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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. Standard and optional parameters include: NBP, SpO2, and Temperature. Intended for monitoring, recording, and alarming of multiple physiological parameters of adults, pediatrics and neonates in healthcare environments. Additionally, the monitors may be used in transport situations within a healthcare facility.

The Philips SureSigns VS2* Vital Signs Monitor is a multi-parameter patient monitor. Standard and optional parameters include: NBP, SpO2, and Temperature. The energy source of the subject device is an internal power supply. The VS2* can run on battery power with batteries similar to the predicate device, however, with improved battery life.

The provided text is a 510(k) summary for the Philips SureSigns VS2 Vital Signs Monitor. It primarily focuses on demonstrating substantial equivalence to a predicate device and does not contain detailed information about specific acceptance criteria or a dedicated study with the kind of quantitative performance metrics, sample sizes, and expert adjudication described in the prompt. The document 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." However, it does not provide these specific criteria or the detailed results of the performance tests.

Therefore, much of the requested information regarding specific acceptance criteria, reported performance, sample sizes, ground truth establishment, expert qualifications, and MRMC studies cannot be extracted from this document.

Here's a breakdown of what can and cannot be answered based on the provided text:

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

• Acceptance Criteria: The document states that "Pass/Fail criteria were based on the specifications cleared for the predicate device, the specifications of the subject device". However, the specific quantitative acceptance criteria (e.g., accuracy ranges for NBP, SpO2, Temperature) are not provided.
• Reported Device Performance: The document only generically states that "The results demonstrate that the Philips SureSigns VS2* Vital Signs monitor meets all reliability requirements and performance claims and supports a determination of substantial equivalence." Specific performance metrics (e.g., bias, precision, accuracy) for NBP, SpO2, or Temperature are not reported.

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 text mentions "system level tests, performance tests, and safety testing" but does not detail the sample sizes for these tests or the nature of the data (e.g., patient data, simulated data).

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)

• Ground truth refers to a definitive correct answer, often established by experts in diagnostic studies. For a vital signs monitor, ground truth typically involves highly accurate reference devices. The document does not mention the use of experts or any process for establishing ground truth in terms of clinical interpretation, as it's a device measuring physiological parameters, not interpreting images or complex clinical scenarios requiring expert consensus.

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

• Adjudication methods are relevant when multiple experts rate the same case and their opinions need to be reconciled. As no expert review or interpretation is described for the performance testing of this vital signs monitor, this information is not applicable/provided.

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 relevant for diagnostic devices that aid medical professionals in interpretation. This device is a vital signs monitor, not a diagnostic imaging or AI-assisted interpretation tool. Therefore, an MRMC study and related effect sizes are not applicable and not mentioned.

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

• The device itself is a standalone monitor. The performance testing would inherently be the "algorithm only" performance (i.e., the device's ability to accurately measure vital signs). The document states "Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the subject device," implying standalone testing. However, specific details of this testing are not provided.

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

• For a vital signs monitor, ground truth typically refers to measurements obtained from highly accurate, calibrated reference devices (e.g., a reference NBP device, a co-oximeter for SpO2, a highly accurate thermometer). The document does not explicitly state the type of ground truth used for its performance testing, but it would logically be comparison to independent reference measurements.

8. The sample size for the training set

• This device is a hardware vital signs monitor. While it has embedded software/firmware (which might have been "trained" or developed), the concept of a "training set" as understood in machine learning/AI is likely not directly applicable in the same way. The document makes no mention of a training set.

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

• Similar to point 8, the concept of a "training set" and its ground truth in the context of this traditional medical device is not applicable/provided.

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