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This product is used for patients who require external beam stereotactic radiation therapy of the head and neck region or radiosurgery of the cranial region. The system provides cranial and head and neck fixation and stereotactic localization with automatic software fiducial localization. It is intended to be used during both Computed Tomography (acquisition of the imaging series used for the patient's treatment plan) and each of the patient radiation treatments.

The Aktina Pinpoint Head and Neck Stereotactic Localizer, Part Number 50-100, is used for the localization and fixation of patients undergoing stereotactic radiotherapy and radiosurgery of the cranial area, as well as radiotherapy of the head and neck area. Fixation is accomplished via two components: a customized Dental Tray that with the aid of slight vacuum suction fixes to the roof of the patient's mouth, and a customized head and neck support. Localization is accomplished via two components: a hardware component which comprises of a Stereotactic Fiducial Frame that is positioned over the patient's treatment area while being accurately interfaced to the Dental Tray, and a software component that reads the patient's Computed Tomography (CT) imaging series and determines the coordinate system of the patient within the fiducial frame. The Fiducial Frames are used during the patient's initial CT and then as a setup target box for each treatment thereafter.

The K103616 510(k) submission for the "Pinpoint Stereotactic Head and Neck Localizer" does not contain the detailed acceptance criteria or a study that proves the device meets specific performance criteria in the way typically expected for submissions involving AI/software with performance metrics.

This submission is for a physical medical device (a head and neck fixation system with stereotactic localization hardware and software). The emphasis is on proving substantial equivalence to a predicate device, as opposed to demonstrating specific diagnostic or predictive performance metrics.

Here's an analysis based on the provided text, highlighting what is present and what is absent:

1. Table of Acceptance Criteria and Reported Device Performance:

Reasoning for Absence: The submission focuses on proving substantial equivalence to a predicate device, which had previously demonstrated its safety and effectiveness. The modifications to the device (new mouthpiece design with external vacuum port) are described as not altering its fundamental form, fit, or function, thus not requiring new performance studies with specific acceptance criteria in the context of diagnostic accuracy.

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

• Sample Size: Not applicable/not provided. This is a hardware device with localization software, not a diagnostic AI system evaluated on a dataset of patient images.
• Data Provenance: Not applicable/not provided.

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

• Not applicable. Ground truth establishment by experts for a test set of data is not relevant for this type of device submission.

4. Adjudication Method for the Test Set:

• Not applicable.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and 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 system. The "software component that reads the patient's Computed Tomography (CT) imaging series and determines the coordinate system of the patient within the fiducial frame" is a localization algorithm, not a system for human reader interpretation improvement.

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

• The submission mentions a "software component that reads the patient's Computed Tomography (CT) imaging series and determines the coordinate system of the patient within the fiducial frame." While this is an algorithm, the 510(k) does not describe a standalone performance study with metrics like accuracy, sensitivity, or specificity for this software component. The focus is on the overall system's equivalence. Hardware specification testing is mentioned, which would include verification of the integrated system's function.

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

• Not applicable in the context of evaluating a diagnostic or predictive algorithm. For the localization software, the "ground truth" would likely involve precise physical measurements of the fiducial frame and geometric validation against CT scans, rather than clinical ground truth from patient data.

8. The sample size for the training set:

• Not applicable. There is no mention of a machine learning training set for an AI model in this submission. The software component described is likely based on deterministic algorithms for coordinate system determination, rather than a learned model.

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

• Not applicable.

Summary of the Study per the 510(k):

The "study" presented in this 510(k) is primarily a substantial equivalence comparison to a predicate device, rather than a detailed performance study with quantitative acceptance criteria for a new AI or diagnostic algorithm.

The core of the "study" is outlined in Section 7, "Performance Standards and Data," and Section 9, "Summary of Substantial Equivalence":

• Performance Data: "Hardware specification testing has been performed to show that the verification, validation and safety requirements have been met." This suggests internal testing against engineering specifications, but specific metrics are not disclosed in this summary.
• Biocompatibility: The new mouthpiece components were evaluated against ISO-10993-1, demonstrating biocompatibility for surface contact of less than 24 hours.

The FDA's decision to clear the device K103616 relies on its similarity in design and intended use to the predicate device (K081935), with the conclusion that "No new issues of safety or effectiveness are introduced by using this device." This implies that the predicate device's established safety and performance profile is leveraged for the current device due to their high degree of similarity and the minor nature of the changes.

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