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This device is intended to be used for diagnostic imaging of organs and lesions. There is no change of intended use from that of the predicate device.

The STEP Option to the Prism Dual Head system is a modification of a gamma camera system. This device includes adding hardware and software to a gamma camera system.

This 510(k) notification describes a modification to an existing gamma camera system (STEP Option to the Prism Dual Head system), rather than a novel device requiring extensive performance studies as seen in AI/ML or new therapeutic devices. The primary claim is substantial equivalence to a predicate device. Therefore, many of the typical "acceptance criteria" and "study types" for assessing diagnostic performance are not explicitly detailed in the provided text in the way one might expect for a new diagnostic algorithm or complex software.

Based on the provided text, the focus is on functional equivalence and safety.

Here's an analysis of the provided information in the requested format, with caveats where the information is not present:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not explicitly stated. The text refers to "Laboratory tests" but does not quantify the number of tests, phantoms, or subjects used to demonstrate minimized attenuation effects.
• Data Provenance: Not explicitly stated. The "laboratory tests" context suggests internal validation, likely in a controlled environment using phantoms or simulated data, rather than human patient data, given the phrase "prior to use on human patients."

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

• Number of Experts: Not applicable/not stated. The validation appears to be based on physical measurements of attenuation effects in laboratory settings, not on expert interpretation of diagnostic images.
• Qualifications of Experts: Not applicable/not stated.

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

• Adjudication Method: Not applicable. The validation described is technical performance (attenuation correction, adherence to specifications) not diagnostic performance requiring expert adjudication of images.

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

• MRMC Study Done: No, an MRMC study was not described. This device is a hardware/software modification to an imaging system, not an AI/ML diagnostic aid intended to improve human reader performance. Its benefit is in image quality, which could indirectly aid interpretation, but this is not measured by an MRMC study in the provided text.
• Effect Size: Not applicable.

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

• Standalone Performance Study Done: Yes, in a limited sense. The "laboratory tests" demonstrating minimized attenuation effects appear to be a standalone assessment of the system's technical function (the "algorithm" here being the hardware/software combination for attenuation correction). This study evaluated the device's physical performance without human diagnostic interpretation as its primary outcome.

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

• Type of Ground Truth: For the attenuation correction claim, the ground truth would likely be physical measurements or established theoretical models of attenuation in controlled laboratory settings (e.g., using phantoms whose composition and density are precisely known). For functional specifications, the ground truth would be the engineering specifications themselves.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/not stated. This is a conventional gamma camera system modification, not an AI/ML algorithm that typically requires a large training set of labeled data in the way modern AI is developed. The "training" here would refer to engineering design and calibration based on known physics, not machine learning.

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

• How Ground Truth for Training Set Was Established: Not applicable in the context of AI/ML. For a conventional medical device, the "ground truth" for its development and calibration would be established through physics principles, engineering specifications, and established medical imaging standards, rather than labeled data sets for machine learning. This would involve designing the attenuation correction method based on known physical properties of radiation interaction with matter.

Summary of the Study Proving Acceptance Criteria:

The provided text indicates that the STEP option meets acceptance criteria primarily through laboratory tests and a comparison matrix to a predicate device.

• Attenuation Correction: "Laboratory tests have shown that the STEP option has minimized the degrading effects of body attenuation when compared to a standard gamma camera without STEP correction." This is the core performance study mentioned.
• Functional Specifications: The device "will perform in accordance with the development specifications." This implies internal verification and validation against those specifications, likely through engineering tests.
• Substantial Equivalence: A "matrix was enclosed comparing the STEP option to a predicate device." This comparative analysis, though not detailed, is foundational to the 510(k) claim of substantial equivalence.
• Safety: The device "will be certified to electrical safety standards (IEC-601or UL-544) by a third party organization." This is a regulatory compliance pathway.

The 510(k) process for this type of device modification relies heavily on demonstrating that the changes do not raise new questions of safety or effectiveness and that the modified device performs comparably to or better than a legally marketed predicate device on relevant technical performance parameters.

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