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The Philips IntelliSpace Perinatal Obsterical Information Management System is indicated for obsterior and after pregnancy, who require monitoring in a healthcare setting.

The Philips IntelliSpace Perinatal system provides:

• Basic and advanced fetal trace alarming for both antepartum and intrapartum patients.
• Central monitoring of maternal alarming.
• Documentation capabilities and data storage.
• Viewing and alarming of patient physiologic data, at remote locations, via the healthcare facility Remote Desktop Session (RDS).
• An interface to launch the Philips IntelliVue XDS Remote viewing and operating of compatible patient monitors.

The Philips IntelliSpace Perinatal Rev. K.00 is a patient-oriented, departmental information management system for the obstetrical care environment. It covers OB care in so far as it is relevant for GYN visits, pregnancy, labor, birth and newborn documentation. It combines surveillance and alarming with comprehensive patient documentation and data storage into one system that covers the continuum of obstetrical care across one or more pregnancies, from the first antepartum visit until delivery and discharge.

The provided document is a 510(k) summary for the Philips IntelliSpace Perinatal Revision K.00. This document describes the device, its intended use, and the testing performed to demonstrate its substantial equivalence to a predicate device.

However, it does not contain details about specific acceptance criteria related to a performance study (e.g., accuracy, sensitivity, specificity) for an AI/ML-based device. The document primarily focuses on non-clinical verification and validation (V&V) activities for software modifications and does not describe a clinical performance study with human readers or an algorithm-only standalone performance study.

Therefore, I cannot populate the requested table and answer many of the questions directly from the provided text. The information given is at a higher level, focusing on regulatory compliance for a software system that manages obstetrical information and integrates with other monitoring devices, rather than a diagnostic AI algorithm with quantifiable performance metrics.

Here is what I can extract and infer based on the document:

1. Table of Acceptance Criteria and Reported Device Performance:

• The document states that "All specified pass/fail criteria have been met" for the nonclinical V&V activities. However, it does not specify what those exact performance criteria are in terms of numerical metrics (e.g., sensitivity, specificity, accuracy) for an AI/ML component. The "performance" described is about the functionality and safety of the software modification, not a diagnostic accuracy metric.

  Acceptance Criterion Type	Specific Criterion (If available in document)	Reported Device Performance (If available in document)
  Functionality	Correctly hand over start-up parameters to XDS software	Met: "conducted tests demonstrate that start-up parameters provided by the modified IntelliSpace Perinatal software are correctly handed over to the XDS software"
  	Correct operation of patient monitors via XDS	Met: "compatible IntelliVue patient monitors can be correctly operated via the XDS software."
  Safety	Effectiveness of implemented risk mitigation measures	Met: "Test results confirmed the effectiveness of implemented risk mitigation measures."
  General V&V	All specified pass/fail criteria	Met: "All specified pass/fail criteria have been met."

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

• Sample Size: Not explicitly stated as this was a software engineering V&V activity, not a clinical trial or performance study on a specific dataset. The tests were likely performed on a simulated environment or
  test cases designed to validate software functionality.
• Data Provenance: Not applicable in the context of a "test set" for an AI/ML performance study. The testing was of the software's functionality and safety, not its diagnostic accuracy on patient data.

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. The document describes software verification and validation, which typically involves software engineers and quality assurance professionals, not clinical experts establishing ground truth for diagnostic outputs.

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

• Not applicable. As no clinical ground truth was established by experts for a test set, no adjudication method was mentioned.

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:

• No MRMC study was performed or described. The document explicitly states: "Therefore, a clinical study was not needed for the changes provided with the Philips IntelliSpace Perinatal Obstetrical Information Management System software revision Rev.K.00." The modifications were software updates related to integration and data management, not an AI assisting human readers in diagnosis.

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

• No standalone algorithm performance study was performed or described. The device is a perinatal information management system, not a standalone diagnostic AI algorithm. Its function is to provide "Basic and advanced fetal trace alarming," "Central monitoring of maternal alarming," and "Documentation capabilities and data storage." These are system functions, not typically evaluated as a "standalone algorithm" in the same way a diagnostic AI would be. The closest described is functional testing of the software's ability to hand off data and control.

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

• Not applicable. The ground truth in this context refers to the expected behavior and output of the software functions as defined by the system requirements and design specifications, not clinical markers. This is a V&V process for software, where "ground truth" would be software specifications and expected functional outcomes.

8. The sample size for the training set:

• Not applicable. The document describes the V&V of a non-AI software system; there is no mention of a training set as would be found with an AI/ML model.

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

• Not applicable, as no training set was used or mentioned.

Summary of what the document does convey regarding validation:

• The modifications to the Philips IntelliSpace Perinatal system (Revision K.00) are primarily software-based, focusing on:
  • Launching the Philips IntelliVue XDS Application for remote access to patient monitors.
  • Adapting web TS client resolution for mobile devices.
  • Adding configurable columns to electronic chalkboards.
  • Providing additional options for documentation and reporting.
  • HL7-based interfacing with ADT systems.
  • Updating Windows Server compatibility.
  • Adding more supported HL7-IHE profiles.
  • Introducing roles and permission-based patient data access control.
  • Adjusting alarm sound pressure levels.
  • Adjusting system scalability (fetal monitors and client sessions).
• Nonclinical V&V activities were performed:
  • Safety and Performance testing according to ANSI/AAMI/IEC 62304:2006.
  • Tests required by Hazard Analysis.
  • Functional tests of the modified software, including the new XDS launch feature, based on FDA guidance for software in medical devices.
  • The software was categorized as having a "MAJOR" level of concern.
• Conclusion from V&V: The tests demonstrated that start-up parameters are correctly handed over to XDS, and patient monitors can be operated via XDS. All pass/fail criteria were met.
• Clinical Data: "a clinical study was not needed" because the "similarities and differences... were determined not to have a significant impact on the device's performance, the clinical performance, and the actual use scenarios."

In essence, this document is a regulatory submission for a software update to an existing information management system, not a submission for a novel AI/ML diagnostic tool requiring a performance study against a clinical ground truth.

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The Endophys Blood Pressure Monitor Model 651 is intended for use in a catheterization laboratory to continuously provide systolic, diastolic and mean blood pressure based on the output of the Endophys Pressure Sensing Sheath in patients undergoing therapeutic and/or diagnostic procedures involving percutaneous vascular access.

The Endophys Blood Pressure Monitor Model 651 is a blood pressure computer that computes and continuously displays systolic, diastolic and mean blood pressure values. The BPM obtains an optical signal from the Endophys Pressure Sensing Sheath, which is a standalone catheterization sheath that is inserted percutaneously during intravascular diagnostic or interventional procedures. The BPM converts the optical transducer data to electrical signals and displays blood pressure measurements.

The Endophys Blood Pressure Monitor Model 651 is powered by a standard AC power adapter. The Endophys Blood Pressure Monitor Model 651 is used outside of the sterile environment and has standard alerts and alarms.

The Endophys Blood Pressure Monitor Model 651 has an operating pressure range of 0-300 mmHg with an accuracy of ±2mmHg or ±4% of the reading, whichever is greater.

The provided document is a 510(k) premarket notification letter and summary for a Blood Pressure Monitor. It outlines the device's indications for use, technological characteristics, and performance data provided to support substantial equivalence to a predicate device.

However, the request asks for information related to a study proving a device (presumably an AI/imaging device, given the context of "human readers" and "ground truth" derived from experts) meets acceptance criteria, specifically for applications like those that might involve image analysis or disease detection.

The provided document describes a Blood Pressure Monitor, which is a physiological measurement device, not an AI-assisted diagnostic imaging device. Therefore, many of the requested criteria (e.g., sample size for test set, number of experts, adjudication method, MRMC studies, standalone algorithm performance, training set details) are not applicable to the type of device described in the input text.

The acceptance criteria and performance data for a blood pressure monitor would typically involve accuracy against a reference standard, electrical safety, electromagnetic compatibility, and functional testing, as mentioned in the document. It does not involve AI model performance metrics like sensitivity, specificity, or AUC, nor does it involve human expert ground truth establishment for image interpretation.

Given this fundamental mismatch, I can only provide information relevant to the blood pressure monitor based on the provided text, recognizing that it addresses very different types of acceptance criteria and study methodologies than those implied by the prompt's structured questions.

Based on the provided document (K160945 for the Endophys Blood Pressure Monitor, Model 651), the following is applicable:

The device is a Blood Pressure Monitor, not an AI/imaging device requiring multi-reader studies or complex ground truth establishment by experts for diagnostic interpretation. Its acceptance criteria and performance studies are focused on its accuracy in measuring blood pressure and its electrical and safety compliance.

Acceptance Criteria and Reported Device Performance (for a Blood Pressure Monitor)

Study that Proves the Device Meets Acceptance Criteria:

The document states that "The following performance data were provided in support of the substantial equivalence:"

• Functional Testing
• Electrical Safety (IEC 60601-1)
• Electromagnetic compatibility (IEC 60601-1-2)
• Safety and Essential Performance for Invasive blood pressure monitoring equipment (IEC 60601-2-34)

The conclusion states: "The modified Endophys Blood Pressure Monitor Model 651 met all specified criteria and did not raise new safety or performance questions. Based on the design verification performance the modified Endophys Blood Pressure Monitor was found to have a safety and effectiveness profile that is similar to the predicate device."

Addressing the prompt's specific questions with respect to the provided document (and highlighting non-applicability):

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

   • See table above. The document primarily focuses on technical and safety standards compliance and intrinsic accuracy, not clinical diagnostic performance metrics.

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

   • Not specified in the document for the performance testing. These are typically design verification/validation tests for electro-mechanical devices, often conducted in-house to demonstrate compliance with standards and specifications. The document does not indicate a clinical study with a patient test set size or data provenance details in the way an AI diagnostic device submission would.

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

   • Not applicable. This device measures blood pressure. "Ground truth" would be established by a primary reference standard source for blood pressure measurement, not by human experts interpreting data.

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

   • Not applicable. This pertains to expert consensus for complex diagnostic interpretation, which is not relevant to a blood pressure 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 is a standalone physiological measurement device, not an AI-assisted diagnostic tool involving human readers.

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

   • Partially applicable/Re-interpretation: The performance outlined (accuracy, electrical safety, etc.) is the "standalone" performance of the blood pressure monitor device itself. It operates as an algorithm (converting optical transducer data to electrical signals and displaying blood pressure) without human interpretive input beyond observing the displayed values. There's no separate "human-in-the-loop" aspect to its fundamental function for evaluation.

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

   • Based on standard practices for such devices: The ground truth for blood pressure measurement accuracy would typically be established by comparison to a highly accurate reference blood pressure measurement device (e.g., a manometer calibrated to a known standard, or another validated invasive blood pressure measurement system). It would not be expert consensus, pathology, or outcomes data in this context. The document does not explicitly state the reference method used.

8. The sample size for the training set

   • Not applicable. This is not a machine learning/AI device that requires a "training set" in the common understanding of the term for AI model development.

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

   • Not applicable. See point 8.

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