evaluation focuses on technical performance and equivalence to a predicate device.

6. Standalone Performance (Algorithm Only)

The device itself is a standalone patient monitor, and its performance was evaluated. However, the term "standalone" in the context of AI often implies an algorithm's performance without any human-in-the-loop. In this case, the device monitors physiological parameters and provides alarms directly, so its performance is inherently standalone in generating these outputs. The text indicates that "testing activities establish the performance, functionality, and reliability characteristics of the subject devices."

7. Type of Ground Truth Used

The document does not explicitly state the type of ground truth used. However, for physiological monitoring devices, the ground truth would typically be established by:

• Reference standard devices: Comparing the device's readings against highly accurate and calibrated reference instruments for each physiological parameter (e.g., a clinical-grade blood pressure cuff for NBP, a calibrated pulse oximeter for SpO2, a gas analyzer for CO2).
• Physical simulators: For certain parameters, using simulators that can generate precise physiological waveforms or values.
• Clinical standards/established norms: Ensuring basic functionality and alarm thresholds align with recognized clinical practices.

8. Sample Size for the Training Set

The document does not specify any training set sample size. This type of device (patient monitor with hardware and software modifications to existing parameters) is unlikely to have a "training set" in the sense of machine learning models requiring large datasets for training. Its development would involve engineering design, component testing, and system-level validation against specifications, not typically a data-driven training process in the modern AI sense.

9. How Ground Truth for the Training Set Was Established

As no training set is mentioned (see point 8), there is no information on how its ground truth would have been established.

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