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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Intended for monitoring and recording of and to generate alarms for multiple physiological parameters of adults, pediatrics and neonates in hospital environments. The MP2, X2, MP5, MP5T, MP20, MP30, MP40, and MP50 are additionally intended for use in transport situations within hospital environments. The MP2, X2 and MP5 are also intended for use during patient transport outside of a hospital environment.

The Philips IntelliVue Patient Monitor family comprises the multi-parameter patient monitor models: MP2, X2, MP5, MP5T, MP20, MP30, MP40, MP50, MP60, MP70, MP80, MP90 and MX800 IntelliVue Patient Monitors that consist of display units including built-in or separate flat panel displays and central processing units (CPU) and physiological measurement modules. All monitors share the same system architecture and exactly the same software is executed on each monitor. The IntelliVue Patient Monitors measure multiple physiological parameters such as surface ECG, invasive and non-invasive pressure, etc., generate alarms, record physiological signals, store derived data, and communicate derived data and alarms to central stations via the IntelliVue Clinical Network. The subject modification is the introduction of software revision H.03 for the entire IntelliVue Patient Monitors family.

The provided text describes a 510(k) summary for Philips IntelliVue Patient Monitors and does not contain detailed information about specific acceptance criteria or an explicit study proving device performance against those criteria in the way typically expected for AI/ML device submissions. This submission predates the widespread use of AI/ML in medical devices and therefore lacks the detailed performance study information relevant to algorithms.

However, based on the general nature of a 510(k) submission for a non-AI/ML device, I can infer some aspects and highlight why other information requested is not present.

Inferred Acceptance Criteria and Reported Device Performance (based on common 510(k) practice for patient monitors):

The acceptance criteria for this type of device (patient monitors with software revisions) would generally revolve around demonstrating that the new software revision maintains the safety, effectiveness, and performance of the predicate devices. The study proving this would typically be a series of verification and validation tests.

Information Not Available in the Provided Text (and Explanations):

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

   • Explanation: This type of detail is not typically provided in a high-level 510(k) summary for a patient monitor software update. The "test set" here refers to the actual testing environment (e.g., simulators, human subjects, recorded physiological data), but specific numbers, provenance, or retrospective/prospective nature of data for such testing are not disclosed.

2. 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):

   • Explanation: For a patient monitor, "ground truth" for physiological parameters is often established through calibrated reference devices or established physiological models, rather than expert human interpretation in the way it is for image analysis in AI/ML. Therefore, the concept of "experts" in this context is not directly applicable.

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

   • Explanation: Adjudication methods are relevant for subjective interpretations, especially in AI/ML where multiple experts might disagree. For physiological monitoring, accuracy is typically measured against objective numerical standards from reference devices, so adjudication amongst experts wouldn't be part of the testing.

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:

   • Explanation: No. This is not an AI/ML device. MRMC studies are specific to evaluating how AI assistance impacts human performance, which is not applicable here.

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

   • Explanation: While the device's algorithms for physiological parameter measurement and alarm generation would have been tested in a standalone manner (e.g., against simulated signals or reference data), the term "standalone performance" is most commonly used in the context of AI/ML for its direct diagnostic or classification output. The submission does not detail specific standalone performance metrics for individual algorithms within the monitor.

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

   • Explanation: The "ground truth" for a patient monitor would be established using:
     • Reference Devices: Calibrated instruments for measuring ECG, blood pressure, SpO2, etc., against which the monitor's readings are compared.
     • Simulators: Devices that generate known physiological waveforms and values.
     • Clinical Data (less likely for initial software change, more for initial device clearance): Real patient data where physiological parameters are independently measured by gold-standard methods.
   • The document does not explicitly state which methods were used, but these are standard for patient monitor testing.

7. The sample size for the training set:

   • Explanation: This device is not an AI/ML device, so there is no "training set" in the machine learning sense. The software revision is based on traditional programming and engineering principles, not statistical learning from a dataset.

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

   • Explanation: As there is no training set for an AI/ML model, this question is not applicable.

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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters patients. Intended for monitoring, recording and alarming of of multiple physiological parameters of adults, pediatrics and neonates healthcare facilities. The MP20, MP30, MP40 and MP50 are in additionally intended for use in transport situations within healthcare facilities.

ST Segment monitoring is restricted to adult patients only.

The transcutaneous gas measurement (tcp02 / tcpCC2) is restricted to neonatal patients only.

The names of the devices are the Philips MP20, MP30, MP40, MP50, MP60, MP70, MP80 and MP90 IntelliVue Patient Monitors. The modification is the introduction of Release D.03 software for the IntelliVue patient monitor devices, MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90.

The Philips IntelliVue Patient Monitor, Release D.03, is a patient monitoring device that received 510(k) clearance (K060541) in 2006. The device is intended for monitoring, recording, and alarming multiple physiological parameters of adult, pediatric, and neonatal patients in healthcare facilities, with some models also suitable for in-hospital transport.

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The provided summary does not specify the sample size used for the test set or the data provenance. It generally refers to "system level tests, performance tests, and safety testing," but does not detail the specific number of patients, data records, or cases used in these tests. The nature of this device (patient monitor) suggests testing would likely involve simulated physiological data, bench testing, and potentially clinical data, but specifics are not given.

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

The provided summary does not mention the involvement of experts for establishing ground truth for a test set. Given that this is a patient monitoring device and the evaluation primarily focuses on substantial equivalence to predicate devices and meeting their specifications, the "ground truth" would likely be defined by the known physiological parameters and expected device responses based on engineering and medical standards, rather than expert interpretation of complex medical imagery or conditions.

4. Adjudication Method for the Test Set

The provided summary does not describe any adjudication method. As mentioned above, the evaluation seems to rely on established specifications and objective measures of device performance rather than subjective expert interpretations that would require adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done or mentioned in the provided summary. This type of study is typically performed for diagnostic imaging devices where human interpretation plays a significant role and the AI's impact on human performance is being assessed. For a patient monitor, the primary evaluation is about the accuracy and reliability of its physiological measurements and alarms.

6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) Was Done

The evaluation of the Philips IntelliVue Patient Monitor is fundamentally about its standalone performance in measuring and alarming physiological parameters. While it's a device that assists human caregivers, the "testing activities" described (system level, performance, safety) intrinsically refer to the device's inherent functional capabilities without necessarily evaluating a human-in-the-loop scenario impacting the device's measurements. The device itself acts in a standalone capacity by continuously monitoring and providing data and alarms.

7. The Type of Ground Truth Used

The ground truth for evaluating this device appears to be based on:

• Established Specifications: "Pass/Fail criteria were based on the specifications cleared for the predicate device." This implies adherence to predetermined acceptable ranges and accuracy for physiological measurements (e.g., heart rate, blood pressure, oxygen saturation, gas concentrations).
• Engineering and Medical Standards: The "performance, functionality, and reliability characteristics" are assessed against what are considered acceptable and safe parameters for patient monitoring in a clinical setting.
• Predicate Device Performance: The core of the 510(k) submission is to demonstrate "substantial equivalence" to legally marketed predicate devices, meaning its performance matches or is equivalent to established, already-cleared devices.

8. The Sample Size for the Training Set

The provided summary does not mention a training set or its sample size. Patient monitoring devices like the IntelliVue are typically developed and validated against known physiological signals and simulated data, rather than "training" in the machine learning sense from a specific dataset of patient cases. While the internal algorithms (e.g., for arrhythmia detection) might have been developed using data, the document does not elaborate on this.

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

Since a "training set" is not explicitly mentioned, the method for establishing its ground truth is also not described. For a traditional patient monitor, the underlying algorithms for parameter calculation and alarm generation are based on established physiological principles and signal processing, validated through rigorous engineering and clinical testing rather than a separate "training set" with explicit ground truth labels as seen in AI/ML applications.

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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters patients. Intended for monitoring, recording and alarming of of multiple physiological parameters of adults, pediatrics and neonates in hospital environments. The MP20, MP30, MP40 and MP50 are additionally intended for use in transport situations within hospital environments.

EASI 12-lead ECG is only for use on adult and pediatric patients.

ST Seqment monitoring is restricted to adult patients only.

The transcutaneous gas measurement (tcp02/tcpCO2) is restricted to neonatal patients only.

The names of the devices are the Philips MP20, MP30, MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitor. The modification is the introduction of Release B.1.1 software for the IntelliVue patient monitor devices, MP20, MP30, MP40, MP50 interfacing the Gas Analyzer model M1013A EGM (K041956). The modified devices have the same technological characteristics as the legally marketed predicate devices.

Here's a breakdown of the acceptance criteria and study information for the Philips IntelliVue Patient Monitor, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided text does not explicitly state specific quantitative acceptance criteria (e.g., "accuracy must be >95%") or detailed reported device performance in a numerical format for individual parameters. Instead, it makes a general statement about meeting specifications and reliability.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "system level tests, performance tests, and safety testing from hazard analysis" but doesn't quantify the number of test cases, patient data, or specific scenarios used.
• Data Provenance: Not explicitly stated. It's unclear if the testing involved real patient data, simulated data, or a combination, nor the country of origin. The regulatory submission is from Germany, suggesting testing might have occurred there or within Phillips' global network. It is implied to be prospective testing, as it's for a new software release.

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

Not mentioned in the document. The type of device (patient monitor) suggests that ground truth would likely be based on established physiological measurement standards and potentially clinical expert review, but this is not detailed.

4. Adjudication Method for the Test Set

Not mentioned in the document.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs. Without AI Assistance

No, an MRMC comparative effectiveness study is not applicable and was not performed. This device is a physiological patient monitor, not an AI-assisted diagnostic tool for human readers. Its primary function is to measure and display physiological parameters, and alarm if they fall outside set limits. There's no AI component for improving human interpretation in the way one would see in an imaging AI application.

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

Yes, the testing described appears to be a standalone evaluation of the device's performance, functionality, and reliability, without explicitly involving human-in-the-loop performance for its core functions. The software Release B.1.1 itself is the "algorithm only" component that was tested.

7. The Type of Ground Truth Used

The document does not specify the exact "type of ground truth" in the way one might for an imaging algorithm (e.g., pathology, outcomes data). For a patient monitor, ground truth would be established through a combination of:

• Known physical inputs/simulations: Using calibration equipment or simulators to generate precise physiological signals (e.g., a known ECG rhythm, a specific blood pressure waveform) and verifying the device's output against these known inputs.
• Comparison to reference devices: Comparing the monitor's readings to those of other validated and highly accurate reference monitoring devices.
• Technical specifications: Verifying that the device's measurements fall within the specified accuracy and precision limits for each parameter.

8. The Sample Size for the Training Set

Not applicable. This device is a patient monitor, not an AI/machine learning algorithm that requires a "training set" in the conventional sense. The "training" would involve the development and engineering process of the software itself, not a data-driven training pipeline for a predictive model.

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

Not applicable, as there is no "training set" in the context of an AI/machine learning algorithm for this device.

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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Intended for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in hospital environments.

EASI 12-lead ECG is only for use on adult and pediatric patients.

ST Segment monitoring is restricted to adult patients only.

The transcutaneous gas measurement (tcpO2 / tcpCO2) is restricted to neonatal patients only.

The Philips MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitor.

The provided text describes a 510(k) summary for the Philips IntelliVue Patient Monitors, specifically focusing on the introduction of Release B.0 software and new models. However, it does not include the details typically found in a study proving a device meets specific acceptance criteria in the context of AI/ML or diagnostic performance. This document is a regulatory submission for a patient monitor, which is a hardware device with software, not a diagnostic AI algorithm.

Therefore, many of the requested categories related to AI/ML or diagnostic performance studies (like sample size for test/training sets, experts for ground truth, MRMC studies, standalone performance, etc.) are not applicable or not provided in this document.

Here's an attempt to answer the questions based only on the provided text, indicating where information is not available:

Acceptance Criteria and Device Performance Study for Philips IntelliVue Patient Monitors (K032858)

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

The document refers to acceptance criteria generally but does not provide a specific table of quantitative acceptance criteria or detailed performance metrics.

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: Not specified. The document mentions "system level tests, performance tests, and safety testing" but does not detail the number of cases, patients, or data points used in these tests.
• Data Provenance: Not specified.

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/Not specified. This document is for a patient monitor (hardware and general software), not a diagnostic AI algorithm requiring expert-established ground truth for a test set in the typical sense of a diagnostic performance study. The "ground truth" for a patient monitor would be its accurate measurement and display of physiological parameters, which is validated through engineering tests against known standards.

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

Not applicable/Not specified. Adjudication methods like 2+1 or 3+1 are typically used in studies involving human interpretation (e.g., image reading) where multiple experts resolve disagreements to establish a ground truth. This is not the type of testing described for a patient monitor.

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 patient monitor, and the testing described is not an MRMC comparative effectiveness study comparing human readers with and without AI assistance for interpretation.

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

Not applicable. While the device contains algorithms for monitoring various physiological parameters (e.g., arrhythmia detection, ST segment monitoring), the document does not describe standalone algorithm performance testing in the context of an AI/ML diagnostic or predictive algorithm being evaluated against a ground truth as typically understood for this type of question. The "performance" mentioned refers to the overall device's ability to accurately measure and display parameters.

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

Not specified in detail. For a patient monitor, the "ground truth" for performance testing would typically involve established reference standards, calibrated equipment, and simulated physiological signals to ensure accuracy of measurements (e.g., ECG, blood pressure, temperature, O2 saturation). The document states "Pass/Fail criteria were based on the specifications cleared for the predicate device," implying performance was compared against predetermined technical specifications.

8. The sample size for the training set

Not applicable/Not specified. The document describes a software release (Release B.0) for established patient monitors, not the development of a novel AI/ML algorithm that requires a "training set" in the context of machine learning.

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

Not applicable/Not specified, as there is no mention of a "training set" in the context of machine learning for an AI algorithm.

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Indicated for use by health care professionals whenever there is a need for monitoring the physiological parameters of patients. Intended for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in hospital environments.

EASI 12-lead ECG is only for use on adult and pediatric patients.

ST Segment monitoring is restricted to adult patients only.

The transcutaneous gas measurement (tcp02 / tcpCO2) is restricted to neonatal patients only.

The Philips MP60, MP70, and MP90 IntelliVue Patient Monitor. The modification is updated software of the Philips Medical System MP60, MP70 and MP90 IntelliVue patient monitor devices.

The provided text describes the Philips MP60, MP70, and MP90 IntelliVue Patient Monitors and their clearance as substantially equivalent to previously marketed devices. However, the document does not contain a detailed study report with specific acceptance criteria and device performance metrics in the format requested.

Here's an analysis based on the available information:

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

The document explicitly states: "Pass/Fail criteria were based on the specifications cleared for the predicate device and test results showed substantial equivalence." It does not provide specific quantitative acceptance criteria (e.g., accuracy, precision) or numerical reported device performance values. It broadly states that the new device "meets all reliability requirements and performance claims."

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 does not specify the sample size used for the test set. It does not mention the country of origin of the data or whether the data was retrospective or prospective.

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)

This information is not provided in the document. As this is a patient monitoring device, the ground truth would likely be established through direct physiological measurements or clinical observation, rather than expert interpretation of images. However, the exact methodology for ground truth establishment for testing is not detailed.

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

The document does not describe any adjudication method.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted or described. This type of study is typically relevant for interpretative diagnostic devices (like imaging AI), not for physiological monitors where the device itself measures and presents data. There is no mention of "human readers" or "AI assistance" in the context of improving human performance.

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

The entire study described is essentially a standalone performance assessment of the device. The "updated software" is an integral part of the patient monitor, and its performance is evaluated as a standalone system against the predicate device's specifications. The evaluation is focused on the device's ability to accurately monitor physiological parameters.

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

The document does not explicitly state the type of ground truth used. For physiological monitors, ground truth is typically established by using highly accurate reference measurement systems (e.g., a calibrated pressure transducer for blood pressure, a reference ECG machine for arrhythmia detection) or direct observation of physiological events.

8. The sample size for the training set

The document does not mention a training set or its sample size. This is a common characteristic of device clearances that involve software updates for existing devices, where the focus is on verification and validation against established specifications, rather than development of new algorithms requiring extensive training data.

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

Since no training set is mentioned, there is no information on how ground truth for a training set was established.

Summary of the Study:

The study described is a verification and validation effort to demonstrate that the updated software for the Philips MP60, MP70, and MP90 IntelliVue Patient Monitors maintains the same performance, functionality, and reliability as the legally marketed predicate devices. The methodology involved "system level tests, performance tests, and safety testing from hazard analysis." The "Pass/Fail criteria were based on the specifications cleared for the predicate device." The conclusion was that "test results showed substantial equivalence" and the device "meets all reliability requirements and performance claims." This type of submission falls under the 510(k) pathway, which primarily focuses on demonstrating substantial equivalence to a predicate device, rather than proving novel clinical effectiveness through extensive clinical trials with detailed statistical reporting of performance metrics.

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The Component Monitoring System and V24/V26 family of patient monitors is intended for the monitoring, recording, and alarming of multiple physiological parameters. The devices are indicated for use in health care facilities by healthcare professionals whenever there is a need for monitoring the physiologic parameters of adult, neonatal, and pediatric patients.

The modification in this submission is the addition of an alternate blood analysis module to the current plug-in i-STAT module as a component to the CMS and V24/V26 family of patient monitors. The proposed M3560A Blood Analysis Portal System is substantially equivalent to the Diametrics IRMA Blood Analysis System but provides a plug-in interface instead of an RS232 interface connection. Only Diametrics analysis cartridges (IRMA cartridges) are used with this module. The new M3560A Blood Analysis Portal System is also smaller than the current Diametrics IRMA Blood Analysis System and does not operate via battery power.

This 510(k) submission describes the M3560A Blood Analysis Portal System Plug-In Module for the Philips Component Monitoring System and V24/V26 family of patient monitors. It does not contain information about the acceptance criteria or a specific study proving the device meets acceptance criteria.

The document focuses on:

• Device Description: The M3560A is an alternate blood analysis module that provides a plug-in interface, is smaller, and does not operate via battery power compared to its predicate device (Diametrics IRMA Blood Analysis System). It uses only Diametrics analysis cartridges (IRMA cartridges).
• Intended Use: Monitoring, recording, and alarming of multiple physiological parameters for adult, neonatal, and pediatric patients in healthcare facilities by healthcare professionals.
• Substantial Equivalence: It claims substantial equivalence to several predicate devices, including the Agilent Component Monitoring System, HP Viridia Component Monitoring System, M1032A VueLink Plug-In Module, Diametrics Medical, Inc. IRMA Blood Analysis System, HP Component Monitoring System, and i-STAT 200 Portable Clinical Analyzer.
• Performance Standards: It states that components with arrhythmia and alarm capability remain compliant with 21 CFR Part 898, as they are unchanged from previous cleared submissions.
• Fundamental Technology: The fundamental scientific technology is described as unchanged from the predicate devices.
• Design Controls: Mentions that "Verification, validation, and testing activities will be successfully conducted and completed prior to commercialization to establish the safety, performance, and reliability characteristics of the M3560A Blood Analysis Portal System," including system level tests, integration tests, safety tests, and predicate device comparison. However, the details of these tests, including specific acceptance criteria or study results, are not provided in this summary.

Therefore, based on the provided text, the requested information regarding acceptance criteria and the study proving the device meets them cannot be extracted. The document states that testing will be conducted, implying this 510(k) summary is submitted before the detailed results of those tests are available or summarized for the FDA in this specific document.

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