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TOPSLANE DMLC is a dynamic multileaf collimator designed to be mounted on the linear accelerator. It is intended to shape the specific fields, either in static or dynamic mode, to assist the radiation oncologist in the delivery of radiation to well-defined target volumes while sparing surrounding normal tissue and critical organs from excess radiation.

TOPSLANE DMLC is a dynamic multileaf collimator designed to be mounted on the various linear accelerator. It comprises multiple motorized tungsten leafs and By controlling each leaf to the designed position according to the software. treatment planning system (TPS), TOPSLANE DMLC is intended to shape the specific field, either in static or dynamic mode. TOPSLANE DMLC can perform different field shaping methods, such as Static Field Shaping, or Step and Shoot IMRT Field Shaping or Sliding Window IMRT Field Shaping.

This document is a 510(k) premarket notification for the TOPSLANE DMLC, a dynamic multileaf collimator. It is a regulatory submission to the FDA, and as such, it focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study report with specific acceptance criteria and performance metrics in the way a clinical trial might.

Therefore, many of the requested sections (e.g., sample size for test set, number of experts, adjudication method, MRMC study, standalone performance, training set details) are not applicable or not provided in this type of regulatory document.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The provided document does not explicitly state specific quantitative acceptance criteria or detailed performance metrics in a tabular format as one would find in a clinical study report. Instead, the submission focuses on demonstrating "substantial equivalence" to a predicate device (m3 micro-Multileaf Collimator, K004022, K020860).

The primary "acceptance criterion" for this type of submission is that the FDA determines the device is "substantially equivalent" to legally marketed predicate devices. This is based on comparisons of design, construction, intended use, and performance characteristics.

The document states:

• "This device is similar in design and construction, and has the same intended use and performance characteristics to the predicate device."
• "It utilizes materials that are already in use in other medical devices."
• "No new issues of safety or effectiveness are introduced by using this device."

Therefore, a table of acceptance criteria and reported performance cannot be created from the provided text. The document asserts that its performance characteristics are comparable to the predicate, implying it meets the performance standards already established for such devices.

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

Not applicable / Not provided.
This 510(k) submission does not describe a test set or associated data provenance from a new clinical study. Instead, it relies on the known performance and substantial equivalence to existing predicate devices.

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

Not applicable / Not provided.
No new test set requiring expert ground truth establishment is described in this submission.

4. Adjudication Method for the Test Set

Not applicable / Not provided.
No test set and adjudication are described.

5. If a Multi-reader Multi-case (MRMC) Comparative Effectiveness Study Was Done

No.
An MRMC comparative effectiveness study was not done, as this is a 510(k) submission seeking substantial equivalence for a physical medical device (a dynamic multileaf collimator), not an AI algorithm.

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

Not applicable.
This document is for a physical device (TOPSLANE DMLC), not an AI algorithm. Therefore, "standalone performance" in the context of an algorithm is not relevant. The device's function is to shape radiation fields, which integrates into the larger radiation therapy workflow managed by radiation oncologists.

7. The Type of Ground Truth Used

Not applicable / Not provided for new data.
The "ground truth" here is effectively the established performance and safety profile of the predicate devices. The submission asserts that the TOPSLANE DMLC operates similarly, thereby inheriting the "ground truth" of safe and effective operation within the scope of its intended design.

8. The Sample Size for the Training Set

Not applicable.
This document is for a physical device, not a machine learning algorithm, so there is no concept of a "training set" in the context of AI.

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

Not applicable.
As this is not an AI algorithm, the concept of a training set and its ground truth establishment is not relevant.

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The ARTP (Anti-Tumor Radiation Treatment Planning System) is a radiation dose planning and simulation of patients undergoing external beam treatment in the oncology clinic. ARTP is used to plan and simulate radiation treatments with linear accelerators and other similar teletherapy devices with x-ray energies from 1 to 25MV, as well as Cobalt-60, and electron energies from 1 to 25 MeV.

The TOPSLANE ATES is a Radiotherapy Treatment Planning System (RTPS) for radiation dose planning of patients undergoing external beam treatment in the oncology clinic. ATES is using modem photon beam convolution dose calculation algorithm and electron pencil beam dose calculation algorithm. The system software is designed to be convenient and low cost to the user.

The provided text describes a Radiation Treatment Planning System (RTPS) called TOPSLANE ATES, which is a medical device subject to FDA 510(k) review. The acceptance criteria and supporting studies for such devices typically focus on demonstrating substantial equivalence to a previously cleared predicate device, rather than explicit numerical performance targets like sensitivity or specificity.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance:

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

The document does not specify a separate "test set" in the context of clinical data or patient outcomes. The performance evaluation appears to be based on:

• Engineering/System Performance Tests: "Performance tests were conducted and the results indicated that the system consistently performed within the design parameters and equivalently to the predicate device."
• Comparison to Predicate: The core of a 510(k) is demonstrating equivalence. This often involves comparing technical specifications, functionalities, and calculation results of the new device against the predicate.

Therefore, there's no explicitly mentioned sample size for a test set comprising patient data, nor is there information on data provenance (e.g., country of origin, retrospective/prospective). The "test set" here refers to the device itself and its internal performance metrics.

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

Not applicable in this context. The "ground truth" for a RTPS in a 510(k) submission generally involves established physics principles, dose calculation algorithms, and comparisons to recognized "gold standard" methods or the predicate device's output, rather than expert consensus on patient images or diagnoses.

4. Adjudication Method for the Test Set:

Not applicable. There's no mention of an adjudication process for a test set in the clinical sense.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC study was not done. The document does not describe human readers or AI assistance in the context of improving diagnostic accuracy. This device is a treatment planning system, not an AI-assisted diagnostic tool.

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

The performance evaluation described seems to be a standalone assessment of the system's dose calculation and planning capabilities. The statement "Performance tests were conducted and the results indicated that the system consistently performed within the design parameters and equivalently to the predicate device" implies testing the algorithm and its output directly, without a human-in-the-loop component being the primary focus of the equivalence demonstration.

7. The Type of Ground Truth Used:

The "ground truth" for demonstrating equivalence would likely be:

• Predicated Device Performance/Output: The results obtained from the predicate device (Theraplan V05B) for specific treatment planning scenarios.
• Physics Principles/Established Models: The accuracy of the "modern photon beam convolution dose calculation algorithm and electron pencil beam dose calculation algorithm" would be evaluated against established physics models for radiation dose distribution.
• Engineering Specifications: The device's output meeting its own design parameters for dose calculation, planning geometry, etc.

8. The Sample Size for the Training Set:

Not applicable. This device is a rule-based/algorithmic system for radiation treatment planning, not a machine learning or AI system that requires a separate "training set" in the conventional sense. Its "training" is embodied in its programmed algorithms and adherence to established physics.

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

Not applicable, as there is no "training set" for this type of device mentioned in the document.

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