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The Agility multileaf collimator is indicated for use when additional flexibility is required in conforming the radiation beam to the anatomy to be exposed.

The associated Integrity R3.1 software is the interface and control software for the Elekta medical digital linear accelerator and is intended to assist a licensed practitioner in the delivery of radiation to defined target volumes (e.g. lesions, arterio-venous malformations, malignant and benign tumors), whilst sparing surrounding normal tissue and critical organs from excess radiation.

Both High Dose Rate mode and flattened beams are intended to be used for single or multiple fractions, delivered as static and/or dynamic, in gated or un-gated deliveries, in all areas of the body where such treatment is indicated.

The use of the Agility multileaf collimator in conjunction with an Elekta digital linear accelerator may be helpful in the delivery of radiation for treatment that includes but is not limited to malignant and benign brain tumors, brain metastases, spine lesions treated using SRS, squamous cell carcinoma of the head and neck, lung, breast, pancreatic, hepatic malignancies treated using SBRT, prostate, and bone metastases.

This Traditional 510(k) describes changes to the Elekta range of medical linear accelerators when fitted with the Agility multileaf collimator and associated Integrity linac control system. Items added are; High Dose Rate mode x-rays, specific clinical indications for use, and the Response™ gating interface that enables the linac treatment beam to be automatically turned on and off by signals from an external gating device.

High Dose Rate mode x-rays are provided by changes to the filtering arrangement to reduce wasteful attenuation of the beam.

The Elekta Agility Multileaf Collimator system, including the Agility MLC, Integrity R3.1 software, High Dose Rate mode, and Response™ gating interface, underwent non-clinical performance testing to demonstrate substantial equivalence to predicate devices and conformance to applicable technical design specifications, assuring safety and effectiveness.

Here's a breakdown of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance:

*with validated external gating device which has 510(k) clearance
**other methods are not supported with the RPM interface

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

The document does not explicitly state a specific numerical sample size for a "test set" in the context of patient data or clinical images. The testing described is "non-clinical performance testing" which involved:

• "module, integration and system level verification"
• "regression testing"
• "Validation of the system under clinically representative conditions"
• "Testing has been undertaken on production equivalent systems both at Elekta and at hospital sites."

This suggests testing was performed on a sufficient number of hardware and software configurations to ensure functionality and safety, but not on a specific number of patient cases or images for diagnostic performance.

The data provenance is from non-clinical performance testing performed at Elekta and hospital sites. It is not based on retrospective or prospective patient data for an AI-specific algorithm performance evaluation. It's focused on the physical and software performance of the medical device components.

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

This information is not provided in the document. Given that the testing is non-clinical performance testing of a medical linear accelerator accessory, the concept of "ground truth" as established by medical experts (e.g., radiologists) for image interpretation or diagnosis would not typically apply. Instead, the acceptance criteria relate to physics performance metrics and software functionality, which would be verified against engineering specifications by qualified engineers and physicists. The document mentions "competent and professionally qualified personnel" for validation, but does not specify their number or exact qualifications.

4. Adjudication method for the test set:

This information is not provided and is not applicable in the context of this type of non-clinical device performance testing. Adjudication methods are typically used when multiple experts are interpreting data to establish a consensus ground truth for classification or detection tasks, which is not the nature of the testing described.

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, a multi-reader, multi-case (MRMC) comparative effectiveness study was not done. This type of study is relevant for evaluating the impact of AI on human reader performance in diagnostic tasks (e.g., radiologists interpreting images with or without AI assistance). The device
(a multileaf collimator and control system for radiation therapy) is not an AI-powered diagnostic tool, but rather a treatment delivery system for which the performance is measured by physical and software parameters, not human reader interpretation.

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

While the device itself is an "algorithm" in the sense of software control, the term "standalone" in this context usually refers to an AI algorithm processing data (e.g., images) independently of human intervention for diagnostic or analytical purposes. This device is a component of a larger system (radiation therapy) where human practitioners make clinical decisions.

The document describes comprehensive testing of the device's components and integrated system to ensure they meet performance specifications. This is "standalone" in the sense that the device performs its functions as designed, but it's not an AI performing an independent diagnostic task. The validation focused on the technical performance of radiation delivery and control.

7. The type of ground truth used:

The "ground truth" for the non-clinical performance testing described would be the engineering design specifications and recognized international standards (e.g., IEC 60601-1, IEC 60601-2-1, IEC 62366, ISO 14971). The device's measured physics performance (e.g., transmission, leakage, dose rates) and software functionality were compared against these predetermined, quantitative criteria.

8. The sample size for the training set:

Not applicable. The device is not an AI algorithm that learns from a training set of data. It's a hardware and software system designed and programmed to perform specific functions.

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

Not applicable. As stated above, there is no "training set" in the context of AI model development for this traditional medical device submission. The device's functionality is based on engineering design and rigorous testing against established specifications and standards.

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