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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 modified Zap-X Radiosurgery System ("Zap-X System") is a computer-controlled system for performing non-invasive stereotactic radiosurgery that is self-shielded for ionizing radiation. A gantry-mounted linear accelerator provides the modified Zap-X System with a source of therapeutic radiation and a kV imaging system is used to accurately locate the treatment target. At the start of treatment, X-ray images of 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 provided text is a 510(k) premarket notification for the Zap-X Radiosurgery System. The purpose of this document is to demonstrate "substantial equivalence" to a legally marketed predicate device, not necessarily to establish acceptance criteria for de novo performance or to report on a study directly proving such criteria.

Therefore, the information requested about acceptance criteria and a study proving the device meets them, particularly regarding AI performance metrics, is largely not present in this document. This document focuses on demonstrating that the modified Zap-X System is equivalent to an existing device and thus does not require new acceptance criteria to be established or proven in the same way an entirely novel device might.

However, I can extract information related to the device's performance characteristics, safety, and testing methods that would implicitly serve as "acceptance criteria" for a substantial equivalence determination.

Here's a breakdown of the available information based on your request:

1. A table of acceptance criteria and the reported device performance

Since this is a substantial equivalence submission, the "acceptance criteria" are implicitly met by demonstrating similarity to the predicate device and conforming to relevant standards. The performance data is primarily bench testing and compliance with recognized standards.

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 / Not Provided for clinical data. The document explicitly states: "No clinical testing was performed to support this premarket notification." The testing performed was "nonclinical, bench testing." Therefore, there is no clinical test set, sample size, or data provenance in the context of human patients.

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. Since no clinical testing was performed, there was no clinical ground truth established by experts for human patient data. The ground truth for bench testing would be based on engineering specifications and physical measurements.

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

• Not Applicable. No clinical test set or human expert review/adjudication was conducted as part of this submission.

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 is not an AI-assisted diagnostic or interpretation device that would involve human readers performing tasks. It is a radiosurgery system. The document does not mention any MRMC studies or AI assistance in the context of human interpretation improvement.

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

• This refers to the performance of the system itself, which is what the "nonclinical, bench testing" covers. The device, being a radiosurgery system, operates as a standalone system (with human planning and oversight, but the delivery itself is automated based on the plan). The performance data cited from bench testing (electrical safety, software V&V, system validation, standards conformance) demonstrates the standalone performance of the device's components and integrated system.

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

• For the nonclinical testing, the "ground truth" would be established through:
  • Engineering specifications and design requirements: To which the system and its components were verified and validated.
  • Physical measurements and dosimetry: For characteristics like dose rate, beam sizes, and depth at maximum dose.
  • Standard compliance: Verification against the requirements of international and national standards (e.g., IEC 60601 series).

8. The sample size for the training set

• Not provided/Not applicable in a clinical sense. This document pertains to a medical device for radiation therapy delivery, not an AI/ML algorithm that requires a "training set" of patient data in the typical sense. While the software within the system would have undergone development and testing (software V&V), the document does not specify a "training set" size for any internal algorithms.

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

• Not provided/Not applicable. As above, a "training set" in the context of AI/ML is not discussed. The "ground truth" for the device's operational parameters would be established by physics principles, engineering design, and quality control processes.

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