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The Varian Millennium 120 Multi-leaf Collimator (MLC) is provided to assist the radiation oncologist in the delivery of radiation to defined target volumes while sparing surrounding normal tissue and critical organs from excess radiation. In static mode, the MLC performs the same function as the customized shadow blocks. In a dynamic mode, a series of MLC leaf positions can be indexed to either Clinac dose fraction or gantry angle to create a changing beam shape while the radiation beam is on to create a three dimensional dose distribution.

Millennium Multi-Leaf Collimator (MLC) Release v7.0 is a modification of the present MLC device (K990085) to include the following:

• 1. Large Field IMRT treatments
• Smooth motion control 2.
• 3. Faster patient plan downloads via ethernet
     The Varian Millennium Multileaf Collimator (MLC) is an x-ray collimator designed to be mounted on a Varian Clinac® radiation therapy linear accelerator and is intended to shape the x-ray field perimeter. Field shape can either be static (fixed) or dynamic. Dynamic field shapes are controlled as a function of either Clinac® dose fraction or gantry angle.

The provided 510(k) summary for the Millennium Multi-leaf Collimator (K050442) outlines a modification to an existing device, primarily focusing on engineering enhancements rather than a new AI-powered diagnostic or predictive tool. Therefore, many standard acceptance criteria and study design elements typically associated with AI/ML medical devices are not explicitly detailed in this document.

Based on the information provided, here's a breakdown:

1. Table of Acceptance Criteria and Reported Device Performance:

The 510(k) summary does not contain a quantitative table of acceptance criteria and reported device performance in the way one would expect for a diagnostic AI device (e.g., sensitivity, specificity, AUC). Instead, the improvements are described qualitatively:

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

The document does not describe a "test set" in the context of data for an AI/ML algorithm. This 510(k) is for a physical device modification, not a software algorithm evaluated against a dataset of patient images or outcomes. Therefore, there is no mention of sample size for a test set or data provenance (country of origin, retrospective/prospective). The evaluations would likely involve engineering testing, performance verification, and possibly clinical use data from the previous version of the device, but these details are not provided in this summary.

3. Number of Experts Used to Establish Ground Truth and Qualifications:

Not applicable. This is not an AI/ML device requiring expert-established ground truth from medical images or patient data. The "ground truth" for this device would be its ability to perform its mechanical and electronic functions (e.g., accurately shaping radiation fields, smooth movement).

4. Adjudication Method:

Not applicable. There is no adjudication method described as this is not a study involving human-expert consensus on medical data.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

Not applicable. This device is a physical component of a radiation therapy system, not an AI software intended to assist human readers in interpretation. Therefore, an MRMC study is not relevant.

6. Standalone (Algorithm Only) Performance:

Not applicable. This is a physical device, not an algorithm. Its performance is intrinsically linked to its operation within the linear accelerator system.

7. Type of Ground Truth Used:

The "ground truth" for this medical device would be based on engineering specifications, physical measurements, and performance benchmarks for radiation field shaping, motion control, and data transfer speeds. It would also implicitly rely on the established safety and efficacy of the predicate device (K990085). The document does not specify the exact methods for defining or verifying this "ground truth."

8. Sample Size for the Training Set:

Not applicable. This device is a hardware modification, not an AI/ML algorithm that requires a "training set" of data.

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

Not applicable. As there is no AI/ML training set, there's no ground truth established for it.

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