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The device is intended to monitor, display and record physiological data to provide cardiac and vital signs monitoring within a medical facility. The device is intended to produce a visual record of the electrical signals produced by the heart and monitor the electrocardiogram to generate audible and/or visual alarms when an arrhythmia exists. The device is also intended to monitor patients' physiological data including heart rate, pulse rate, blood oxygen saturation (SpO2) noninvasive blood pressure (NIBP), body temperature respiratory rate and apnea. The device may generate an audible and/or visual alarm when a measured rate falls outside preset limits. This device receives physiological signals via radio from the transmitters. The device has the capability of communicating with external devices, such as personal computers. Vital sign data can be sent to a central monitor through the network via network card.

The system provides for monitoring ECG, ST levels, VPC rate, arrhythmia events, SpO2, NIBP, temperature, respiration and apnea. The device will be offered with up to 5 multi-connectors and SpO2. The system is comprised of a receiver (WEP-4200A), and a transmitter (ZS-910P). WEP is a multi-parameter monitor consisting of a color LCD touch-screen to display waveforms and numerics of monitored parameters, multi-parameter receiver unit, visual alarm indicator, external communications port, and a removable battery pack for transmitter. Options include a thermal array recorder and a laser printer interface card.

The provided text is a 510(k) summary for the Nihon Kohden WEP-4200A series Central Telemetry System, seeking clearance as substantially equivalent to previously marketed predicate devices. It describes the device, its intended use, and a summary of non-clinical tests conducted. However, the document does not contain explicit acceptance criteria or detailed study results with specific performance metrics (like sensitivity, specificity, accuracy) to "prove the device meets acceptance criteria" in the way typically expected for an AI/ML medical device.

The document focuses on demonstrating substantial equivalence to existing devices, primarily by highlighting that the new device uses the same underlying technology, algorithms, and accessories for its physiological monitoring functions (ECG, arrhythmia detection, SpO2, NIBP, temperature, respiration) as the predicate devices. The main differences noted are an updated transmitting frequency band (WMTS) and increased storage memory.

Therefore, many of the requested items cannot be directly extracted from the provided text. I will address what can be inferred or explicitly stated.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not present a table of quantitative acceptance criteria (e.g., minimum sensitivity/specificity for arrhythmia detection) or corresponding reported device performance metrics for specific functions. Instead, it relies on demonstrating that the core monitoring functionalities are "the same" as or use "the same control software" or "same algorithm" as legally marketed predicate devices.

The closest to "acceptance criteria" are compliance with regulatory standards and verification of operation.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a sample size for a test set in the context of performance evaluation for detection algorithms. The "non-clinical tests" mentioned refer to compliance with standards and design validation, not a clinical performance study with patient data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

This information is not provided as the submission relies on equivalence to predicate devices and compliance with engineering/safety standards rather than a de novo clinical ground truth establishment for a novel algorithm.

4. Adjudication Method for the Test Set

This information is not provided as there is no mention of a clinical test set requiring adjudication in the context of algorithm performance.

5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study was done, and its effect size.

No, an MRMC comparative effectiveness study was not done or mentioned in this document. The device is a "Central Telemetry System" for physiological monitoring and arrhythmia detection, which typically involves automated algorithms rather than human "readers" interpreting cases in the same way a diagnostic imaging AI might.

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

The document implies standalone algorithm performance was established by demonstrating that the arrhythmia detection and other physiological monitoring algorithms are the same as those in legally marketed predicate devices. It states: "The device incorporates the same multi-template arrhythmia detection and analysis of the predicate device..." and "The software algorithm that determines the respiration rate count is the same in both the new device and the predicate." However, specific standalone performance metrics (e.g., sensitivity, specificity) for these algorithms on a defined dataset are not provided.

7. The Type of Ground Truth Used

For the specific algorithms, the ground truth establishment is implicitly linked to the predicate devices. Since the algorithms are stated to be "the same," their original validation (which would have involved expert consensus or known physiological states for ground truth) for the predicate devices serves as the basis. The current document does not detail this original ground truth establishment.

8. The Sample Size for the Training Set

The document does not specify a sample size for a training set. This is likely because the device is not introducing a new, independently trained AI/ML algorithm but re-using or updating established (presumably rule-based or historically trained) algorithms from predicate devices.

9. How the Ground Truth for the Training Set was Established

This information is not provided, as the document focuses on substantial equivalence based on existing, proven technologies from predicate devices rather than the development and training of a new algorithm. The ground truth for the predicate devices' algorithms would have been established historically, but those details are not in this submission.

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The device currently marketed per K001693 is intended to monitor, display and record physiological data to provide cardiac and vital signs monitoring including arrhythmia detection and is available for use by medical personnel on patients within a medical facility including adults, children and infants.

The optional AG-920RA module will measure carbon dioxide (CO2), nitrous oxide (N₂O), oxygen (O₂), and any of five anesthetic agents (Halothane, Isoflurane, Enflurane, Sevoflurane, and Desflurane) of a patient undergoing anesthesia and display the results on a bedside monitor.

The device currently marketed per K001693 is intended to monitor, display and record physiological data to provide cardiac and vital signs monitoring including arrhythmia detection and alarms within a medical facility. With the new AG-920PA option, the device will measure and display Carbon dioxide (CO2), nitrous oxide (N2O), oxygen (O2), and any of five anesthetic agents (Halothane, Isofilurane, Enflurane, Sevoflurane, and Desflurane).

The provided documentation for the NIHON KOHDEN AMERICA, INC. BSM-4100A Series with AG-920RA Option does not contain the detailed information necessary to complete all sections of the request.

Specifically, the document focuses on regulatory equivalence and general performance testing, not on a specific study designed to prove the device meets acceptance criteria related to accuracy, specificity, or sensitivity of gas measurement.

Here's an attempt to answer based on the available information, with many sections marked as "Not provided in the document."

Acceptance Criteria and Device Performance Study for BSM-4100A Series with AG-920RA Option

Based on the provided 510(k) notification summary (K020046), the acceptance criteria and study details are largely about general performance and safety rather than specific measurement accuracy criteria for the gas analyzer function.

The document states that "The device was subjected to electromagnetic, environmental, safety and performance testing procedures. These tests verified the operation of the device. Software validation tested the operation of the device software. The results confirmed that the device performed within specifications." However, it does not specify what those "specifications" or "acceptance criteria" are for the gas measurement accuracy.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not provided for the gas analysis function. The document refers to "testing procedures" without specifying sample sizes for specific performance metrics of the AG-920RA module.
• Data Provenance: Not provided. The company is based in Foothill Ranch, California, USA, but actual test location/origin is not specified.
• Retrospective or Prospective: Not provided. The testing described appears to be pre-market validation testing.

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

• Not applicable/Not provided. The testing described focuses on engineering validation and compliance with standards, not on clinical interpretation or expert-derived ground truth for gas analysis.

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

• Not applicable/Not provided. This type of adjudication pertains to human interpretation/review, which is not relevant to the described engineering and performance testing.

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 measurement instrument, not an AI-assisted diagnostic tool requiring human reader studies.

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

• The AG-920RA module, as a measurement device, inherently operates in a "standalone" fashion to measure gas concentrations. The "performance testing procedures" and "software validation" would constitute its standalone evaluation for its specified functions. However, specific results for accuracy, precision, etc., for each gas are not detailed.

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

• For a gas analyzer, the "ground truth" would typically be established by reference instruments or known gas concentrations, calibrated against traceable standards. The document does not specify the exact methods or reference standards used to establish ground truth for the gas measurements. It implies that "performance testing" was conducted which would involve such methods.

8. The sample size for the training set

• Not applicable. This is not an AI/machine learning device that would require a "training set."

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

• Not applicable. (See #8)

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The device is intended to monitor, display and record physiological data to provide cardiac and vital signs monitoring within a medical facility. The device is intended to monitor the electrocardiogram to generate audible and/or visible alarms when an arrhythmia exists. The device is also intended to monitor heart rate, pulse rate, blood oxygen saturation (SpO2) noninvasive blood pressure (NIBP) invasive blood pressure (IBP), body temperature, carbon dioxide concentration (CO2 and EtCO2), and respiratory rate. The device may generate an audible and/or visual alarm when a measured rate falls outside preset limits. The device may also condition and transmit physiological signals via radio frequency. The device will be available for use by medical personnel on all patient populations.

The device is a multi-parameter monitor consisting of a color LCD touchscreen to display waveforms and numerics of monitored parameters, multi-parameter input unit (socket for input cables), and a removable battery pack. Options include a built-in thermal array recorder and network communication card. The device is software driven.

The provided document describes a 510(k) notification for the Nihon Kohden BSM-2300A Series Bedside Monitor. The primary focus of the performance testing section is on compliance with electrical safety, electromagnetic compatibility, and general functional specifications, rather than detailed clinical performance metrics for arrhythmia detection or specific vital sign monitoring accuracy.

Therefore, many of the requested elements regarding acceptance criteria for specific clinical performance, sample sizes for test sets with ground truth, expert adjudication, or MRMC studies are not available in the provided text. The document primarily focuses on demonstrating substantial equivalence to a predicate device through adherence to recognized electrical and safety standards and internal product specifications.

Here's a breakdown of the available information based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The provided text does not include specific numerical acceptance criteria for clinical performance metrics (e.g., sensitivity, specificity, accuracy for arrhythmia detection, or accuracy ranges for vital signs) nor corresponding reported numerical device performance for such criteria. The performance testing described is focused on engineering and safety standards compliance and internal product specifications.

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

• Sample Size for Test Set: Not specified for clinical performance. The document mentions "product verification and validation" for software and "completed testing" for environmental/safety, but no sample sizes for patients or data points are provided.
• Data Provenance: Not specified. Given the nature of the testing described (compliance with standards, software validation, environmental), it likely involved in-house engineering testing rather than patient data collected from a specific country. If patient data was used for validation of arrhythmia or vital signs, it is not mentioned.
• Retrospective or Prospective: Not specified.

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

Not applicable/Not specified. The document does not describe the establishment of ground truth for clinical performance using human experts. The testing described focuses on engineering and safety compliance.

4. Adjudication method for the test set

Not applicable/Not specified. There is no mention of clinical test sets 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 bedside monitor with automated arrhythmia and vital sign detection/alarms, not an AI-assisted diagnostic tool that would typically be evaluated with MRMC studies comparing human readers with and without AI assistance. The technology described predates widespread "AI" as we understand it in medical devices today.

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

Yes, implicitly. The device itself is designed to perform monitoring, detect arrhythmias, and generate alarms without continuous human intervention. The testing described (compliance with standards, software functionality) would inherently evaluate the device's standalone performance against its specifications and the requirements of the standards. However, specific standalone performance metrics (e.g., sensitivity/specificity for arrhythmia without human review) are not provided.

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

Not specified for clinical performance. For the engineering and safety tests, the "ground truth" would be the specifications and requirements defined by the applicable standards (e.g., IEC 60601-1, AAMI EC 12-27). For software, the ground truth would be the expected functional behavior defined in the product specifications.

8. The sample size for the training set

Not applicable/Not specified. This document is for a conventional medical device (bedside monitor) from 2001 and does not describe machine learning or AI models requiring training sets as typically understood in current contexts.

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

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

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