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The VCAR option to the DST Gamma Camera and VISION Powerstation and the VCAR option to the DST-XL Gamma Camera and VISION Powerstation produce images which depict the anatomical distribution of radioisotopes within the human body, for interpretation by medical clinicians. The systems are intended for whole body nuclear imaging.

Indication for use of the VCR option to the DST and DST-XL cameras and Powerstation computer is an accessory upgrade intended to perform electronic collimation for coincidence imaging of positron-emitting radioisotopes in the human body.

The VCAR option to the DST Gamma Camera and VISION Powerstation (DST&PS/VCAR) and the VCAR option to the DST-XL Gamma Camera and VISION Powerstation are accessory upgrades to the previously cleared DST an DST-XL Gamma Cameras (K921008) and VISION Powerstation (K912753). This accessory enables the system to perform electronic collimation for positron emission tomography (PET) of radioisotopes within the human body. The DST&PS/VCAR and DST-XL&PS/VCAR utilize proprietary coincidence detection electronics and software to perform electronic collimation of gamma rays emitted from decay of radioisotopes in a patient. This process is called "Coincident Photon Emission Computed Tomography" (CPECT).

The DST&PS/VCAR and DST-XL&PS/VCAR allows the operator to acquire coincidence imaging data by controlling the detector electronics and processors in coincidence imaging mode. The DST&PS/VCAR and DST-XL&PS/VCAR also includes the control, processing, and display screens to perform reconstruction of 2-D and 3-D information from the acquired datasets.

Here's a breakdown of the acceptance criteria and study information for the SMV America DS-VCAR Option 510(k) (K972686), based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state specific pass/fail acceptance criteria values for performance metrics. Instead, it makes a general claim of meeting "system performance specifications" and performing "in a fashion similar to predicate devices."

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a sample size for a test set in terms of cases or patients. It mentions that "Major performance parameters have been measured using industry-standard test methods," implying laboratory or phantom-based testing rather than studies involving patient data for a test set. There is no information provided regarding data provenance (e.g., country of origin, retrospective or prospective) for any clinical or test data.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications

This information is not provided in the document. The testing described appears to be technical performance validation against specifications and predicate devices, not a clinical study requiring expert ground truth for interpretation.

4. Adjudication Method for the Test Set

This information is not applicable/not provided. The document describes technical performance testing rather than a study requiring adjudication of clinical interpretations.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The document does not describe an MRMC comparative effectiveness study. There is no mention of human readers, AI assistance, or an effect size for human improvement. The focus is on the device's technical performance and equivalence to predicate devices.

6. Standalone Performance Study (Algorithm Only Without Human-in-the-Loop)

The document describes standalone performance in terms of the device's technical capabilities. The "Summary of Testing" states that "Major performance parameters have been measured using industry-standard test methods to determine that the device meets its system performance specifications and performs in a fashion similar to predicate devices." This implies testing of the device's imaging and reconstruction capabilities in isolation.

7. Type of Ground Truth Used

The ground truth for the testing appears to be based on industry-standard test methods and published specifications for gamma cameras and PET systems, rather than clinical ground truth like pathology or outcomes data. The comparisons are made against established performance characteristics of predicate devices.

8. Sample Size for the Training Set

The document does not mention a training set sample size. This is expected as the device predates modern machine learning/AI paradigms that typically rely on large training datasets. The "VCAR option" appears to be an accessory upgrade that utilizes coincidence detection electronics and software for image processing and reconstruction, not a deep learning model.

9. How the Ground Truth for the Training Set Was Established

Since there is no mention of a training set in the context of machine learning, the question of how its ground truth was established is not applicable. The development and validation were "in accordance with the Company's product and software development procedures," implying traditional engineering and software verification methods.

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