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The SURGIMEDICS®/TMP® Vision Cardioplegia Delivery System is intended to mix, cool, warm, and deliver oxygenated blood cardioplegia solution during cardiopulmonary bypass.

The Vision Cardioplegia Delivery System is intended for mixing, cooling, warming, and delivery of oxygenated blood cardioplegia solution during cardiopulmonary bypass.

The K964571 submission for the SURGIMEDICS®/TMP® Vision Cardioplegia Delivery System does not contain the specific details required to fully address all parts of your request regarding acceptance criteria and study particulars. This document is a 510(k) summary, which typically focuses on demonstrating substantial equivalence to predicate devices rather than providing detailed clinical study results or acceptance criteria in the format you've requested for a device that might be AI-driven.

However, based on the provided text, I can extract and infer some information, particularly regarding the performance comparison to predicate devices, which serves as the basis for demonstrating substantial equivalence.

Here's an attempt to answer your questions based on the available information, with caveats for missing details:

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

The provided document does not explicitly state acceptance criteria in a quantitative or pass/fail format, nor does it present device performance in a structured table against such criteria. Instead, it states that the device "showed similar rates" to the predicate, implying that performance comparable to the predicate device was considered acceptable.

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

The document mentions "comparative bench testing" but does not specify the sample size (e.g., number of devices tested, number of test runs) or the provenance (country of origin, retrospective/prospective) of the data. It appears to be bench testing rather than human subject 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)

This is not applicable as the device is a medical device for cardioplegia delivery, not an AI-powered diagnostic or image analysis tool requiring expert ground truth for a test set in the context of typical AI/ML studies. The evaluation was based on physical performance metrics.

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

Not applicable, as this was not an expert-driven adjudication of medical cases, but rather bench testing of device performance.

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 device is not an AI-assisted diagnostic tool or an imaging device involving human readers.

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

This question is framed for AI algorithms. For this physical medical device, the "standalone" performance is the bench testing described, which evaluated the device's operational characteristics independently.

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

For this device, the "ground truth" for performance comparison was the established performance characteristics of the legally marketed predicate devices (Sorin Biomedical Buckberg BCD Vanguard, Sorin Biomedical BCD Advanced Blood Cardioplegia System, Avecor Cardiovascular MYOtherm Cardioplegia System). The performance metrics (hemolysis, heat exchange, etc.) themselves are quantifiable physical parameters, not subjective expert interpretations or pathological diagnoses.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set. The device was developed and then tested against established physical performance criteria.

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

Not applicable, as there is no training set for this type of device.

In summary, the provided document is a 510(k) summary demonstrating substantial equivalence for a physical medical device. The evaluation relies on comparative bench testing against predicate devices. The concepts of AI-specific acceptance criteria, test/training sets, expert ground truth, and MRMC studies are not relevant to this type of submission.

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