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The intended use of the Mobile Single Circular Camera (MSC) and the Mobile Digital Cardiac Camera (MDCC) is to detect the location and distribution of gamma ray emitting radionuclides in the body and store data for analysis. This device includes accessories such as signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts and accessories.

The Mobile Single Circular Camera (MSC) and the Mobile Digital Cardiac Camera (MDCC) manufactured by IS2 Medical Systems Inc., is substantially equivalent to the Mobile Single Rectangular Camera (MSR). The detector head is identical in hardware and software. The gantry of the Mobile Single Circular Camera (MSC) and the Mobile Digital Cardiac Camera (MDCC) are optimised for being attached to a mobile platform which is transported from location to location in a truck and have the same range of automatic clinical motions of Mobile Single Rectangular Cumera (MSR).

This 510(k) summary (K033956) is for the Mobile Single Circular Camera (MSC) and Mobile Digital Cardiac Camera (MDCC). This submission is a declaration of substantial equivalence to a predicate device, the Mobile Single Rectangular Camera (MSR) (K032779). As such, it does not involve new clinical studies with acceptance criteria and device performance evaluation in the typical sense of a novel device. Instead, the focus is on demonstrating that the new devices are essentially the same as the predicate in terms of safety and effectiveness, despite some mechanical differences.

Here's a breakdown of the requested information based on the provided text:

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

Since this is a substantial equivalence claim for a device with the same hardware and software as a predicate, there are no specific performance acceptance criteria or reported performance values from a new study presented in this 510(k) summary. The acceptance criteria are implicitly met by demonstrating substantial equivalence to the predicate device, which would have already met its own performance specifications.

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

No new test set or clinical data is mentioned or used for this 510(k) submission as it relies on substantial equivalence to a predicate device.

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)

Not applicable. No new test set requiring expert ground truth establishment was conducted for this substantial equivalence claim.

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

Not applicable. No new test set requiring adjudication was conducted for this substantial equivalence claim.

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 a gamma camera, not an AI-powered diagnostic tool, and the submission does not mention any MRMC studies or AI assistance.

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

Not applicable. This device is a gamma camera, not an algorithm, and the submission does not describe any standalone performance studies of an algorithm.

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

Not applicable. No new ground truth was established as the submission relies on the substantial equivalence to a predicate device. The predicate device's performance would have been established against appropriate ground truths for medical imaging devices (e.g., phantom studies, clinical correlation, etc. as per the standards applicable at the time of its clearance).

8. The sample size for the training set

Not applicable. The submission does not describe any machine learning or AI components requiring a training set. The device is a medical imaging hardware system with associated software for signal analysis and display.

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

Not applicable. No training set for machine learning was involved.

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