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The Varian High Energy Linear Accelerator is indicated for stereotactic radiosurgery and precision radiotherapy for lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

The UNIQUE is indicated for stereotactic radiosurgery and precision radiotherapy for lesions, turnors and conditions anywhere in the body when radiation treatment is indicated.

The Varian Multileaf Collimator is intended to assist the clinician in the delivery of external beam radiation to defined target volumes during radiosurgery and radiotherapy while sparing surrounding normal tissue and critical organs from excess radiation.

The High Energy Linear Accelerator is a radiotherapy treatment unit. The equipment consists of a gantry, couch, stand and control console. The device is permanently installed. The radiotherapy treatment beam is generated by a linear accelerator assembly consisting of an electron gun, waveguide and collimator.

The UNIQUE is a radiotherapy treatment unit. The equipment consists of a gantry, couch, stand and control console. The device is permanently installed. The radiotherapy treatment beam is generated by a linear accelerator assembly consisting of an electron gun, waveguide and collimator.

The Varian Multileaf Collimator (MLC) is an accessory X-ray collimator designed to be mounted on a Varian radiotherapy linear accelerator. The MLC head is mounted on a Varian Linear Accelerator, where it shapes the radiation beam before it is delivered to the patient. The MLC head is permanently bolted onto the casting that holds the X-Jaws and Y-Jaws of the Varian Linear Accelerator gantry.

This document is a 510(k) premarket notification for a Varian Medical Systems linear accelerator and multileaf collimator. It explicitly states that no animal studies or clinical tests were included in this pre-market submission.

Therefore, based on the provided text, there is no information available to describe the acceptance criteria and the study that proves the device meets the acceptance criteria in the manner requested by the prompt. The submission focuses on demonstrating substantial equivalence to predicate devices through non-clinical tests, software verification and validation, and regression testing, rather than clinical performance data.

Specifically, the document states:

• "No animal studies or clinical tests have been included in this pre-market submission."
• "Verification testing was performed to demonstrate that the performance and functionality of the new C-Series operating system and MLC controller existing features met the design input requirements for CCMC."
• "Regression testing was performed to verify the integrity of any changes."
• "Validation testing was performed on production equivalent devices, under clinically representative conditions by qualified personnel."

As such, I cannot provide the requested information about:

1. A table of acceptance criteria and reported device performance (in a clinical context).
2. Sample size for test set and data provenance.
3. Number of experts, qualifications, and ground truth establishment for a test set.
4. Adjudication method.
5. MRMC comparative effectiveness study parameters or effect size.
6. Standalone algorithm performance.
7. Type of ground truth used (clinical ground truth, specifically).
8. Sample size for training set.
9. How ground truth for the training set was established.

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The Varian Multileaf Collimator is intended to assist the clinician in the delivery of external beam radiation to defined target volumes during radiosurgery and radiotherapy while sparing surrounding normal tissue and critical organs from excess radiation.

The Varian Multileaf Collimator (MLC) is an accessory X-ray collimator designed to be mounted on a Varian radiotherapy linear accelerator. The Multileaf Collimator (MLC) head is mounted on a Varian Linear Accelerator, where it shapes the radiation beam before it is delivered to the patient. The MLC head is permanently bolted onto the casting that holds the X-Jaws and Y-Jaws of the Varian Linear Accelerator gantry. The MLC head assembly contains two leaf banks, plus associated electromechanical components. The MLC leaves reside in two carriages. Each leaf is driven by an independent motor. When shaping the radiation field perimeter to conform to the tumor outline, adjacent MLC leaves from opposing banks can move past one another. This feature, known as leaf inter-digitation, allows for the creation of complex shapes. The two carriage drives are independently controlled. Smooth motion of the leaves and carriages is achieved using a trapezoidal velocity profile that ramps up speed of the leaves or carriages to a fixed speed, and then ramps speed down until the leaf (or carriage) stops precisely at the final position. The various MLC models provide varying numbers and widths of leaves. The leaf banks extend over different widths within the Multileaf Collimator, for the 52-leaf. 80-leaf, and the 120-leaf Millennium MLC, and the HD120 Ml.C models. The Varian Multileaf Collimator (MLC) head contains many computer-controlled mechanical "leaves" or "slats" that continually shape the treatment beam as the radiation is delivered from different angles around the patient. The changes to the Varian Multileaf Collimator provide updates for electronics and information technology hardware, improvements in usability and serviceability, and additional discrepancy fixes. The operation and intended use of the device remain unchanged. Overall safety of the device is unchanged.

The provided text describes a 510(k) premarket notification for a Varian Multileaf Collimator (MLC). This document is primarily focused on demonstrating substantial equivalence to a predicate device rather than presenting a clinical study with detailed acceptance criteria and performance metrics typically seen for novel AI algorithms.

Therefore, many of the requested points, especially those related to AI-specific study details (such as sample sizes for test/training sets, ground truth establishment methods with expert consensus/pathology, MRMC studies, or standalone algorithm performance), are not applicable or not provided in this type of regulatory submission for a hardware device with software updates.

Here's an analysis based on the information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The submission does not specify numerical acceptance criteria for a clinical study in terms of metrics like sensitivity, specificity, accuracy, or effect sizes for human reader improvement. Instead, the acceptance criteria are implicitly met by demonstrating that the device performs at least as well as the predicate device and met design input requirements.

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

• Sample Size: Not specified. The testing described is "Verification testing," "Regression testing," and "Validation testing" on a "production equivalent device." These typically involve engineering and functional tests rather than studies with "test sets" in the clinical AI sense.
• Data Provenance: Not applicable in the context of hardware/software functional testing. The testing would have been conducted internally by Varian Medical Systems.

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

• This concept is not directly applicable here. The "ground truth" for this type of device (a medical linear accelerator accessory) would be its physical and functional specifications meeting engineering standards and its performance aligning with its intended use (accurate beam shaping). "Qualified personnel" are mentioned for validation testing, implying engineers and possibly clinical physicists who understand the device's operation. No mention of radiologists or other clinical experts establishing "ground truth" in terms of diagnoses or outcomes is made.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not applicable. Adjudication methods are typically used in clinical studies comparing human interpretations or AI outputs against a consensus. For a hardware device, performance is evaluated against engineering specifications.

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. This is not an AI diagnostic or assistive device in the direct sense of providing interpretations to human readers. It is a radiation therapy accessory. Therefore, an MRMC study is not relevant here.

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

• Not applicable. The device is a physical accessory controlled by software, working in conjunction with a linear accelerator and under clinician control for treatment planning and delivery. It's not a standalone algorithm in the sense of a medical diagnostic AI.

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

• The "ground truth" for this device's performance would be the engineering specifications, functional requirements (e.g., leaf positioning accuracy, speed, inter-digitation capability), safety standards, and demonstrated equivalence to the predicate device's performance. It is based on engineering and functional test results demonstrating adherence to design inputs, rather than clinical ground truth like pathology or outcomes data.

8. The sample size for the training set

• Not applicable. This document describes a medical device accessory and its software updates, not an AI model that undergoes "training."

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

• Not applicable, as there is no mention of a "training set" for an AI model.

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The Varian MultiLeaf 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 this the MLC performs the same function as customized shadow blocks. Employing the MLC Arc Therapy feature enables movement of individual leaves of the MLC according to a pre-existing schedule while the host Clinac® linear accelerator is performing arc therapy. In this modality the beam shape created by the MLC can correspond to a beam's eye view of the treatment volume at all times while the gantry is rotating in arc therapy.

The Varian MultiLeaf Collimator (MLC) is an x-ray collimator designed to be mounted on a Varian Clinac® radiation therapy linear accelerator beneath the standard field defining collimator jaws to provide complex beam shaping supplementary to the Clinac's rectangular fields. It contains either 52 or 80 collimator leaves (26 or 40 opposed pairs of leaves), each of which can be positioned individually in order to provide an irregularly shaped treatment field that corresponds closely with the volume intended to be irradiated. With the dynamic arc therapy feature, the leaves may be continuously repositioned as a function of the Clinac gantry position during rotational irradiation (arc therapy) in order to provide dynamic conformal therapy.

This submission is a 510(k) premarket notification for a medical device submitted in 1997. At that time, the FDA's requirements for demonstrating safety and effectiveness relied heavily on substantial equivalence to predicate devices, rather than extensive clinical studies with detailed acceptance criteria and performance metrics as are common today for novel AI/ML devices. Therefore, much of the information typically requested for AI/ML device studies (e.g., sample sizes for training/test sets, expert qualifications, MRMC studies, specific performance metrics beyond functional equivalence) is not present in this document.

The document focuses on establishing substantial equivalence to existing devices and describing the device's intended use and technological considerations.

Here's an analysis based on the provided text, highlighting what can be extracted and what information is not available:

1. Table of Acceptance Criteria and Reported Device Performance

Explanation of "Acceptance Criteria" for this 1997 510(k): For a 510(k) premarket notification, the primary "acceptance criterion" is demonstrating substantial equivalence to a predicate device already legally marketed in interstate commerce. This means the new device is as safe and effective as the predicate. The "performance" reported is primarily a description of the device's function and how it aligns with the predicate, rather than quantitative performance metrics from a formal study.

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

• Not Provided. This document does not describe a "test set" in the modern sense of a dataset used to evaluate an AI/ML algorithm. The submission is for a hardware/software device primarily relying on functional equivalence.

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

• Not Applicable/Not Provided. Ground truth establishment for a test set is not part of this type of 510(k) submission for a hardware/software device like an MLC. The "ground truth" for the device's function would be its ability to physically shape the radiation beam as intended, which is typically verified through engineering tests and quality control, not clinical expert consensus on a dataset.

4. Adjudication method for the test set

• Not Applicable/Not Provided. Adjudication methods are relevant for studies involving human interpretation or uncertain diagnoses, which is not the nature of this device's submission.

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

• Not Applicable/Not Provided. This device is an MLC, a physical component of a radiation therapy system, not an AI/ML diagnostic or assistive tool for human interpretation. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this submission.

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

• Not Applicable/Not Provided. The device itself is the "standalone" component of the radiation delivery system. The "algorithm" here refers to the software controlling the MLC. Its performance is inherent in the device's function to shape the beam according to a pre-existing schedule. No separate "standalone performance" study of an algorithm only in the context of an AI/ML device is described or implied.

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

• Not Applicable/Not Provided as a formal ground truth for a clinical study. For this device, the "ground truth" concerning its function relates to engineering specifications and physical accuracy of beam shaping. For instance, whether the leaves move to the commanded positions, and whether the resulting field shape matches the planned shape. This is typically verified through phantom measurements and quality assurance procedures rather than clinical ground truth types like pathology or outcomes data for diagnostic algorithms.

8. The sample size for the training set

• Not Provided. This document does not describe an AI/ML training set. The software for the dynamic arc therapy feature would have been developed through conventional software engineering practices, not machine learning model training.

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

• Not Applicable. Since there's no mention of an AI/ML training set, the concept of establishing ground truth for it does not apply to this submission.

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