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The Zap-X Radiosurgery System is intended to provide treatment planning and image-guided stereotactic radiosurgery and precision radiotherapy for tumors, lesions and conditions in the brain, head and neck when radiation treatment is indicated.

The subject device, a modification to the previously cleared Zap-X Radiosurgery System, is a computer-controlled system for performing non-invasive stereotactic radiosurgery that is selfshielded for ionizing radiation. A gantry-mounted linear accelerator provides the modified Zap-X System with a therapeutic radiation source, and a kV imaging system is used to locate the treatment target accurately. At the start of treatment, X-ray images of the patient skeletal anatomy serve to align the treatment target with respect to the system isocenter. During radiosurgical treatment, the kV imaging system of the modified Zap-X System tracks patient movement and adjusts the table precisely to compensate for such movement.

The subject device for which this submission is being made improves the patient tracking/alignment throughout treatment. This is the only new feature which has been implemented on the subject device differentiating it from the predicate device. There is no change to the system specifications as a result of this improvement in patient alignment - the overall system patient positioning is still within 1 mm as with the predicate device.

The provided document is an FDA 510(k) clearance letter and summary for the Zap-X® Radiosurgery System. This type of submission focuses on demonstrating substantial equivalence to a legally marketed predicate device rather than proving clinical effectiveness through a standalone study with defined acceptance criteria and performance metrics.

Therefore, the document does not contain the following information typically found in a study designed to prove a device meets specific acceptance criteria:

• A table of acceptance criteria and reported device performance (in terms of clinical outcomes or diagnostic accuracy).
• Sample size used for a test set in a performance study, or data provenance for such a set.
• Number of experts used to establish ground truth for a test set, or their qualifications.
• Adjudication method for a test set.
• Multi-reader multi-case (MRMC) comparative effectiveness study, nor its effect size.
• Standalone (algorithm only without human-in-the-loop performance) study results.
• Type of ground truth used for a performance study (e.g., pathology, outcomes data).
• Sample size for a training set (as this is a medical device, not an AI/ML algorithm in the traditional sense, though it uses software).
• How ground truth for a training set was established.

What the document does describe is that the modified Zap-X Radiosurgery System was evaluated through non-clinical bench testing to demonstrate that it performed as intended and was substantially equivalent to its predicate device.

Here's an overview of the "acceptance criteria" and "study" as presented in this 510(k) submission:

1. Acceptance Criteria and Reported Device Performance:

The "acceptance criteria" in this context are primarily conformance to established design specifications and relevant industry standards, as well as demonstration of substantial equivalence to a predicate device. The "reported device performance" is that these specifications and standards were met.

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

• Sample Size: Not applicable in the context of clinical data for performance evaluation. The "tests" refer to engineering bench tests and validations of device components and software.
• Data Provenance: Not applicable for a typical "test set" as defined for clinical performance. The testing was internal ("Internal testing performed confirmed...") and focused on engineering validation.

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

• Not applicable. This submission doesn't describe a study involving expert-established ground truth for a clinical test set. The validation was against engineering specifications and industry standards.

4. Adjudication method for the test set:

• Not applicable, as there was no clinical test set requiring expert adjudication.

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, an MRMC comparative effectiveness study was not done. The device is a radiosurgery system for treatment delivery, not an AI-assisted diagnostic or interpretation tool for human readers.

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

• The device itself is a standalone system for radiosurgery. The "study" described is the non-clinical bench testing of this system. It is not an algorithm being assessed in isolation from a human-in-the-loop context. The enhancement is an "improvement in patient alignment during treatment" within the system itself.

7. The type of ground truth used:

• The "ground truth" for the non-clinical testing was defined by engineering design specifications, performance metrics derived from the predicate device, and the requirements of various industry standards (e.g., IEC 60601-1, IEC 62304).

8. The sample size for the training set:

• Not applicable. This is not a submission for an AI/Machine Learning algorithm that requires a "training set" in the common sense. The system is a medical device incorporating software and hardware.

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

• Not applicable, as there was no training set in the context of an AI/ML algorithm.

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