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Indicated for central monitoring of multiple adult, pediatric, and neonatal patients; and where the clinician decides to monitor cardiac arrhythmia of adult, pediatric, and neonatal patients and/or ST segment of adult patients to gain information for treatment, to monitor adequacy of treatment, or to exclude causes of symptoms.

M3290A IntelliVue Information Center Software Release E.01 and IntelliVue Clinical Network on VLAN. The modification is a change that permits operation of the IntelliVue Clinical Network on customer provided IEEE 802.1q compatible VLAN network infrastructure.

This 510(k) pertains to a software update (Release E.01) for the M3290A IntelliVue Information Center and its operation on a VLAN. The submission focuses on demonstrating substantial equivalence to previously cleared versions of the device, rather than presenting a de novo study with acceptance criteria and performance metrics for a new medical device. The document primarily highlights that the updated device maintains the same performance, functionality, and reliability characteristics as its predicate devices.

Therefore, much of the requested information regarding detailed acceptance criteria, specific performance metrics, sample sizes for test/training sets, ground truth establishment, and expert involvement for a new device's performance evaluation is not explicitly provided in this 510(k) summary. The document emphasizes verification, validation, and testing activities to ensure the updated software and network capabilities meet established specifications for the predicate device.

However, based on the provided text, here's what can be extracted and inferred:

1. A table of Acceptance Criteria and the Reported Device Performance:

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 for Test Set: Not specified. The document states "Testing involved system level tests, integration tests, environmental tests, and safety testing from hazard analysis." This suggests a battery of tests rather than a single, defined patient-data test set.
• Data Provenance: Not specified. Given the nature of the update (software and network compatibility), the testing might have been primarily internal engineering and software validation, rather than extensive clinical data.

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

• Not specified. The ground truth for this type of software and network update would likely be based on technical specifications and expected system behavior, rather than expert clinical consensus on patient data.

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

• Not specified. Adjudication methods are typically used for clinical endpoints where there's variability in interpretation among experts, which is not directly applicable to a software and network compatibility update.

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 done or mentioned. This is a software and network compatibility update for a physiological monitor, not an AI-assisted diagnostic tool.

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

• The testing described ("system level tests, integration tests, environmental tests, and safety testing") would inherently involve evaluation of the algorithm and system performance in a standalone manner (without specific human-in-the-loop clinical performance evaluation as might be done for diagnostic AI). However, this isn't a standalone AI performance study.

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

• For this type of device update, the "ground truth" would be the successful adherence to pre-defined technical specifications, functional requirements, and reliability standards of the predicate device, as well as successful operation within the new VLAN environment. This is an engineering and software validation ground truth, not a clinical diagnostic ground truth.

8. The sample size for the training set:

• Not applicable. This is not a machine learning or AI device that requires a training set in the conventional sense. The "training" would be the development and refinement of the software, and the "test" would be the verification/validation.

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

• Not applicable, as no training set for machine learning was used.

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