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The patient positioning system is a SCARA designed to position a patient for medical procedures prescribed by oncologists and others that require a high degree of accuracy and repeatability.

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This document is a 510(k) premarket notification decision letter from the FDA for a device called "Patient Positioning System" by Forte Automation Systems, Inc. It does not contain information about acceptance criteria, device performance, study details, or ground truth establishment.

Therefore, I cannot provide a response to your request, as the necessary information is not present in the provided text.

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The patient positioning system is a SCARA designed robotic arm designed to position a patient for medical procedures prescribed by oncologists and others that require a high degree of accuracy and repeatability.

The patient positioning system is a SCARA designed robotic arm allowing six degrees of freedom.

The provided 510(k) summary for the Forte Automation Systems Patient Positioning System (K122413) does not contain information about acceptance criteria and a study proving the device meets those criteria in the context of clinical performance or diagnostic accuracy.

This 510(k) pertains to a patient positioning system, which is a robotic arm for positioning patients during medical procedures. The submission focuses on non-clinical testing to demonstrate substantial equivalence to a predicate device. Clinical efficacy or diagnostic performance, which would typically involve acceptance criteria related to accuracy, sensitivity, specificity, and a clinical study to prove them, are not applicable to this type of device submission.

Here's a breakdown of the available information based on your requested points, highlighting why some are not present for this specific device:

1. Table of Acceptance Criteria and Reported Device Performance

It's crucial to understand that these are engineering/safety performance criteria, not clinical performance criteria in the context of diagnostic devices. The document states, "The submitted device does not impart energies into a patient. Therefore no clinical testing was needed." This confirms that the 510(k) submission relies on non-clinical engineering and safety performance to demonstrate substantial equivalence.

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. The tests conducted were non-clinical engineering tests (e.g., electromagnetic compatibility, vibration, speed, accuracy, collision detection) on the device itself, not on a patient population. There is no "test set" of patient 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)

Not applicable. Ground truth, in the context of clinical or diagnostic performance, is established by experts (e.g., radiologists, pathologists). Since no clinical testing was performed and no patient data was analyzed, this information is not relevant to this 510(k) submission.

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

Not applicable. Adjudication methods are used to resolve discrepancies in expert interpretations of clinical or diagnostic data. This was not part of the non-clinical testing performed.

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. MRMC studies are used to assess the clinical effectiveness of diagnostic devices and often involve human readers interpreting medical images. This device is a patient positioning system, not an imaging or diagnostic device with AI assistance.

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

Not applicable. Standalone performance refers to the diagnostic accuracy of an algorithm without human intervention. This is not a diagnostic algorithm. The "performance" discussed for this device relates to its mechanical and electrical functions.

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

For the non-clinical tests, the "ground truth" was established by engineering specifications, industry standards, and the physical properties of the device. For example, the accuracy tests would compare the device's measured positioning to its programmed target position, with "ground truth" being the theoretically correct position based on the design.

8. The sample size for the training set

Not applicable. This device is not an AI/ML algorithm that requires a "training set" for model development.

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

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

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