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The RAUMEDIC® MPR2 is indicated for use by qualified neurosurgeons or neurointensivists for measurement of intracranial pressure, temperature as well as to determine the partial oxygen pressure ptiO2 of the interstitial fluid.

The MPR2 is a small bedside monitor for measuring, displaying and monitoring of physiological parameters in the human brain which is used only in conjunction with RAUMEDIC ® -catheters. Analog signals can be relayed to a series-connected bedside monitor via its 2 analog outputs. Optionally, the device can be operated in stand-alone mode. The power is supplied by either the internal rechargeable battery or by an external, medically approved wide range wall plug transformer.

The MPR2 is a diagnostic device with physiological limit value monitor for measuring, displaying and monitoring the following physiological parameters: oxygen partial pressure (pO2), intracranial pressure (ICP) and brain temperature (T). These parameters are determined exclusively by connecting RAUMEDIC® catheters for single channel ICP measurement or with RAUMEDIC® multi-parameter catheters for combined measurement of ICP, pO2 and temperature (thermistor-based with YSI400 characteristic) or combinations derived from them such as pO2 and temperature. Optionally the pO2 and ICP signals can be relayed via the 2 analog outputs to a bedside monitor with limit value monitoring.

The provided document is a 510(k) Premarket Notification from the FDA regarding the RAUMEDIC® MPR2 logO DATALOGGER. This document is focused on demonstrating substantial equivalence to previously marketed predicate devices, rather than presenting a performance study against a defined set of acceptance criteria for a new AI/Machine Learning device.

Therefore, many of the typical acceptance criteria and study design elements requested for an AI/ML device (e.g., sample size for test/training sets, data provenance, number of experts for ground truth, MRMC studies, standalone performance, etc.) are not present in this type of regulatory submission.

Instead, the document focuses on:

• Intended Use/Indications for Use: Comparing the intended use of the new device to predicates.
• Technological Characteristics: Comparing features, materials, and form/function.
• Performance Testing: Stating that "extensive performance testing" was conducted and results showed the device is "safe for its intended use," but without providing specific test data, methodologies, or acceptance criteria in the format typically used for AI/ML device validation. The performance parameters listed in Table 1 (Accuracy of ICP Measurement, Temperature Measurement, etc.) are characteristics of the device itself and are compared to the predicate devices, serving as a form of "acceptance criteria" for substantial equivalence.

Given this context, I will extract the information that is available and clearly state what information is not present.

Acceptance Criteria and Device Performance (Derived from Substantial Equivalence Comparison)

The acceptance criteria are not explicitly stated as pass/fail thresholds in the traditional sense for a de novo device. Instead, they are implied through the comparison to predicate devices, where the new device's performance characteristics must be "substantially equivalent" or better. The reported device performance is listed alongside the predicate devices.

Table 1: Device Performance Comparison (Acting as Acceptance Criteria Check)

Study Details (Information Not Provided/Applicable for this Type of Submission)

This document is a 510(k) submission for a non-AI/ML medical device, focusing on substantial equivalence to predicate devices. As such, the typical detailed study information requested for a novel AI/ML device is not included.

1. Sample size used for the test set and the data provenance: Not applicable/not provided for this type of device. The performance testing mentioned is likely bench testing or pre-clinical validation, not clinical data for an AI model.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for physiological measurements is based on reference methods and calibration, not expert consensus on images.
3. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
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: Not applicable, as this is not an AI/ML device.
5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a measurement device, not an algorithm.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc): For a medical device measuring physiological parameters (ICP, temp, ptiO2), "ground truth" is established through:
   • Calibration Standards: Using known physical standards for pressure, temperature, and gas concentration.
   • Reference Devices: Comparing measurements to established, highly accurate reference instruments.
   • Controlled Bench Testing: Testing device accuracy and performance under controlled laboratory conditions, including simulated physiological ranges.
     The document states "extensive performance testing" was done, and the specific accuracy values in the table are the results of such testing.
7. The sample size for the training set: Not applicable, as this is not an AI/ML device.
8. How the ground truth for the training set was established: Not applicable.

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