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The CSM monitor is intended for use in monitoring the hypnotic state of the brain by data acquisition of EEG signals of the anaesthetized or sedated patient in all areas of the hospital.

The Cerebral State Monitor (CSM) is a non-invasive measurement tool for use by trained healthcare professionals to measure the level of consciousness (LOC) in all areas of the hospital. Based on EEG, an index (CSI) is calculated, which is used in the estimation of LOC. The CSM displays the CSI but does not perform any data interpretation (i.e., all data interpretation is performed by a physician).

The provided document is a 510(k) summary for the Cerebral State Monitor (CSM) and the FDA clearance letter. It states that the device is substantially equivalent to predicate devices but does not contain detailed information about acceptance criteria or specific studies showcasing performance data in the structured format requested. The document explicitly states: "The CSM displays the CSI but does not perform any data interpretation (i.e., all data interpretation is performed by a physician)". This indicates that the device itself is a measurement tool and not an AI or algorithm-driven diagnostic device as implied by some of the questions.

Therefore, many of the requested details, such as sample sizes for test and training sets, the number and qualifications of experts, adjudication methods, MRMC studies, standalone performance details, and how ground truth for training was established, are not present in this regulatory document.

However, based on the information available, I can construct a table for the acceptance criteria and intended use, and provide what limited information is available regarding performance.

Acceptance Criteria and Device Performance (Based on available information)

Study Details and Data Provenance

1. Sample size used for the test set and the data provenance: Not specified in the provided document. As the device is primarily a measurement tool and does not perform "data interpretation," it is unlikely a "test set" in the context of an algorithm's diagnostic performance was used or described in this type of submission. Performance is assessed against technical specifications and equivalence to predicate devices.
2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not specified. Ground truth in the context of diagnostic accuracy is not directly applicable here as the device does not perform diagnosis. The document states "all data interpretation is performed by a physician."
3. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable based on the information provided.
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. The CSM is a monitoring device that provides an index (CSI) for human interpretation; it is not described as an AI-assisted diagnostic tool designed to improve human reader performance.
5. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The document states, "The CSM displays the CSI but does not perform any data interpretation (i.e., all data interpretation is performed by a physician)." This confirms that the device is not designed for standalone diagnostic performance; it is a measurement tool.
6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not specified as the device does not perform diagnosis. The "ground truth" for a monitor typically relates to the accuracy and reliability of its measurements against established physiological standards or reference devices, which is not detailed here. The "level of consciousness" is a clinical assessment made by a physician using various inputs, including the CSI.
7. The sample size for the training set: Not specified and likely not applicable in the context of an AI training set, as the device is not described as an AI/ML algorithm for diagnosis.
8. How the ground truth for the training set was established: Not specified and likely not applicable for the reasons stated above.

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The BIS/EEG Module is intended for use under the direct supervision of a licensed healthcare practitioner or by personnel trained in its proper use. The BIS/EEG Module is intended for use on adult and pediatric patients within a hospital or medical facility providing patient care to monitor the state of the brain by data acquisition of EEG signals.

The BIS/EEG Module is intended to monitor the Bispectral Index (BIS), a processed EEG variable which may be used as an aid in monitoring the effects of certain anesthetic agents.

The BIS/EEG Module is part of a modular system used to monitor the state of the brain by data acquisition of EEG signals. The module is also used as an aid in monitoring the effects of certain anesthetic agents using Aspect's Bispectral Index® parameter.

BIS/EEG Module works as a component of a GE Medical Systems Information Technologies host monitoring system and does not function on its own. The BIS/EEG Module provides information obtained from the sensors/electrodes to the bedside monitor for display.

The provided document is a 510(k) summary for the BIS/EEG Module, declaring substantial equivalence to a predicate device, rather than a study report detailing specific acceptance criteria and performance metrics for the device itself. Therefore, many of the requested details are not present in this document.

Here's what can be extracted and what is not available:

1. Table of Acceptance Criteria and Reported Device Performance:

Explanation: The document states that "The BIS/EEG Module and its host patient monitoring system comply with the voluntary standards as detailed in Section 9 of this submission." However, Section 9 (which would detail these standards and associated acceptance criteria) is not provided in the given excerpt. Without Section 9, specific numerical or qualitative acceptance criteria and reported performance values related to those criteria are not available. The conclusion simply states that the module "is as safe, as effective, and perform as well as the predicate device," which is a general statement of substantial equivalence, not a detailed performance report against specific criteria.

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

• Sample size: Not specified.
• Data provenance: Not specified.

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

• Not applicable. This information would be relevant to studies involving human interpretation or clinical outcomes, which are not detailed here. The focus of this 510(k) summary is on technical performance relative to a predicate device.

4. Adjudication method for the test set:

• Not applicable for the information provided.

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:

• Not applicable. This device is an Electroencephalograph (EEG) module that monitors brain state and the effects of anesthetic agents by processing EEG signals. It's a sensor/processing unit, not an AI-assisted diagnostic tool that human readers would use to improve their interpretation of images or other subjective data. Therefore, an MRMC study in this context is unlikely or not relevant based on the described intended use.

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

• The document describes "Testing on unit level (Module verification)" and "Integration testing (System verification)", which imply standalone and integrated performance testing of the device's algorithms and hardware. However, specific details of these tests, including the metrics and results of such standalone performance, are not provided. The phrase "algorithm only" may not be entirely appropriate as this is a hardware and software system.

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

• Not specified. Given the nature of an EEG module, ground truth for performance testing would likely involve established physiological measurements or simulated signals to verify accuracy and precision, but no specifics are provided in this summary.

8. The sample size for the training set:

• Not applicable. The document does not describe the development of a machine learning or AI model that would typically involve a "training set." The device "employs the same functional scientific technology as its predicate device," suggesting an established technology rather than a newly trained algorithm.

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

• Not applicable (see point 8).

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