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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 monitors are intended to be used for monitoring, storing, recording, and reviewing of, and to generate alarms for, multiple physiological parameters of adults and pediatrics. The monitors are intended for use by trained healthcare professionals in hospital environments. The monitored physiological parameters include: ECG, respiration (RESP), temperature (TEMP), oxygen saturation of arterial blood (SpO2), pulse rate (PR), non-invasive blood pressure (NIBP), invasive blood pressure (IBP), carbon dioxide (CO2), cardiac output (C.O.), and Anaesthesia gas(AG). The arrhythmia detection and ST Segment analysis are intended for adult patients. The monitors are not intended for MRI environments.

The iM series Patient Monitor including iM50, iM60, iM70 and iM80 can perform long-time continuous monitoring of multiple physiological parameters. Also, it is capable of storing, displaying, analyzing and controlling measurements, and it will indicate alarms in case of abnormalities so that doctors and nurses can respond to the patient's situation as appropriate.

Based on the provided text, the device in question is a Patient Monitor (Model: iM50, iM60, iM70, iM80), which monitors various physiological parameters. The document focuses on demonstrating substantial equivalence to a predicate device, rather than providing detailed acceptance criteria and a standalone study for a novel AI device. Therefore, much of the requested information regarding AI-specific evaluation (e.g., sample size for AI test sets, expert adjudication, MRMC studies, AI effect size, ground truth establishment for training) is not applicable or not present in this 510(k) summary.

However, I can extract information related to the device's self-contained performance testing and regulatory acceptance criteria.

Acceptance Criteria and Device Performance for Patient Monitor (iM Series)

The document primarily relies on bench testing and software verification and validation to demonstrate that the iM series Patient Monitor meets its accuracy specifications and relevant consensus standards, thereby establishing substantial equivalence to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present explicit "acceptance criteria" in a quantitative table for this specific device in the manner typically seen for novel AI models. Instead, it compares the technical specifications of the subject device to a predicate device and states that "the results of the bench testing show that the subject device meets its accuracy specification and meet relevant consensus standards."

The comparison table (pages 5 & 6) implicitly indicates that "acceptance" for the subject device's performance corresponds to ranges/specifications that are identical or comparable to the cleared predicate device.

Notes on the 'AG Module': The document explicitly states for the AG (Anesthesia Gas) module that its "indication is not present in the primary predicate, but is present in Edan Patient Monitor V series K160981." This implies that while it differs from the immediate primary predicate, it is substantially equivalent to a different, already cleared, predicate device from the same manufacturer.

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

• Sample Size: The document does not specify a distinct "test set" sample size in terms of patient data or number of tests. The performance data section refers to "functional and system level testing" and "bench testing." This implies testing against specifications and standards rather than a clinical dataset of a specific size.
• Data Provenance: Not specified. Given it's a bench test, it would typically be conducted in a laboratory setting. There's no mention of country of origin for test data, nor whether it's retrospective or prospective patient data, as clinical data was deemed "Not applicable."

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

Not applicable. This is a physiological monitor, not an AI diagnostic device requiring expert consensus for ground truth on images or signals. The "ground truth" for the device's performance would be derived from calibrated measurement references and established engineering principles in bench testing.

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

Not applicable. As above, this is not an AI diagnostic device relying on human expert review for ground truth.

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 physiological monitor, not an AI-assisted diagnostic tool that requires human readers for interpretation.

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

The device's performance in terms of its physiological measurements and alarm detection is inherently "standalone" in that it performs these functions without direct human intervention in the measurement process itself, generating outputs for healthcare professionals. The bench testing performed would be considered evaluating this standalone performance against technical specifications and standards.

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

For the non-clinical performance (bench testing), the "ground truth" is based on:

• Calibration standards: Using known, precise inputs (e.g., electrical signals simulating ECG, precise pressures for NIBP, known gas concentrations for CO2/AG) to verify the accuracy of the device's measurements.
• Consensus Standards: Adherence to recognized international standards for medical electrical equipment (e.g., IEC 60601 series, ISO 80601 series). These standards define acceptable performance limits and test methodologies.

8. The sample size for the training set

Not applicable. This document does not describe an AI/ML device that requires a "training set" in the conventional sense. The device's algorithms are likely based on established physiological signal processing, not deep learning models trained on large datasets.

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

Not applicable, as there is no "training set" for an AI/ML model described.

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