To detect or image the distribution of radionuclides in the body or organ, using the following technique(s): C. Tomographic imaging (SPECT) for non Positron emitter

The Beacon system is a new option for non-uniform attenuation correction on the Axis or Irix gamma camera systems. This device is intended to be used for diagnostic imaging of organs and lesions. This device includes adding hardware and software to a gamma camera system.

This 510(k) premarket notification for the "Beacon S System" does not contain information typically found in acceptance criteria or a study proving device performance in the way modern AI/software as a medical device (SaMD) submissions do. This submission is for a hardware and software upgrade to a gamma camera system for non-uniform attenuation correction, not an AI diagnostic algorithm.

Therefore, many of the requested fields cannot be directly answered from the provided text.

Here's an analysis based on the information provided:

PICKER INTERNATIONAL 510(k) NOTICE - K983948 BEACON S SYSTEM

This submission does not detail acceptance criteria or a study in the context of an AI/SaMD diagnostic device with performance metrics like sensitivity, specificity, or AUC. Instead, it describes an upgrade (hardware and software) to existing gamma camera systems to improve non-uniform attenuation correction. The "study" here refers to the engineering development and compliance with quality systems, rather than a clinical performance study with statistical endpoints.

1. A table of acceptance criteria and the reported device performance
   • Acceptance Criteria: Not explicitly stated in terms of diagnostic performance metrics for a specific clinical task. The acceptance criteria appear to be related to functional specifications, electrical safety standards (IEC-601 or UL-544), and adherence to Picker's internal quality system (OI 2300) and ISO-9001.
   • Reported Device Performance: The document states, "The Beacon system is substantially equivalent to legally marketed devices." This is the core "performance" claim for a 510(k) – that its performance is comparable to a predicate device, rather than providing novel performance metrics. The stated purpose is for "non-uniform attenuation correction" to be used for "diagnostic imaging of organs and lesions," which implies improved image quality or diagnostic accuracy compared to uncorrected images, but no specific metrics are provided.

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

   • Not Applicable. This submission does not describe a clinical test set in the context of diagnostic performance evaluation for an AI/SaMD. The "study" mentioned pertains to internal quality system compliance and substantial equivalence claims, not a clinical trial.

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 ground truth establishment is described for a clinical test set.

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

   • Not Applicable. No test set or related adjudication is described.

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. This device is an attenuation correction system for gamma cameras, not an AI diagnostic algorithm for image interpretation that would typically involve human reader performance studies.

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

   • Not Applicable/No. This device is described as an "option for non-uniform attenuation correction" and "includes adding hardware and software to a gamma camera system." It's an integral component of an imaging system, not a standalone diagnostic algorithm. Its performance is intrinsically linked to the overall imaging process.

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

   • Not Applicable. No explicit ground truth is mentioned in the context of evaluating a diagnostic algorithm's accuracy. The "ground truth" for the device's function would be the physical reality of the radionuclide distribution, which the attenuation correction is trying to represent more accurately. However, this is not described in terms of a clinical validation study's ground truth for disease diagnosis.

8. The sample size for the training set

   • Not Applicable. This is not an AI/ML algorithm that requires a "training set" in the modern sense. It's a software/hardware component for image correction.

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

   • Not Applicable. As above, no training set for an AI/ML algorithm is used.

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