Search Results

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.

Ask a specific question about this device

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.

Ask a specific question about this device

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 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 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 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. This document focuses on demonstrating substantial equivalence to previously cleared predicate devices, rather than proving a device meets specific acceptance criteria through a clinical study or a study directly assessing performance against quantitative criteria (e.g., accuracy metrics for an AI algorithm).

The nature of this document (a 510(k) for a radiation therapy system) means it presents a technical and safety comparison to existing devices, not a study evaluating AI performance against predefined acceptance criteria for a diagnostic/AI-driven device. Therefore, many of the requested items (e.g., sample size for test set, number of experts for ground truth, MRMC study, standalone performance) are not applicable or measurable from this document.

Here's an analysis of what can be extracted and what cannot, based on the provided text:

Acceptance Criteria and Reported Device Performance

This document does not present explicit "acceptance criteria" in the format typically seen for AI/diagnostic devices (e.g., "sensitivity must be >X%, specificity >Y%"). Instead, it focuses on demonstrating that the Zap-X Radiosurgery System is as safe and effective as the predicate device(s) and performs as intended through non-clinical testing. The "performance" discussed relates to the engineering and functional performance of the radiotherapy system itself.

• Electrical safety and electromagnetic compatibility testing
• Software verification and validation testing (System and subsystem verification, system validation of commissioning, treatment planning, and treatment delivery)
• Usability testing
• Standards conformance testing (IEC 60601-1, IEC 60601-1-2, IEC 60601-2-1, IEC 60825-1, IEC 61217, IEC 62083) |
  | Radiation Leakage/Protection: The system must protect operators and the general public from radiation. | "The Zap-X System was demonstrated to meet the requirements for radiation leakage and provide protection from radiation to the operator and general public similar to that of CyberKnife within a radiation shielded vault." |
  | Technological Characteristics Comparability: Key technical features should be comparable to predicate/reference devices. | Detailed comparison table provided for: Regulation Number, Classification Product Code, Indications for Use, Accelerator (treatment beam), Dose rate, Depth of Dose Maximum, Treatment Beam, Moveable Treatment Beam, Patient Table/Couch, Shielding for ionizing radiation, Real-Time Dosimetry, Safety subsystem, System console/user interface software, Treatment target tracking software, Treatment planning software, Treatment delivery software. |

1. Sample sizes used for the test set and the data provenance:

• Sample Size: Not applicable. This document describes non-clinical bench testing, software V&V, and physical device performance verification, not an AI model trained or tested on a dataset of patient cases. There is no "test set" of patient data in the context of an AI algorithm described here.
• Data Provenance: Not applicable. The "data" comes from engineering tests, software logs, and measurements on the physical device, not patient data from specific countries or types of studies.

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

• Not Applicable: This process does not involve establishing ground truth from experts on a patient image or clinical dataset. The "ground truth" for this device relates to engineering specifications, safety standards, and functional performance, which are verified through standard testing procedures by qualified engineers and testers.

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

• Not Applicable: No expert adjudication of clinical data or images is involved as this is a physical radiation therapy system, not an AI diagnostic device.

4. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improved with AI vs without AI assistance:

• Not Applicable: This is not an AI-assisted diagnostic device; it is a physical radiosurgery system. Therefore, no MRMC study, human reader improvement, or AI assistance is relevant.

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

• Not Applicable: No AI algorithm in the diagnostic sense is described. The "performance" is of the complete physical system.

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

• Not Applicable in the traditional sense for AI/diagnostic devices: The "ground truth" here is based on engineering specifications, international and national standards (e.g., IEC standards), established physics principles of radiation therapy, and the performance characteristics of predicate devices. Verification ensures the device meets these pre-defined, measurable engineering and safety parameters.

7. The sample size for the training set:

• Not Applicable: This device is a physical system with integrated software for control and planning. It is not an AI system trained on a "training set" of patient data like a typical deep learning algorithm. Software verification and validation (V&V) is performed, which involves testing against requirements, but this is distinct from training an AI model.

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

• Not Applicable: As there's no "training set" of patient data in the AI sense, there's no ground truth establishment for it.

Summary of Document's Purpose:

This Section 510(k) submission for the Zap-X Radiosurgery System demonstrates substantial equivalence to existing predicate devices (CyberKnife M6 Systems and Leksell Gamma Knife Icon). The "study" proving its capabilities is primarily a comprehensive suite of nonclinical bench testing, software verification and validation, usability testing, and standards conformance testing. The acceptance criteria are implicitly that the device performs as intended, meets its design specifications, and is as safe and effective as the legally marketed predicate devices without raising new questions concerning safety or effectiveness. No clinical trials or AI performance evaluations with patient data are discussed or required for this type of submission for this device.

Ask a specific question about this device