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The Sarns 8000 Cardioplegia Monitor is indicated to measure extracorporeal line pressure and signal when the pressure exceeds the operator-set limit. either providing an alert or triggering an alarm which stops the cardioplegia pump only. The monitor also measures temperatures of the patient or extracorporeal circuit, counts the time during and between cardiopleqia doses, and tracks the volume of cardioplegia delivered.

The Sarns 8000 Cardioplegia Monitor is only compatible with the Sarns 8000 Modular Perfusion System. The Sarns 8000 Cardioplegia Monitor can display two pressures and three temperatures from attached probes. The pressure has alert and alarm levels which are set by the operator. The Sarns 8000 Cardioplegia Monitor also has a timer for use in tracking the time during and between doses of cardioplegia. With the new added features, it also displays the volume of cardioplegia delivered to the patient. The Sarns 8000 Cardioplegia Monitor communicates with the Sarns 8000 Roller Pump for obtaining volume information and to stop the roller pump (designated for cardioplegia delivery) during a pressure alarm condition if set by the operator to do so. The Sarns 8000 Cardioplegia Monitor also communicates with the Sarns 8000 Data Communications Module for purposes of data acquisition.

The provided text is a 510(k) summary for the Sarns 8000 Cardioplegia Monitor and does not describe acceptance criteria, performance data, or a study that typically involves AI or human interpretation elements. The device is a monitor for extracorporeal line pressure, temperature, time, and cardioplegia volume, and the document focuses on its substantial equivalence to predicate devices and software modifications.

Therefore, many of the requested items (e.g., sample size for test/training sets, experts for ground truth, adjudication methods, MRMC studies, standalone performance, type of ground truth for AI, etc.) are not applicable to the information provided in this regulatory submission.

However, based on the nonclinical performance section, we can infer some information that partially addresses your request, specifically point a.

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

Based on the "Nonclinical Performance" and "Conclusions from Nonclinical Tests" sections, the implied acceptance criterion is that the device functions as intended according to its performance specification.

The following points cannot be answered from the provided text as they relate to AI/machine learning studies or clinical trials, which were not part of this device's submission or testing.

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

• Not applicable; no "test set" in the context of AI/ML or clinical data is mentioned. The testing was nonclinical and focused on software validation.

c. 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; no expert-established ground truth is mentioned. The "ground truth" for this device would be its engineering specifications and correct functional operation.

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

• Not applicable; no adjudication method is mentioned as this was a nonclinical software validation.

e. 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; no MRMC study, human readers, or AI assistance is mentioned.

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

• Not applicable; no standalone algorithm performance in the context of AI is mentioned. The device itself is an "algorithm only" device in a sense, as it performs its functions without direct human intervention once parameters are set, but this is not typically what is meant by "standalone performance" in AI contexts.

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

• The "ground truth" for this device's performance would be engineering and software validation against pre-defined functional specifications.

h. The sample size for the training set

• Not applicable; no training set (in the context of AI/ML) is mentioned.

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

• Not applicable; no training set is mentioned.

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