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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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