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The BeneVision N12N15/N17/N19/N22 patient monitors are intended for monitoring, displaying, storing, alarming, and transferring of multiple physiological parameters including ECG (3-lead, 5-lead or 12-lead selectable, Arrhythmia Detection, ST Segment Analysis, QT Analysis, and Heart Rate (HR)), Respiration Rate (Resp), Temperature (Temp), Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Invasive Blood Pressure (IBP), Pulmonary Artery Wedge Pressure (PAWP), Cardiac Output (C.O.), Continuous Cardiac Output (CCO), Mixed/Central Venous Oxygen Saturation (SvO2/ScvO2), Carbon Dioxide (CO2), Oxygen (O2), Anesthetic Gas (AG), Impedance Cardiograph (ICG), Bispectral Index (BIS), Respiration Mechanics (RM), Neuromuscular Transmission Monitoring (NMT), Electroencephalograph (EEG), and Regional Oxygen Saturation (rSO2). The system also provides an interpretation of resting 12-lead ECG.

All the parameters can be monitored on single adult, pediatric, and neonatal patients except for the following:

• · BIS, RM, CCO, SvO2/ScvO2, PAWP, NMT monitoring, PNP, and PNC are intended for adult and pediatric patients only. CCO using FloTrac is intended for adult patients only;
• · C.O. monitoring and A-Fib are intended for adult patients only;
• · ICG monitoring is intended for only adult patients who meet the following requirements: height: 122 to 229cm, weight: 30 to 155kg.
• · rSO2 monitoring is intended for use in individuals greater than 2.5kg.

The monitors are to be used in healthcare facilities by clinical professionals or under their guidance. They should only be used by persons who have received adequate training in their use. The BeneVision N12/N15/N17/N19/N22 monitors are not intended for helicopter transport, hospital ambulance, or home use.

The BeneVision N1 Patient Monitor is intended for monitoring, reviewing, storing, alarming, and transferring of multiple physiological parameters including ECG (3-lead, 5-lead or 12-lead selectable, Arrhythmia Detection, ST Segment Analysis, and Heart Rate (HR)), Respiration (Resp), Temperature (Temp), Pulse Oxygen Saturation (SpO₂), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Invasive Blood Pressure (IBP), Pulmonary Artery Wedge Pressure (PAWP), Carbon Dioxide (CO2) and Oxygen (O2). The system also provides an interpretation of resting 12-lead ECG.

All the parameters can be monitored on single adult, pediatric, and neonatal patients except for the following:

• PAWP, PNP, and PNC are intended for adult and pediatric patients only;
• A-Fib is intended for adult patients only;

The BeneVision N1 monitor is to be used in healthcare facilities. It can also be used during patient transport inside and outside of the hospital environment, whereas N1 configured with WMTS technology can be used inside the hospital only. It should be used by clinical professionals or under their guidance. It should only be used by persons who have received adequate training in its use. It is not intended for home use.

The subject BeneVision N Series Patient Monitors includes six monitors:

• . BeneVision N12 Patient Monitor
• BeneVision N15 Patient Monitor
• BeneVision N17 Patient Monitor ●
• BeneVision N19 Patient Monitor ●
• BeneVision N22 Patient Monitor ●
• BeneVision N1 Patient Monitor

Mindray's BeneVision N Series Patient Monitors provide a flexible software and hardware platform to meet the clinical needs of patient monitoring.

This document is a 510(k) Summary for the Mindray BeneVision N Series Patient Monitors, which focuses on demonstrating substantial equivalence to a previously cleared predicate device (K202405).

The information provided primarily details the device's technical specifications and comparisons to a predicate device, rather than a full study proving the device meets acceptance criteria for a specific medical condition or AI diagnostic output.

Therefore, I cannot fully answer all parts of your request as the document does not contain the detailed clinical study results (like sample sizes for test sets, number of experts for ground truth, adjudication methods, MRMC studies, or specific AI performance metrics) that would typically be found for a device requiring those types of studies (e.g., an AI-powered diagnostic tool).

However, I can extract the relevant information regarding the device's functional and technical performance as demonstrated in this 510(k) submission.

Here's a breakdown of what can be inferred and what is missing:

Acceptance Criteria and Reported Device Performance

The "acceptance criteria" in this context are related to meeting the performance specifications of the predicate device and relevant consensus standards. The "reported device performance" is demonstrated through functional and system-level testing, ensuring the device meets its accuracy specifications for the various physiological parameters it monitors.

Table of "Acceptance Criteria" (Implied Specifications) and "Reported Device Performance" (Conformance):

Detailed Study Information (Based on Document Content):

1. Sample sizes used for the test set and the data provenance:

   • The document states that "functional and system level testing" and "bench testing" were conducted.
   • However, no specific sample sizes for test sets (e.g., number of patients, number of data recordings) are provided for any of the performance evaluations.
   • Data Provenance: Not explicitly stated (e.g., country of origin, retrospective/prospective). The tests described are generic "bench testing" to ensure compliance with technical specifications and standards, not clinical studies.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable / Not provided. This document describes engineering and bench testing against pre-defined technical specifications and industry standards for physiological measurement accuracy. It does not describe a clinical study involving human experts establishing ground truth for diagnostic interpretation (e.g., for an AI algorithm interpreting medical images).

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

   • Not applicable / Not provided. Same reason as above.

4. 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. This device is a patient monitor. It detects physiological parameters and provides alarms, and some interpretations of ECG (e.g., 12-lead ECG interpretation, arrhythmia detection). It is not an AI-assisted diagnostic device in the sense of image interpretation for which MRMC studies are typically performed. The document details that "optimized auditory ALARM SIGNALS" and "alarm highlight" were added, suggesting improvements to the human-device interface, but not a formal MRMC study on diagnostic improvement.

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

   • The document implies that algorithms for ECG (Mindray or Mortara algorithm for arrhythmia and ST-segment analysis) are embedded in the device. The listed accuracy specifications for these measurements (e.g., HR, ST, QT) reflect the standalone performance of these measurement algorithms and sensors against established benchmarks. However, a formal "standalone study" with detailed methodology, distinct from the general bench testing, is not specifically described or provided with separate results. The performance data listed (e.g., accuracy for HR, ST, QT) serves as the "standalone" performance verification for these integrated functionalities.

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

   • For physiological measurements (ECG, SpO2, NIBP, etc.), the "ground truth" would typically refer to reference measurement devices or calibrated simulators used during bench testing to verify the accuracy of the monitor's readings against a known, accurate value.
   • For the ECG interpretation (e.g., 12-lead ECG interpretation, arrhythmia detection), the ground truth for the algorithms would have been established during their development and previous clearance processes (Mindray or Mortara algorithms). This document focuses on demonstrating that the integration and revised features maintain that established accuracy rather than re-proving the core algorithms.

7. The sample size for the training set:

   • Not provided. This document pertains to the 510(k) clearance of updates to an existing patient monitor series. It does not detail the development or training of new AI/ML algorithms, which would typically involve substantial training datasets. The ECG algorithms (Mindray or Mortara) were presumably "trained" (or developed and validated) previously as part of their initial predicate clearances.

8. How the ground truth for the training set was established:

   • Not provided. (See point 7). For existing algorithms like Mortara or Mindray ECG algorithms, ground truth for their original development would likely have been established using large, diverse ECG databases with expert cardiologist interpretations and/or correlation with clinical outcomes where relevant. This particular 510(k) document is concerned with demonstrating equivalence and continued performance with minor changes, not the original algorithm development.

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The BeneVision N12/N15/N17/N19/N22 patient monitors are intended for monitoring, displaying, reviewing, storing, and transferring of multiple physiological parameters including ECG (3-lead, 5-lead, 6-lead or 12-lead selectable, Arrhythmia Detection, ST Segment Analysis, QT Analysis, and Heart Rate (HR)), Respiration Rate (Resp), Temperature (Temp), Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Invasive Blood Pressure(IBP), Pulmonary Artery Wedge Pressure (PAWP), Cardiac Output (C.O.), Continuous Cardiac Output (CCO), Mixed/Central Venous Oxygen Saturation (SvO2/ScvO2), Carbon Dioxide (CO2), Oxygen (O2), Anesthetic Gas (AG), Impedance Cardiograph (ICG), Respiration Mechanics (RM), Neuromuscular Transmission Monitoring (NMT), Electroencephalograph (EEG), and Regional Oxygen Saturation (rSO2). The system also provides an interpretation of resting 12-lead ECG.

All the parameters can be monitored on single adult, pediatric, and neonatal patients except for the following:

• The arrhythmia detection, RM, CCO, SvO2/ScvO2, PAWP, and NMT monitoring are intended for adult and pediatric patients only;
• C.O. monitoring is intended for adult patients only;
• ICG monitoring is intended for only adult patients who meet the following requirements: height: 122 to 229cm, weight: 30 to 155kg.
• rSO2 monitoring is intended for use in individuals greater than 2.5kg.

The monitors are to be used in healthcare facilities by clinical professionals or under their guidance. They should only be used by persons who have received adequate training in their use. The BeneVision N12/N15/N17/N19/N22 monitors are not intended for helicopter transport, hospital ambulance, or home use.

The BeneVision N1 Patient Monitor is intended for monitoring, displaying, storing, alarming, and transferring of multiple physiological parameters including ECG (3-lead, 5-lead, 6-lead or 12-lead selectable, Arrhythmia Detection, ST Segment Analysis, QT Analysis, and Heart Rate (HR)), Respiration (Resp), Temperature (Temp), Pulse Oxygen Saturation (SpO2), Pulse Rate (PR), Non-invasive Blood Pressure (NIBP), Invasive Blood Pressure (IBP) , Pulmonary Artery Wedge Pressure (PAWP), Carbon Dioxide (CO2) and Oxygen (O2). The system also provides an interpretation of resting 12-lead ECG.

All the parameters can be monitored on single adult, pediatric, and neonatal patients except for the following:

• The arrhythmia detection and PAWP is intended for adult and pediatric patients only
  The BeneVision N1 monitor is to be used in healthcare facilities. It can also be used during patient transport inside and outside of the hospital environment. It should be used by clinical professionals or under their guidance. It should only be used by persons who have received adequate training in its use. It is not intended for home use.

The subject BeneVision N Series Patient Monitors includes six monitors:

• BeneVision N12 Patient Monitor
• BeneVision N15 Patient Monitor
• BeneVision N17 Patient Monitor
• BeneVision N19 Patient Monitor
• BeneVision N22 Patient Monitor
• BeneVision N1 Patient Monitor

The BeneVision N Series Patient Monitors are Mindray's new generation monitoring product family with ergonomic and flexible design in platform of both software and hardware to meet the clinical needs of monitoring.

The provided document is a 510(k) Summary for the Mindray BeneVision N Series Patient Monitors. It focuses on demonstrating substantial equivalence to predicate devices rather than proving the device meets specific acceptance criteria through a dedicated study with statistical endpoints.

Therefore, many of the requested elements for a detailed study description (e.g., sample size for test/training sets, data provenance, number/qualifications of experts, adjudication methods, MRMC studies, standalone performance with specific metrics, and ground truth establishment for training data) are not present in the provided text.

The document primarily highlights changes from predicate devices and states that functional and system-level testing, along with compliance with consensus standards, demonstrate equivalence.

Here's a summary of the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding device performance metrics in the format typically seen for a new device's efficacy study. Instead, it compares the specifications of the subject device with those of predicate devices, implicitly indicating that the subject device's performance meets or exceeds the predicate's established performance or relevant cleared standards.

2. Sample size used for the test set and the data provenance

The document does not specify sample sizes for test sets. The testing mentioned is referred to as "functional and system level testing" and "bench testing." It also states Mindray conducted "clinical testing to demonstrate that the Mindray and Nellcor SpO2 modules meet relevant consensus standards."
There is no mention of data provenance (e.g., country of origin of data, retrospective or prospective).

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The document does not describe the use of experts to establish ground truth for testing. The evaluation focused on meeting specifications and consensus standards, and demonstrating equivalence to predicate devices.

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

Not applicable. There is no mention of adjudication methods.

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 is a patient monitor, not an AI-assisted diagnostic device, and no MRMC studies are mentioned.

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

The document describes performance in terms of functionality and adherence to technical specifications and consensus standards, not in terms of "algorithm-only" performance as would be relevant for an AI device. The tests performed are for the integrated device.

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

The term "ground truth" is not used. The performance evaluation is based on meeting engineering specifications, comparing against predicate device performance, and compliance with recognized consensus standards (e.g., IEC, ISO, AAMI standards for physiological measurement accuracy).

8. The sample size for the training set

Not applicable. A "training set" is relevant for machine learning algorithms. This document describes a patient monitor, and no machine learning model training is discussed.

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

Not applicable, as no training set for a machine learning model is mentioned.

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