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The Circular Small Field Collimators, Elekta Integrated, are intended for use with the Elekta line of digital medical linear accelerators (LINAC) for the creation of small circular fields. These fields are used for patients who require external beam radiation therapy or stereotactic radiosurgery of the cranial or extra cranial regions for the treatment of tumors or lesions.

This is a Traditional 510(k) that describes the Circular Small Field Collimators, Elekta Integrated, 50-370, a tertiary collimation system used in conjunction with digital medical linear accelerators (LINAC) for external beam radiation therapy. It consists of collimator cone inserts and a cone insert mounting adapter. The collimator cone inserts create small circular fields of various sizes. Each cone insert is uniquely identified with the diameter in millimeters on the top and bottom covers. The cone mounting adapter is inserted directly into the beam block tray slot of the LINAC head without any need for modification to the LINAC. The collimator cones are easily inserted into and removed from the mounting adapter without removing the adapter from the LINAC. The collimator cone inserts are also uniquely identified electronically, and use the LINAC beam block tray electronic interlock to prevent irradiation, if the cone insert size does not conform to the treatment plan.

The provided document is a 510(k) premarket notification for a medical device called "Circular Small Field Collimators, Elekta Integrated." This document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a study with specific acceptance criteria and detailed performance of an AI/algorithm. Therefore, much of the requested information regarding AI study specifics cannot be extracted.

However, I can extract information related to the device's performance validation as described in this regulatory submission.

Summary of Device Performance and Validation:

The document describes the device as tertiary collimation system used with linear accelerators for creating small circular radiation fields for radiation therapy. The validation focused on demonstrating that the device meets functional, performance, safety, and labeling requirements, and performs comparably to or better than the predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a 510(k) for a medical hardware device (collimators) and not an AI/software algorithm, the "acceptance criteria" are not reported as statistical performance metrics (like sensitivity, specificity, or AUC) against a ground truth dataset in the way an AI study would. Instead, the acceptance criteria are met through compliance with established standards and successful functional and safety testing.

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

This information is not applicable as the document describes the validation of a hardware medical device (collimators) and not a data-driven AI/software device. The "test set" would refer to specific physical device units and their components being tested, rather than a dataset. The document does not specify the number of collimator units tested or the specific provenance of any data beyond compliance with standards and physical testing.

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

This information is not applicable. For this hardware device, "ground truth" is established by engineering specifications, physical measurements, and adherence to established medical device standards, rather than expert consensus on diagnostic interpretations. The document refers to "hardware specification testing" and assessment against ISO and IEC standards.

4. Adjudication Method for the Test Set

This information is not applicable. Adjudication methods like 2+1 or 3+1 are typically used for resolving discrepancies in expert interpretations during the establishment of ground truth for image-based diagnostic AI studies. The validation of this hardware device does not involve such a process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

This information is not applicable. An MRMC study is used to evaluate the impact of an AI system on human reader performance. This document concerns a hardware device, not an AI system.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Study Was Done

This information is not applicable. This device is a hardware component, not an algorithm. Therefore, a standalone algorithm performance study was not conducted.

7. The Type of Ground Truth Used

For this device, the "ground truth" is based on:

• Engineering Specifications: The design and manufacturing specifications for the collimators (e.g., precise diameter sizes, material composition).
• Physical Measurements: Verification of the actual performance (e.g., field size accuracy, beam attenuation) against these specifications.
• Compliance with Standards: Adherence to recognized national and international medical device safety and performance standards (e.g., ISO 14971, IEC 60601-1, IEC 60601-2-1, IEC 62366-1).
• Comparative Performance to Predicate: Demonstrating that the device performs "as well as or better than" the predicate device based on its design, intended use, and functional characteristics.

8. The Sample Size for the Training Set

This information is not applicable. This hardware device does not involve machine learning or AI, and therefore does not have a "training set."

9. How the Ground Truth for the Training Set Was Established

This information is not applicable as there is no training set for this hardware device.

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