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The vital signs monitor is intended to be used for monitoring, reviewing, storing and alarming of multiple physiological parameters of patients, including Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Carbon dioxide (CO2) and Temperature (Temp).

This vital signs monitor is used to monitor vital signals for use in hospital environments including out-patient department, wards and NICU. It is not intended for helicopter transport, hospital ambulance or home use. And it is applicable for adult, pediatric and neonatal patients.

The Vital Signs Monitor is a portable device intended for use by health care professionals. The monitor could provide the monitoring of physiological parameters such as the Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Carbon dioxide (CO2) and Temperature (Temp).

The following lists the detailed features of the subject device.

• LCD/LED display
• SpO2, Pulse Rate NIBPCO2 and TEMP measurement
• Infrared ear temp or fast temp module for Temp measurement
• Nellcor or Masimo or BLT SpO2 module
• Display numeric and waveform information simultaneously .
• Nurse call feature
• . Built-in Lithium-ion Battery
• Suitable for adult, pediatric and neonate patients .
• Visual and audible alarm

The document describes a 510(k) premarket notification for the V6 Vital Signs Monitor. The submission focuses on demonstrating substantial equivalence to a predicate device rather than presenting a performance study with acceptance criteria in the context of AI/ML devices.

Therefore, many of the requested categories for acceptance criteria and study details for an AI-powered device are not applicable to this submission. This document describes a traditional medical device (vital signs monitor) which is evaluated based on its compliance with established electrical safety, EMC, and performance standards for each physiological parameter.

Here's an analysis based on the provided document, highlighting applicable information and noting where information for AI/ML performance would typically be found but isn't present here:

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

The document provides a comparison of the proposed device (Vital Signs Monitor V6) and its predicate devices, especially regarding technical specifications and compliance with standards. It does not present specific "accepted criteria" in terms of performance metrics like sensitivity, specificity, or AUC, as would be typical for an AI/ML diagnostic or assistive device. Instead, it demonstrates compliance with recognized medical device standards and equivalence to a predicate.

For the newly added F3000 Temperature Module, a "Clinical Electronic Thermometers testing" was conducted pursuant to guidance. The table below focuses on the reported accuracy of the temperature measurement, which can be interpreted as a performance metric for that specific function.

Note: The "acceptance criteria" here are largely derived from the predicate device's performance or standard requirements that both devices are expected to meet. For NIBP, SpO2, and CO2, the document only states that compliance with relevant IEC standards (IEC80601-2-30 for NIBP, IEC80601-2-61 for pulse oximeter) was demonstrated, but does not provide specific accuracy ranges within the provided text.

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

The document states: "No clinical study is included in this submission." This implies that for the substantial equivalence claim, the manufacturer relied on non-clinical testing and comparison to the predicate device, rather than a clinical study with a dedicated test set on human subjects to evaluate performance like sensitivity or specificity. For the temperature module, it mentions "Clinical Electronic Thermometers testing" but does not detail sample size or data provenance.

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 applicable. There is no clinical study data presented for expert-established ground truth.

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

Not applicable. There is no clinical study data presented requiring adjudication.

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. This device is a vital signs monitor, not an AI-assisted diagnostic tool for human readers.

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

The device is a standalone vital signs monitor. Its "performance" is evaluated by its ability to accurately measure physiological parameters and comply with relevant international standards. The non-clinical tests mentioned demonstrate the device's standalone performance in meeting design specifications and standard requirements.

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

For the non-clinical tests and equivalence claims of the vital signs monitor, the "ground truth" would be established by:

• Reference instruments and calibration standards for objective measurements (e.g., for SpO2, NIBP, temperature).
• Compliance with the technical requirements and performance specifications outlined in international standards (e.g., IEC 60601 series, IEC80601 series).

For the temperature module, "Clinical Electronic Thermometers testing" likely involved comparison to highly accurate reference thermometers in a controlled clinical setting, but specific details are not provided.

8. The sample size for the training set

Not applicable. This device does not appear to involve an AI/ML algorithm that would require a "training set" in the conventional sense for a diagnostic product. Its functionality is based on established hardware and software algorithms for physiological parameter measurement rather than machine learning on a dataset.

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

Not applicable, as there is no mention of a training set for an AI/ML algorithm.

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