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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, MP20, MP30, MP40, and MP50 are additionally intended for use in transport situations within hospital environments. The MP5 is also intended for use during patient transport outside of a hospital environment.

The Philips MP2, X2, MP5, MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90 IntelliVue Patient Monitors. The modification is the introduction of the models MP2 and X2 IntelliVue Patient Monitors and the introduction of software release F.00 for the entire IntelliVue Patient Monitors family, models MP2, X2, MP5, MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90.

The Philips IntelliVue Patient Monitors (models MP2, X2, MP5, MP20, MP30, MP40, MP50, MP60, MP70, MP80, and MP90, with software release F.00) are intended for monitoring and recording multiple physiological parameters and generating alarms for adults, pediatrics, and neonates in hospital environments. Some models (MP2, X2, MP20, MP30, MP40, MP50) are also for hospital transport, and the MP5 is for transport outside the hospital. They are intended for use by healthcare professionals.

Acceptance Criteria and Device Performance:

The provided 510(k) summary states that "Pass/Fail criteria were based on the specifications cleared for the predicate devices." However, it does not provide specific quantitative acceptance criteria or detailed reported device performance metrics in a table. It generally states that "test results showed substantial equivalence" and that the "results demonstrate that the Philips IntelliVue Patient Monitors meet all reliability requirements and performance claims."

Study Details:

1. Sample size for the test set and data provenance:
   The document does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It only mentions "system level and regression tests, safety and performance tests, EMC and environmental tests, such as testing from the hazard analysis."

2. Number of experts used to establish the ground truth for the test set and qualifications:
   This information is not provided in the document.

3. Adjudication method for the test set:
   This information is not provided in the document.

4. Multi-reader multi-case (MRMC) comparative effectiveness study:
   No MRMC comparative effectiveness study is mentioned. The submission focuses on demonstrating substantial equivalence to predicate devices through technical verification and validation, not comparative effectiveness with human readers.

5. Standalone (algorithm only without human-in-the-loop performance) study:
   The submission describes testing activities for the device itself ("Verification, validation, and testing activities establish the performance, functionality, and reliability characteristics of the modified devices"). This implies standalone performance testing of the device's functions, but details on specific standalone performance metrics or a study explicitly labeled as such are not provided. The device is a patient monitor, implying continuous monitoring of physiological parameters by the device itself before human interpretation.

6. Type of ground truth used:
   The document does not explicitly state the type of ground truth used for performance evaluation, beyond stating that "Pass/Fail criteria were based on the specifications cleared for the predicate devices." For physiological monitoring, ground truth would typically come from calibrated reference measurements or expert clinical assessment for phenomena like arrhythmia detection.

7. Sample size for the training set:
   This information is not provided. The document describes verification and validation activities for the device, but does not mention "training sets," which implies that this device might not incorporate machine learning or AI that requires a distinct training phase in the way a diagnostic imaging AI would.

8. How the ground truth for the training set was established:
   As no training set is explicitly mentioned or implied to be relevant to the device's development as described, this information is not provided.

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Indicated for use by health care professionals monitoring the physiological parameters whenever there is a need for monitoring, recording and alarming of multiple physiological parameters of adults, pediatrics and neonates in hospital environment and during transport within hospital environments. Models MP20, MP30, MP40 and MP50 are additionally intended for use in transport situations within hospital environments. ST Segment monitoring is restricted to adult patients only. The transcutaneous gas measurement (tcp0₂ / tcpCO₂) is restricted to neonatal patients only.

The Philips MP20, MP30, MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitors.

The Philips MP20, MP30, MP40, MP50, MP60, MP70, and MP90 IntelliVue Patient Monitors underwent verification, validation, and testing activities to establish their performance, functionality, and reliability characteristics. The study that proves the device meets the acceptance criteria is described as follows:

1. Table of Acceptance Criteria and Reported Device Performance:

Note: The 510(k) summary explicitly states that "Pass/Fail criteria were based on the specifications cleared for the predicate device." However, the exact quantitative metrics for these specifications are not provided in the summary.

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It generally refers to "system level tests, performance tests, and safety testing."

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

This information is not provided in the 510(k) summary. The testing appears to be based on engineering and predefined specifications rather than expert adjudicated ground truth on clinical data.

4. Adjudication Method for the Test Set:

This information is not provided. The testing described appears to be based on objective pass/fail criteria derived from predicate device specifications, rather than a clinical adjudication process.

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 explicitly mentioned or performed. The submission focuses on substantial equivalence to predicate devices through technical testing.

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

The testing described appears to be standalone performance testing of the device's hardware and software against predefined specifications. The summary does not describe any human-in-the-loop performance evaluation in the context of this submission.

7. The type of Ground Truth Used:

The "ground truth" for the device's performance was based on the "specifications cleared for the predicate device." This implies a comparison to established technical and performance requirements of previously approved devices, rather than clinical outcomes data or pathology.

8. The Sample Size for the Training Set:

This information is not applicable or provided. The submission describes a medical device update (software release and new models) and does not suggest the use of machine learning algorithms that would require a distinct "training set" for model development.

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

This information is not applicable or provided, as the submission does not detail the use of a training set for machine learning. The device's performance validation is based on adherence to the specifications of predicate devices.

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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.

The Philips MP60, MP70, and MP90 IntelliVue Patient Monitor with Portal Technology and Wireless LAN. The modification is primarily a hardware based change that offers, as an option, the addition of an externally mounted wireless network connection to the Philips Medical System MP60, MP70 and MP90 IntelliVue patient monitor devices.

The provided text is a 510(k) summary for the Philips MP60, MP70, and MP90 IntelliVue Patient Monitors with Portal Technology and Wireless LAN. It describes the device, its classification, and asserts substantial equivalence to previously cleared devices. However, it does not contain detailed information about specific acceptance criteria or a study proving the device meets those criteria in the way a clinical performance study would.

Instead, the summary focuses on verification testing activities to establish performance and reliability characteristics, and safety testing from the risk analysis. This type of submission (510(k)) for a patient monitor and its wireless adapter typically relies on demonstrating substantial equivalence to existing devices through engineering and functional testing, rather than a clinical efficacy study with specific performance metrics against a ground truth as one might expect for an AI/ML diagnostic device.

Therefore, many of the requested fields cannot be filled from the provided text because such a study was not described.

Here's a breakdown based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document states: "Verification testing activities were conducted to establish the performance and reliability characteristics of the new device. Testing involved functional level tests and safety testing from the risk analysis."

However, no specific quantitative acceptance criteria (e.g., sensitivity, specificity, accuracy for a particular physiological parameter) or their corresponding performance results are reported in this 510(k) summary. The submission focuses on demonstrating substantial equivalence to predicate devices, implying that if the new device performs similarly and meets safety standards, it is acceptable.

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

Not applicable. The document describes "functional level tests and safety testing," which are typically internal engineering and validation tests, not clinical studies with a "test set" in the context of diagnostic performance. There is no mention of patient data being used for a performance evaluation in this summary.

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

Not applicable. No "ground truth" derived from expert consensus for a clinical performance study is described.

4. Adjudication method for the test set

Not applicable. No clinical performance study requiring adjudication is described.

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 with a wireless adapter, not an AI-based diagnostic tool. No MRMC study or AI-assistance evaluation is mentioned.

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

Not applicable. This device is a patient monitor, and its "performance" is inherent to its sensors and measurement capabilities, not a standalone algorithm in the sense of an AI/ML product.

7. The type of ground truth used

Not applicable. For this type of device (patient monitor), "ground truth" would generally refer to highly accurate reference measurements from calibrated equipment during functional testing, or clinical reference standards for physiological parameters. The summary doesn't detail the specifics of such ground truth used in their verification activities.

8. The sample size for the training set

Not applicable. This document pertains to a medical device (patient monitor with wireless capabilities), not an AI/ML model that requires a training set.

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

Not applicable. As above, this document does not describe an AI/ML model with a training set.

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