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AccuLeaf is intended 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.

In this application AccuLeaf performs the same function as customized beam shaping blocks, and circular or cut blocks collimators, which have been used for many years.

AccuLeaf is a LINAC based Micro-Multi-Leaf-Collimator (MMLC), used in radiation treatment.

It enables shaping the Linac beam according to target geometrical and clinical requirements.

The device is composed of the MMLC module, the Linac interface module, the Workstation (with AccuLeaf Control Software), and the Distribution module.

The device operates in conjunction with a Linac, a treatment couch, and any additional equipment required in radiation treatment.

The MMLC apertures, (defined in treatment data file), are generated by positioning the motor-driven leaves. The motors, controlled by AccuLeaf, bring the leaves to specified positions. The AccuLeaf control operates as a sequential linear process, where the apertures are performed one by one.

To form a desired aperture, AccuLeaf calculates leaves motion from knowledge of their current positions (measured) and desired destination (delivered by treatment plan).

AccuLeaf displays an image reflecting the leaves current position. Numeric indication of each leaf position is available.

AccuLeaf two operation modes are Step-and-Shoot and Dynamic Arc:

• Step-and-Shoot: MMLC modifies the apertures prior to irradiation.

• Dynamic Arc: Irradiating Linac forms an arc while AccuLeaf forms apertures at a set of Gantry angles.

This document is a 510(k) summary for the AccuLeaf device, which is a Micro-Multi-Leaf-Collimator (MMLC) used in radiation treatment. The document focuses on establishing substantial equivalence to previously cleared devices and does not contain detailed information about acceptance criteria or specific studies to prove device performance in the way a clinical trial report would.

Therefore, many of the requested fields cannot be filled from the provided text.

Here's a breakdown of what can and cannot be extracted:

1. A table of acceptance criteria and the reported device performance

   • Not found. This document is a 510(k) summary, which focuses on regulatory clearance based on substantial equivalence. It does not detail specific engineering or clinical acceptance criteria for performance, nor does it provide a table of measured device performance against such criteria.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

   • Not found. The document does not describe any specific test set or clinical study involving patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g., radiologist with 10 years of experience)

   • Not found. No test set or ground truth establishment is described.

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

   • Not found. No test set or adjudication is 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

   • Not found. The AccuLeaf device is a hardware component (MMLC) for radiation therapy delivery, not an AI or diagnostic imaging device that would typically involve human readers or MRMC studies. It assists radiation oncologists in delivering radiation, performing functions akin to physical beam shaping blocks.

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

   • Not found. This is not applicable. The device is a physical system that shapes radiation beams under the control of software and in conjunction with a linear accelerator; it's not a standalone algorithm in the sense of AI.

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

   • Not found. No ground truth for performance evaluation is described. The "ground truth" for its function would be the intended radiation field shape as defined by the treatment plan.

8. The sample size for the training set

   • Not found. The document does not describe the development of a machine learning model that would require a training set.

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

   • Not found. Not applicable as no training set is described.

Summary of available information related to acceptance criteria and study:

The provided text is a 510(k) summary, which is a premarket notification to the FDA. It primarily focuses on demonstrating "substantial equivalence" to existing legally marketed devices, rather than detailed performance study results against specific criteria.

Key points from the document:

• Predicate Devices: The device establishes substantial equivalence to:
  1. AccuLeaf v1.03 (K021338)
  2. BrainLAB MMLC (K970586)
• Performance Standards: No specific performance standards have been established by Section 514 of the Federal Food, Drug, and Cosmetic Act for this type of device.
• Voluntary Standards: The AccuLeaf complies with the following voluntary standards, which serve as a form of "acceptance criteria" for safety and general performance characteristics:
  • IEC 60601-1 (1990) +A1 (1993) +A2 (1995)
  • IEC 60601-1-1 (2000)
  • IEC 60601-1-2 (1993)
  • IEC 60601-1-4, Ed.1.1 (2000)
• Device Function: The device "enables shaping the Linac beam according to target geometrical and clinical requirements." It "calculates leaves motion from knowledge of their current positions (measured) and desired destination (delivered by treatment plan)." The core performance is the accurate positioning of leaves to achieve the desired aperture.
• The document implies that the device "meets the acceptance criteria" by being substantially equivalent to its predicates and complying with recognized voluntary standards. The "study" proving this is the 510(k) submission itself, where the manufacturer provides documentation to the FDA to demonstrate equivalence, which typically involves comparing technical specifications, operating principles, and intended use with the predicate devices. It does not refer to a separate clinical or performance study with a test set of data points as would be done for a diagnostic AI device.

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The AccuLeaf is intended 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. The AccuLeaf enables irregular fields treatments to be performed with finely shaped patterns. In this application the AccuLeaf performs the same function as customized beam shaping blocks, and circular or cut blocks collimators, which have been used for many years

AccuLeaf is a LINAC based Micro-Multi-Leaf-Collimator (MMLC), used during both conformal stereotactic radiotherapy and conformal stereotactic radiosurgery. It enables shaping the LINAC beam according to tumor shape and clinical demands. The device is composed of the MMLC module, the LINAC interface module, the Workstation (with AccuLeaf-CS), and the Distribution module. The device is operated in conjunction with a LINAC, its treatment couch, a data file that contains the desired aperture parameters, and any additional equipment required during radiotherapy/ radiosurgery.

Here's an analysis of the provided 510(k) summary regarding the AccuLeaf device, focusing on the requested information:

Summary of Acceptance Criteria and Study Details for AccuLeaf

The provided 510(k) summary for the AccuLeaf device does not explicitly describe specific acceptance criteria in a quantitative or qualitative manner, nor does it detail a study performed to prove the device meets such criteria.

Instead, the submission primarily focuses on establishing substantial equivalence to a predicate device (BrainLAB Med. GmbH's Micro-Multi Leaf Collimator (K970586)). The core argument for safety and effectiveness is that AccuLeaf performs the same function as customized beam shaping blocks and other collimators that have been used for many years, and that validations and performance testing results support this equivalence without raising new safety and/or effectiveness issues.

Here's a breakdown of the requested information based on the provided text, with significant gaps noted:

1. Table of Acceptance Criteria and Reported Device Performance

   Acceptance Criteria	Reported Device Performance
   Not explicitly stated as quantitative acceptance criteria. The general intent is to perform the same function as existing beam shaping methods (customized blocks, circular/cut blocks).	The device's description and intended use state that it "enables irregular fields treatments to be performed with finely shaped patterns" and "performs the same function as customized beam shaping blocks, and circular or cut blocks collimators, which have been used for many years."
   Compliance with voluntary standards:	AccuLeaf complies with:
   * IEC 60601-1 (1990) +A1 (1993) +A2 (1995)	Yes
   * IEC 60601-1-1 (2000)	Yes
   * IEC 60601-1-2 (1993)	Yes
   * IEC 60601-1-4 (2000)	Yes
   No specific quantitative performance metrics (e.g., accuracy of leaf positioning, leakage, transmission, dose conformity limits) are provided as acceptance criteria.	The submission implies that "validations and performance testing results" demonstrated substantial equivalence, but the specifics of these results are not detailed.

   Note: 510(k) summaries often do not include the detailed performance data from bench testing or clinical studies. This document only provides a high-level overview. For the detailed acceptance criteria and performance data, one would typically need to review the full 510(k) submission, including the testing protocols and results submitted to the FDA.

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

   • Sample Size: Not specified. The document refers to "validations and performance testing results" but does not detail the nature or sample size of these tests. Given it's a hardware device (Multi-Leaf Collimator), the "test set" would likely refer to engineering and bench testing, not a clinical data set in the way an AI algorithm might use one.
   • Data Provenance: Not specified. Again, for a hardware device validation, this would refer more to the origin of the test materials, LINACs used, etc., which are not detailed here. It's likely these were internal company tests.

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

   • Not applicable/Not specified. The ground truth for a device like a multi-leaf collimator would be based on engineering specifications, physical measurements (e.g., leaf position accuracy, radiation field shaping), and established physics principles, rather than expert consensus on medical images or diagnoses.

4. Adjudication method for the test set

   • Not applicable/Not specified. Adjudication methods like 2+1 or 3+1 are typically used for clinical study endpoints, particularly when human readers are involved in interpreting data. This is not described for the AccuLeaf.

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 MRMC study is mentioned. This device is a mechanical component of a LINAC used in radiation therapy, not an AI-powered diagnostic or assistive tool for human readers. Therefore, the concept of "human readers improve with AI vs without AI assistance" does not apply to the AccuLeaf as described.

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

   • Not applicable. The AccuLeaf is a hardware device (MMLC) that operates in conjunction with a LINAC and its associated software (AccuLeaf-CS) to shape radiation beams. It is not an "algorithm only" device in the sense of a standalone AI solution. Its performance is inherent to its mechanical and control functionality. The document indicates it's "operated in conjunction with a LINAC, its treatment couch, a data file that contains the desired aperture parameters."

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

   • The ground truth for evaluating an MMLC would primarily be engineering specifications, physical dosimetry, and mechanical accuracy measurements. This would involve ensuring the leaves move to the commanded positions accurately, maintain their shape, minimize leakage between leaves, and effectively conform the radiation field to the intended shape. This is measured against design performance targets, not against clinical outcomes or pathology directly.

8. The sample size for the training set

   • Not applicable. This device is not an AI/machine learning product that requires a "training set" in the conventional sense. Its "training" would be its design, manufacturing, and calibration processes.

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

   • Not applicable, as there is no "training set" for an MMLC in the AI/ML context.

In conclusion, the 510(k) summary for AccuLeaf establishes substantial equivalence by demonstrating that the device performs the same function as legally marketed predicate devices and complies with relevant voluntary electrical safety and EMC standards. It does not provide detailed quantitative acceptance criteria or the specific results of performance testing, nor does it involve the types of studies typically conducted for AI-powered diagnostic or assistive devices. The "study" referenced implicitly is the "validations and performance testing" which supports the claim of substantial equivalence but is not detailed in this summary.

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