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The Gating Reflector Block is an accessory medical device indicated for adult and pediatric patients undergoing radiotherapy treatment simulation and/or delivery. GRB is a reusable device indicated for monitoring patient motion including respiratory patterns.

The Gating Reflector Block is a passive hardware device used for tracking the respiratory motion of the patient. It is intended to be used as an accessory to with Respiratory Gating System Scanners (K213927); TrueBeam STx™, Edge™, VitalBeam (K213977); and RPM Respiratory Gating System 1.7MR2 (K102024) to support motion management during imaging acquisition or radiation therapy treatment.

The Gating Reflector Block (Figure 01) has the following major components:

1. Body
2. Reflective Markers

The device has 4 mounted optical markers such that the reflected light can be processed by a camera to quantify the displacement of the markers, thus obtaining the patient's breathing curve.

The device consists of a plastic block and back plate and the optical markers. The optical lenses reflect the IR light wavelengths used by parent devices. Their arrangement ensures that the spatial displacement and the rotation of the block is detectable by optical monitoring systems. The alignment to the treatment room lasers is ensured by the printed alignment marks on the top and the sides of the block.

The major function of the device is to act as a surrogate device for tracking patient motion. Current parent devices track respiratory motion after placing the gating block on the chest of the patient. The IR reflectors are used by the parent devices to calculate the position and rotation of the block.

The provided text describes the regulatory filing for a medical device called the "Gating Reflector Block" (GRB). It is a passive hardware accessory used in radiotherapy to track patient motion, primarily respiratory patterns. The filing (K242874) seeks to register the GRB as an individual product, as it was previously cleared as an accessory to other Varian devices.

Based on the provided document, the device (Gating Reflector Block) is a passive hardware device and does not involve an AI algorithm, software, or human-in-the-loop performance studies as described in the prompt's requirements (e.g., MRMC studies, training/test sets, ground truth establishment for AI/software). Therefore, many of the requested details about acceptance criteria and studies (especially those related to AI/software performance) are not applicable to this specific device.

However, I can provide information based on the performance testing that was conducted for this hardware device, which focuses on safety and physical performance characteristics.

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

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

The document does not present explicit numerical "acceptance criteria" for performance metrics like sensitivity, specificity, or accuracy that would be typical for an AI/software device. Instead, the "acceptance criteria" for a passive hardware device like the GRB are met through demonstrated compliance with relevant standards and successful completion of specific non-clinical tests. The "performance" is reported as the successful demonstration of functionality and safety.

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

• Sample Size for Test Set: Not applicable in the context of an AI/software test set. The testing performed was validation and verification of the physical device and its reprocessing instructions. These tests are typically conducted on a representative sample of devices, but a "test set" in the sense of a dataset for algorithm evaluation is not relevant here.
• Data Provenance: Not applicable. This refers to physical device testing, not data collection for an algorithm.

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

• Not applicable. Ground truth establishment by experts is relevant for AI/software diagnosis/prognosis, not for a passive hardware device like the GRB. The "ground truth" for this device's performance relates to its physical properties, functionality, and safety as determined through engineering and biological testing.

4. Adjudication method for the test set

• Not applicable. Adjudication methods are used in studies involving human interpretation or AI output, not for the physical performance validation of a hardware device.

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. An MRMC study is relevant for AI-assisted diagnostic or clinical decision support software. The Gating Reflector Block is a passive hardware device for motion tracking, not an AI or software product.

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

• No. This device is a passive hardware component. It does not contain an algorithm or software. Its performance is intrinsically linked to its use within a larger system (like the Respiratory Gating System Scanners) rather than as a standalone algorithmic product.

7. The type of ground truth used

• The "ground truth" for this device's acceptance is based on:
  • Engineering specifications and measurements: For dimensions, material properties, and marker effectiveness.
  • Standardized test methods: For biocompatibility (e.g., ISO 10993 series) and cleaning validation (AAMI, ISO standards).
  • Regulatory compliance: Adherence to FDA Quality System Regulation (21 CFR §820), ISO 13485, and ISO 14971.
  • Demonstrated performance: The device "performs as intended" based on product verification and validation.

8. The sample size for the training set

• Not applicable. This device is a passive hardware component, not an AI system that requires a training set.

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

• Not applicable, as there is no training set for this hardware device.

In Summary:

The K242874 filing for the Gating Reflector Block pertains to a physical medical device accessory. The "acceptance criteria" and "study" described in the document are focused on demonstrating the device's physical performance, safety, and compatibility with existing Varian systems through non-clinical testing and adherence to established regulatory and industry standards, rather than evaluating an AI algorithm's diagnostic or predictive capabilities. The device has been on the market for 13 years with approximately 27,000 units in the field, further supporting its established performance and safety.

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PET Digital Gating generates a respiratory signal which can be used to automate motion correction of PET images with respiratory motion. Images that are motion-corrected using PET Digital Gating are intended to aid physicians in: detection; localization; evaluation; diagnosis; staging; monitoring; and/or follow up of disease, abnormality, and or function; therapy planning, monitoring, and guidance; radiotherapy treatment planning; and for Nuclear Medicine interventional procedures. PET Digital Gating maybe used with PET radiopharmaceuticals approved by the regulatory authority in the country of use, in patients of all ages, with a wide range of sizes, body habitus, and extent/type of disease.

Areas of the body most impacted by respiratory motion are the chest, abdomen, and pelvis. Disease types in which respiratory motion may have a significant impact, if uncorrected, include:

-Lung Cancer (e.g. Small Cell and Non-Small Cell);

-Liver Cancer;

• -Colorectal Cancer;
• -Lymphoma (e.g. Hodgkin's and Non-Hodgkin's);
• -Cancers that have metastasized to the liver;
• -Cancers in the Thorax; and
• -Heart Disease

The new PET Digital Gating option (aka Data Driven Gating or DDG) is a software-only device created within an update to the existing PET/CT acquisition and processing software. DDG provides the capability to derive a respiratory signal from the acquired PET data as an alternative to existing device-based respiratory gations that are available on the predicate device, GE's Discovery MI (K161574). The respiratory triggers generated by PET Digital Gating are used in a manner identical to those generated by device-based respiratory gating systems such as the reference device, Varian's "RPM Respiratory Gating System" (K102024).

PET Digital Gating provides the analogous respiratory triggers as the device-based systems, without the use of an external respiratory gating device and is an alternative to any existing external device. PET Digital Gating may be used both retrospectively on previously acquired exams, or prospectively where it operates during the acquisition. As is the case for existing device-based respiratory gating systems, PET Digital Gating's triggers may be used with GE's existing motion compensation techniques (gated, Q.Freeze, Q.Static).

Device-based systems rely on external body motion to determine the respiratory waveform. With PET Digital Gating the respiratory motion determined from internal patient anatomical movement.

PET Digital Gating uses an algorithm that incorporates a principal components analysis (PCA) to compute the spatial-temporal variation of PET list data. Principal components analysis is a data processing technique to find a mathematical basis for a dataset where the basis vectors are ordered to explain the maximum variation within the data. The PCA computes the basis vectors (eigenvectors) of the input data variation. The largest principal components are used along with the input data to generate 1-dimensional eigenvectors. These eigenvectors are subsequently used along with the list data to generate respiratory waveforms.

A fast Fourier transforms of the waveforms are used to determine an "R-value" that is used as the characterization metric for the respiratory motion identified in the dataset.

The R value threshold is user configurable. When DDG is used prospectively in conjunction with Q.Static motion correction, the R value threshold can be set for each bed position along with a "base" (no motion) acquisition time and a "Q.Static" (motion detected) acquisition time. Then, for each bed position, PET Digital Gating will evaluate the R value near the end of the base acquisition time, and if it is greater than the preset R value threshold, extend the acquisition time to the Q.Static acquisition time and generate the respiratory triggers to be used. If the evaluated R value is below the preset value, the acquisition completes and the next bed position is acquired.

The GE Medical Systems PET Digital Gating device aims to provide automated motion correction for PET images affected by respiratory motion. The primary study presented to demonstrate substantial equivalence and meet acceptance criteria involves engineering bench testing using a respiratory motion phantom and analysis of representative clinical examples.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria in a table format. Instead, it describes the testing performed and the conclusion drawn from it: that the device is "as safe and effective the legally marketed predicate device." The performance reported is that the device successfully generates respiratory triggers and allows for motion correction, analogous to device-based systems, but using internal patient anatomical movement.

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

• Test Set Sample Size: The document mentions "a representative sample of each of the current GE PET/CT scanner platforms" for the engineering bench testing. For the clinical examples, it states "representative clinical examples." Specific numbers for either are not provided.
• Data Provenance: The engineering bench testing used a "commercially available respiratory motion phantom." The "representative clinical examples" are not specified for their country of origin or whether they were retrospective or prospective, though it does state that "retrospective application of PET Digital Gating was used" for evaluation.

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

The document does not specify the number of experts or their qualifications used to establish ground truth for the test set.

4. Adjudication Method for the Test Set

The document does not describe an adjudication method for the test set. The validation relies on engineering bench testing with a phantom and comparison to clinical examples, rather than a multi-reader assessment.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not performed or reported in this document. The study described focuses on the technical capability of the device to generate motion correction signals, not on direct human reader performance with or without AI assistance.

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

Yes, a standalone performance assessment was conducted through the engineering bench testing. This testing evaluated the algorithm's ability to "determine the respiratory waveform" and "generate respiratory triggers" using a phantom, indicating an algorithm-only evaluation of its core functionality. The analysis of "representative clinical examples" also implies a standalone evaluation of the algorithm's output (motion-corrected images) using retrospective data.

7. The Type of Ground Truth Used

• For Engineering Bench Testing: The ground truth was established by the precisely controlled and known motion of the "commercially available respiratory motion phantom," which is designed to move inserts with varying speed and amplitude linked to "clinically determined, patient-specific respiratory motion waveforms." This is an engineered/simulated ground truth.
• For Clinical Examples: The type of ground truth used for "representative clinical examples" is not explicitly defined, but the quantitative analysis would likely compare the motion-corrected images generated by the device against a clinical expectation or standard for motion reduction, potentially based on qualitative assessment or SUV/volume measurements from previous methods, though this is not detailed.

8. The Sample Size for the Training Set

The document does not provide a sample size for the training set. It describes the algorithm (PCA) and its function, but not the data used to train it.

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

The document does not describe how the ground truth for the training set was established, as details about a training set are not provided. The algorithm (Principal Component Analysis) is described as a "data processing technique to find a mathematical basis for a dataset," which is typically unsupervised or uses specific characteristics of the input data rather than explicitly labeled ground truth in the traditional sense of classification.

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