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SunCHECK is a software platform intended to collect, detect, compare, calculate, analyze, display, and store radiotherapy quality assurance and dosimetry data.

SunCHECK is a server-based Web application which is accessible from any networked PC. It is intended to provide radiation therapy professionals with a platform that integrates patient QA, machine QA and data management workflows. This platform consists of a single GUI and database that is intended to provide a centralized view of a radiation therapy department's QA efforts.

The provided document is a 510(k) premarket notification for the SunCHECK medical device. It describes the device's intended use and compares its technological characteristics to predicate devices. However, the document does not contain specific information about acceptance criteria or a detailed study proving the device meets acceptance criteria.

The only statement related to performance data is:

"Model 1299028 SunCHECK has been tested using appropriate bench testing methods. Test results of the modified device have demonstrated that the device performs within its design specifications and equivalently to the predicate devices."

This is a very general statement and does not provide the specific details requested in your prompt.

Therefore, I cannot extract the following information from the provided text:

1. A table of acceptance criteria and the reported device performance
2. Sample sized used for the test set and the data provenance
3. Number of experts used to establish the ground truth for the test set and the qualifications
4. Adjudication method for the test set
5. If a multi reader multi case (MRMC) comparative effectiveness study was done, an effect size of how much human readers improve with AI vs without AI assistance
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
7. The type of ground truth used
8. The sample size for the training set
9. How the ground truth for the training set was established

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Sun Nuclear's DoseCHECK is a software product intended to independently calculate radiotherapy dose to provide a quality check of the planned dose.

DoseCHECK provides the clinician with the opportunity to compute dose using a different algorithm from that used by their treatment planning system (TPS). DoseCHECK utilizes the DICOM RT Plan, RT Structure Set, and CT Image Set from the TPS as inputs for the calculation, along with a linear accelerator characterization and CT-to-ED curve. The resulting dose distribution can be compared to the TPS dose distribution as a means of independent check. This comparison allows for detection of errors or inaccuracies that may occur within the TPS such as with beam modeling, calculation algorithm, and inhomogeneities.

The provided text describes the regulatory clearance for the device "Model 1217028 DoseCHECK" which is a software product intended to independently calculate radiotherapy dose to provide a quality check of the planned dose. However, the document provided does not contain detailed information about specific acceptance criteria, a comprehensive study proving the device meets those criteria, or the methodology (sample size, ground truth establishment, expert qualifications, adjudication, training set details) typically found in a clinical performance study.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Mobius3D) through performance data from "appropriate bench testing methods."

Based on the provided text, here's what can be extracted and what information is missing:

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

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

• Sample Size: Not specified.
• Data Provenance (e.g., country of origin, retrospective/prospective): Not specified.

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

• Number of experts: Not specified.
• Qualifications of experts: Not specified.

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

• Adjudication method: Not specified.

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

• MRMC study: No. This device is a software for independent dose calculation, not an AI-assisted diagnostic tool for human readers. Therefore, an MRMC study related to human reader improvement is not applicable in this context.

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

• Standalone Performance: Yes, the essence of the device's function is standalone dose calculation. The document states: "DoseCHECK provides the clinician with the opportunity to compute dose using a different algorithm from that used by their treatment planning system (TPS)." and "The resulting dose distribution can be compared to the TPS dose distribution as a means of independent check." This implies the algorithm performs its calculation independently. The "bench testing methods" would assess this standalone performance against typically accepted benchmarks or the predicate device.

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

• Type of Ground Truth: Not explicitly stated. For a dose calculation software, the "ground truth" would likely be a highly accurate or gold-standard dose calculation result, possibly from a very sophisticated and validated calculation engine, or physical dosimetry measurements in phantoms. The comparison is stated as being to the "TPS dose distribution," implying the TPS result is the primary reference for the check, but the device aims to provide an independent check, suggesting it performs its own calculation to a certain expected accuracy. The document mentions "beam modeling, calculation algorithm, and inhomogeneities" as areas where errors might occur in the TPS, which DoseCHECK is designed to detect, implying DoseCHECK has its own accurate model.

8. The sample size for the training set

• Sample Size: Not specified. The document does not mention "training" in the context of machine learning, as this is a physics-based calculation software. It uses "linear accelerator characterization and CT-to-ED curve" as inputs, which are configuration data rather than a "training set" in the AI/ML sense.

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

• Ground Truth Establishment for Training Set: Not applicable, as there's no mention of a training set in the machine learning sense. The device utilizes pre-characterized parameters (e.g., linear accelerator characterization, CT-to-ED curve) for its calculations.

Summary of Missing Information:

The provided document, being a 510(k) summary, focuses on substantial equivalence. It lacks the granular detail about specific performance metrics, the number of cases/patients in test sets, the methodologies for establishing ground truth (other than implicitly comparing to TPS or a "predicate device"), and expert involvement that would be needed to fully answer the detailed questions about acceptance criteria and study particulars. The "bench testing methods" are mentioned but not elaborated upon.

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Model 1220-MR ArcCHECK-MR is a three-dimensional (3D) ionizing radiation measurement device intended for radiotherapy quality assurance.

Model 1220-MR ArcCHECK-MR is a three dimensional diode sensor used for ionizing radiation measurement for radiotherapy quality assurance. The cylindrical diode array is embedded in a cylindrical plastic phantom that allows for dosimetry measurements to be made from all gantry angles as the therapy beam rotates about the diode array.

The provided GUI 'SNC Patient' software application installed on the user's computer and connected to the Model 1220-MR ArcCHECK-MR by an 8 pin DIN cable and is unchanged from the K142617 predicate device.

This submission introduces Model 1220-MR ArcCHECK-MR. This model is equivalent in form and function to the cleared Model 1220, but has been verified as an MR-conditional product.

The provided document is a 510(k) summary for the ArcCHECK-MR device, which is an ionizing radiation measurement device for radiotherapy quality assurance. It focuses on demonstrating substantial equivalence to a predicate device (ArcCHECK) rather than providing detailed acceptance criteria and a comprehensive study report for the device's performance.

Therefore, much of the requested information regarding detailed acceptance criteria, specific performance metrics, sample sizes for test and training sets, data provenance, ground truth establishment, expert qualifications, and MRMC studies is not available in the provided text.

However, I can extract the following based on the available information:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly list quantitative acceptance criteria in a table format. Instead, it states that "Test results of the modified device have demonstrated that the device performs within its design specifications and equivalently to the predicate K142617 Model 1220 ArcCHECK device."

The primary change to the device is its MR-conditional status, achieved through material changes (stainless steel hardware instead of carbon steel). The equivalence is based on the subject device performing "as well as" the predicate.

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

The document states that the device was "tested using appropriate bench testing methods." However, it does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective/prospective). This is typically detailed in a full test report, not a 510(k) summary.

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

The document does not provide information regarding experts used to establish ground truth or their qualifications. The testing appears to be primarily bench testing for functionality and MR compatibility, not a clinical study involving human interpretation.

4. Adjudication method for the test set

The document does not provide information regarding an adjudication method. This is not typically relevant for bench testing of a radiation measurement 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 MRMC study was performed or mentioned. The device is a "three-dimensional (3D) ionizing radiation measurement device" for quality assurance, not an AI-powered diagnostic tool for human readers.

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

The device itself is a measurement tool; its performance is inherently "standalone" in the sense that it collects radiation data. The "SNC Patient" software processes this data. The document states that "Test results of the modified device have demonstrated that the device performs within its design specifications and equivalently to the predicate..." indicating standalone performance testing was conducted, but details of this standalone testing (metrics, full results) are not provided.

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

For a radiation measurement device, the "ground truth" would likely be based on established physics principles and validated reference dosimetry measurements from calibrated instruments or computational models. The document does not explicitly state the type of ground truth but implies it through "bench testing methods" and comparison to a predicate device's established performance.

8. The sample size for the training set

The device is a hardware measurement device with associated software. It does not appear to be an AI/machine learning model that requires a "training set" in the conventional sense. The "SNC Patient" software is described as "unchanged from the K142617 predicate device," suggesting its algorithms are already developed and validated.

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

As there is no indication of an AI/machine learning training set, this information is not applicable/provided.

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ArcCHECK, Model 1220 is a three-dimensional (3D) ionizing radiation measurement device intended for radiotherapy quality assurance.

ArcCHECK, Model 1220 is a three dimensional diode sensor used for ionizing radiation measurement for radiotherapy quality assurance. The cylindrical diode array is embedded in a cylindrical plastic phantom that allows for dosimetry measurements to be made from all gantry angles as the therapy beam rotates about the diode array.

The provided GUI 'SNC Patient' software application installed on the user's computer and connected to the Model 1220 ArcCHECK by an 8 pin DIN cable, includes the following functions:

• . Array and dose calibration.
• Measurement and display of the spatial distribution of the dose resulting from delivery of a radiation treatment plan.
• . Save measurements.
• Import treatment plan dose map in the phantom and compares with the measurement dose points.
• . Compare the measured and planned dose distribution using the analysis methods of gamma or dose difference and distance to agreement (DTA) with user specified analysis criteria.
• Report of the analysis including a percent pass rate. ●
• Perform quality assurance (QA) on the planned versus delivered multi leaf ● collimator (MLC) pattern as a function of time. The MLC QA is capable of detecting an error of 5mm or greater in the planned position of an MLC leaf.

This submission introduces a 'primary' software modification to the cleared Model 1220 ArcCHECK device (K131466). Sun Nuclear intends to introduce a software feature which allows for the user to perform quality assurance (QA) on the planned versus delivered Multi Leaf Collimator (MLC) pattern. The MLC QA is capable of detecting an error of 5mm or greater in the planned position of an MLC leaf. This 'primary' modification which is believed to affect the indications for use, but not the intended use, is the subject of this premarket notification.

I am sorry, but the provided text is a 510(k) summary for a medical device (ArcCHECK Model 1220 from Sun Nuclear Corporation) and does not contain the specific information required to answer your request regarding acceptance criteria and a study that proves the device meets those criteria.

The document focuses on:

• Regulatory classification and predicate device information.
• Description and intended use of the device.
• Technological characteristics.
• A general statement about performance data and comparison with the predicate device.

It explicitly states: "Test results of the modified device have demonstrated that the device performs within its design specifications and equivalently to the predicate K131466 ArcCHECK device." However, it does not provide a table of acceptance criteria, reported performance, details of a specific study, sample sizes, data provenance, expert qualifications, adjudication methods, or specific ground truth information.

Therefore, I cannot extract the requested information from this document.

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The Sun Nuclear Dose Calculator is a software product intended to compute a radiotherapy dose volume.

Model 1218028 Dose Calculator computes a dose volume for a user-specified treatment delivery device based on user-provided three dimensional volumetric imaging information (e.g., computed tomography) and beam intensity values. Both the imaging data and beam intensity values are specified in DICOM-RT format. The beam model for the specified treatment delivery device is provided with the software. The output of the SDC is a DICOM RT dose volume.

The Dose Calculator is for use with external beam photon radiation therapy calculations. Charged particle radiotherapy calculations (including electron, proton, and heavy ion therapy) are not indicated for use with this product.

The Dose Calculator software application is considered to be a software module that may be used by several Sun Nuclear Corporation products and/or 300 party applications.

The provided document is a 510(k) premarket notification for a medical device called the "Model 1218028 Dose Calculator." It does not contain the detailed study information typically found in a clinical trial report or a comprehensive validation study. The document primarily focuses on establishing substantial equivalence to a predicate device.

Therefore, many of the requested categories cannot be fully answered with the information available.

Here's an attempt to extract what is present and indicate what is missing:

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

The document states: "Test results of the modified device have demonstrated that the device performs within its design specifications and equivalently to the predicate device." However, specific acceptance criteria or quantitative performance metrics are not provided in this summary.

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

The document mentions "appropriate bench testing methods" but does not specify the sample size of the test set, the provenance of the data, or whether it was retrospective or prospective.

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)

This information is not provided and is unlikely to be relevant for this type of device (a dose calculator) where "ground truth" would likely be established through physical measurements or established theoretical models, not expert consensus on images.

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

Adjudication methods are typically used for subjective assessments by experts. Since the device calculates dose volumes, and the "ground truth" establishment is not explicitly described, this information is not provided and likely not applicable in the traditional sense.

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

The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study. This device is a dose calculator, not an AI-assisted diagnostic tool that would typically involve human readers.

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

The document describes the device as "a software product intended to compute a radiotherapy dose volume." This inherently implies a standalone performance of the algorithm in its primary function, as it computes the dose volume based on inputs. The statement "Test results of the modified device have demonstrated that the device performs within its design specifications" suggests standalone testing, but explicit details of "standalone performance" metrics are not provided.

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

The document does not explicitly state the type of "ground truth" used. For a dose calculator, the ground truth would likely be derived from:

• Physical measurements: Using phantoms and dosimeters to measure actual dose distribution.
• Established reference calculations/models: Comparing the device's output to results from validated and widely accepted dose calculation algorithms or commercial treatment planning systems.
• Theoretical physics principles: Ensuring calculations adhere to fundamental physics.

Given the context of "bench testing," it's highly probable that physical measurements or comparisons to established reference systems were used, but this is not explicitly stated.

8. The sample size for the training set

The document does not mention a training set sample size. This type of device, which computes dose volumes based on physical models and inputs, is generally not "trained" on a dataset in the way a machine learning algorithm for image recognition would be. Its "knowledge" is embedded in its beam model and calculation algorithms.

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

As there is no mention of a traditional "training set" for machine learning, this information is not applicable/provided. The "ground truth" for the device's underlying models (like beam models) would be established through extensive calibration and measurement using dosimetric equipment and physical phantoms, but this process is not detailed here.

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Model 1216 Quality Reports is intended for quality assessment of radiotherapy treatment plans and the radiotherapy treatment planning process.

Model 1216 Quality Reports is a multi-functional information and data management software application intended to be used by trained clinicians who are familiar with radiation therapy, such as medical physicists, medical dosimetrists, and radiation oncologists.

Model 1216 Quality Reports receives DICOM data from Treatment Planning Systems (TPS) which are capable of providing such data. The data may include images, radiation therapy (RT) objects such as RT Plan, RT Structure Set and RT Dose. The user may also capture data through manual input.

The application allows the user to:

• Create reports and charts which may be sent to the institution's Electronic . Medical Record (EMR) system.
• Measure quality with standardized metrics that are based on the institution's ● chosen standards.
• Capture information from the clinical team during pre-treatment planning. ●
• Establish benchmark and progress data to demonstrate continuous improvement.
• Audit the Dose Volume Histogram (DVH) from the TPS.
• . Facilitate quantitative review of the estimated dose via a single DVH metric.
• Customize and save a library of non-dosimetric plan parameters and compare ● them against user defined constraints.
• . Track plan quality scores over time to assess performance and provide the basis for measuring continual improvement.

This document is a 510(k) premarket notification for the Model 1216 Quality Reports by Sun Nuclear Corporation. The information provided outlines the device's intended use and technical specifications, comparing it to predicate devices. However, it explicitly states that no clinical trials were performed for this device, and testing was limited to bench testing using simulated clinical workflows.

Therefore, it is not possible to provide acceptance criteria and a study proving the device meets those criteria based on the provided text, as such a study was not conducted. The document states:

"As with the predicate device, no clinical trials were performed for Model 1216 Quality Reports. Testing was limited to Bench testing using simulated clinical workflows." (Page 5)

Without clinical data or a formal study demonstrating performance against specific acceptance criteria, the detailed information requested below cannot be extracted from this document.

However, I can extract information related to the testing that was performed and the general claims made:

1. A table of acceptance criteria and the reported device performance
No specific acceptance criteria for clinical performance are provided in the document, nor is there reported clinical device performance, as no clinical trials were conducted. The document mentions:

• "Verification tests were written and executed to ensure that the system was working as designed."
• "Pass/fail criteria were used to verify requirements and to ensure that risk mitigations functioned as intended."
• "Regression tests were performed."

This indicates internal verification and validation testing, but not performance against clinical acceptance criteria.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
As no clinical trials were performed, there is no "test set" in the context of patient data. Testing was done with "simulated clinical workflows," meaning synthetic or representative data, but the sample size (number of cases or types of simulations) and data provenance (country, retrospective/prospective) are not specified.

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 applicable, as no clinical test set with externally established ground truth was used.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable, as no clinical test set was used that would require adjudication.

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 was performed. The device is a "multi-functional information and data management software application" for quality assessment, not an AI-assisted diagnostic tool for human readers in the traditional sense of improving interpretation.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device functions as a standalone software, but its performance was assessed through "bench testing using simulated clinical workflows" rather than a formal standalone clinical performance study using patient data and ground truth. The document states it is "intended to be used by trained clinicians who are familiar with radiation therapy, such as medical physicists, medical dosimetrists, and radiation oncologists," indicating human-in-the-loop operation for the overall process, even if the software itself runs independently.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
For the internal "verification tests" and "simulated clinical workflows," the ground truth would typically be defined by the expected behavior or output of the software according to its design specifications. It would not involve expert consensus, pathology, or outcomes data from real patients.

8. The sample size for the training set
Not applicable. The document does not describe an AI/machine learning model that requires a training set. This is a software application for data management and quality assessment, not a learning algorithm.

9. How the ground truth for the training set was established
Not applicable, as there is no training set mentioned or implied.

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The Model 1203 WaterProof PROFILER is intended for radiotherapy dosimetry measurements for commissioning a treatment planning system (TPS) computer. It is also intended for periodic beam quality assurance (QA) tests as defined by the medical physicist responsible for the QA program.

The WaterProof PROFILER, model 1203, is a linear array of radiation detectors that are housed in a water proof enclosure that mounts to the 3D SCANNER's movement mechanisms. The electronics in the array measures charge produced in the detectors due to ionizing radiation. The WaterProof PROFILER provides measurement updates to the 3D SCANNER. The 3D SCANNER correlates the dose information with motor position information and transfers the data to a computer running SNC Dosimetry. SNC Dosimetry then provides a graphical user interface for viewing the measured dose distributions and its parameter analysis; it also provides a tool for exporting the data to treatment planning systems.

The WaterProof PROFILER includes five components:

1. a linear array of radiation detectors;
2. a water proof enclosure;
3. a means for connecting to the 3D SCANNER's movement mechanisms;
4. control electronics and embedded code to manage and transmit the data recorded from the radiation detectors;
5. a cable that connects the water proof enclosure to the 3D SCANNER

This document is a 510(k) premarket notification for the Sun Nuclear Corporation Model 1203 - WaterProof Profiler. It primarily focuses on demonstrating substantial equivalence to previously cleared devices rather than a typical clinical study with acceptance criteria for a diagnostic algorithm. Therefore, many of the requested fields are not directly applicable.

However, I can extract the information provided about the device's technical specifications and the comparison made for demonstrating substantial equivalence.

1. Table of Acceptance Criteria and Reported Device Performance

This document describes technical characteristics and a comparison against predicate devices, but it does not specify explicit "acceptance criteria" in the sense of performance metrics with thresholds (e.g., sensitivity, specificity, accuracy for a diagnostic algorithm). Instead, the performance evaluation for this device, a radiotherapy dosimetry measurement tool, focuses on its ability to perform its intended measurements and its compliance with relevant standards.

The table below summarizes the key performance specifications of the Model 1203 WaterProof PROFILER compared to its predicate devices, as presented in the document. The "acceptance criteria" here are implied by the ability to function as intended and similarity to predicate devices.

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

The document does not describe a "test set" in the context of clinical data for an AI/diagnostic algorithm. The device is a physical measurement tool.

• Test Set Description: The "performance data" refers to bench testing of the physical device.
• Sample Size: Not applicable in the traditional sense of a patient cohort. The testing involves the device itself and its components.
• Data Provenance: The testing was conducted by Sun Nuclear Corporation as part of the device's design and verification process. Specific country of origin for the data is not mentioned beyond the manufacturing location. The testing is assumed to be prospective as it's part of the device's development and validation.

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

Not applicable. This is not a diagnostic device relying on expert interpretation for ground truth. Ground truth for dosimetry measurements would typically involve established physics principles, reference detectors, and phantoms.

4. Adjudication Method for the Test Set

Not applicable. There's no human interpretation or adjudication described for the device's performance testing.

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

No, an MRMC comparative effectiveness study was not done. This device is a physical dosimetry measurement tool, not an AI-powered diagnostic tool requiring human-in-the-loop performance evaluation.

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

The device itself is a "standalone" measurement tool. Its performance was evaluated through "bench testing" to ensure it met design specifications and correlated with predicate devices. This is analogous to a standalone performance evaluation for a physical device.

7. The Type of Ground Truth Used

The "ground truth" for evaluating this device would be established physical dosimetry standards, reference measurement devices, and known radiation fields. The document states: "Tests that compare known static and dynamic fields required during treatment planning system commissioning have been performed." This implies that the device's measurements were compared against these "known" or established values.

8. The Sample Size for the Training Set

Not applicable. This device is a hardware measurement tool, not a machine learning algorithm that requires a training set of data.

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

Not applicable. As explained above, there is no training set mentioned in the context of this device.

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PerFRACTION is intended to allow for the detection of errors that can occur in the delivery of a patient's radiation therapy treatment.

PerFRACTION allows for the comparison of the cumulative exit image(s) for one treatment fraction to the cumulative exit image(s) for another treatment fraction, thus providing a consistency check on the delivery of the treatment fraction.

PerFRACTION™ is a device that includes software installed on standard, modern computing hardware (provided with the software) that allows clinicians to perform quality assurance for each fraction of a radiotherapy treatment plan. PerFRACTION compares the beam-exit measurement data from a treatment fraction to data from a prior baseline fraction. This comparison allows for the detection of errors that may occur with the delivery system such as the multi-leaf collimator, accelerator, and collimating jaws.

The FDA 510(k) summary for the PerFRACTION (Model 1215) device provides limited information regarding specific acceptance criteria and detailed study designs. However, based on the provided text, here's what can be extracted and inferred:

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

The document does not explicitly state a table of acceptance criteria with numerical targets. Instead, it broadly states: "PerFRACTION™ has been tested in non-clinical and clinical settings, and it has been shown that this device performs within its design specifications."

It further elaborates on the types of errors the device was tested to detect:

• Errors in the collimation jaws
• Errors in multileaf collimator leaves
• Errors in accelerator output
• Errors in gantry rotation of the treatment delivery device

The performance claim is: "Based on the results of this performance testing when evaluated against published data for the predicate, Model 1215 PerFRACTION is as safe, as effective, and performs as well or better than the predicate device."

Inferred Acceptance Criteria (based on the description):
The device is expected to reliably detect errors in the delivery of radiation therapy treatment, specifically those related to collimation jaws, MLCs, accelerator output, and gantry rotation, to a degree comparable to or better than the predicate device.

Reported Device Performance:
The device "performs within its design specifications" and is "as safe, as effective, and performs as well or better than the predicate device" in detecting the specified errors.

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

The document does not specify the sample size for the test set or the data provenance (e.g., country of origin, retrospective or prospective). It simply states that the device was "tested in non-clinical and clinical settings."

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

This information is not provided in the document.

4. Adjudication method for the test set

This information is not provided in the document.

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

The document does not mention an MRMC study or any assessment of human reader improvement with or without AI assistance. The device is described as software that performs a consistency check, implying a standalone analysis rather than an assistive tool for human interpretation.

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

Yes, based on the description, a standalone performance assessment was done. The device "compares the beam-exit measurement data from a treatment fraction to data from a prior baseline fraction," and this comparison "allows for the detection of errors." This implies the algorithm (PerFRACTION) performs the detection independently.

7. The type of ground truth used

The document does not explicitly state the type of ground truth used. However, given that the device detects "errors that can occur in the delivery of a patient's radiation therapy treatment" and was tested when "exposed to errors," it strongly suggests that the ground truth was based on known, induced errors or simulated errors in a controlled environment, likely confirmed by physical measurements or pre-defined error scenarios. It could also potentially involve comparison against highly precise reference measurements or other established QA methods.

8. The sample size for the training set

The document does not mention a "training set" or its sample size. This suggests that the device might not be based on a machine learning model that requires explicit training data in the typical sense. It seems to be a rule-based or algorithmic comparison system.

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

As no training set is explicitly mentioned, this information is not provided.

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Model 1212 3DVH is a radiotherapy dose delivery quality assurance (QA) software application intended to estimate the dosimetric impacts of the deviations and imperfections of a treatment delivery device. and its accessories, on the 3D patient dose volume as defined by a treatment planning system (TPS). These dosimetric impacts are based upon QA measurement of the radiation dose distributions that are delivered to a phantom.

The Sun Nuclear 3DVH product, model 1212, is a software application that creates an estimated patient dose distribution using data measured during delivery of the treatment plan to certain 2D or 3D detector arrays and the planning patient dose volume computed by the treatment planning system (TPS) as inputs. The patented 3DVH dose algorithm (US patent #7,945,022) uses the measured data to make perturbations to the TPS patient dose volume to produce the estimated patient dose volume. From a comparison of the 3DVH result to the TPS planned dose, a qualified clinician makes the decision whether the TDD along with its accessories (including the treatment planning system, or TPS) is capable of delivering the treatment as prescribed.

The provided text describes a 510(k) summary for the Sun Nuclear Model 1212 3DVH device, a radiotherapy dose delivery quality assurance (QA) software application. However, the document does not explicitly state specific acceptance criteria or provide a detailed study proving the device meets acceptance criteria. It contains general statements about performance testing but lacks the quantitative details required to answer your request fully.

Based on the information available:

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

The document does not explicitly list quantitative acceptance criteria (e.g., specific thresholds for accuracy, precision, or agreement metrics). It only states: "3DVH has been tested in non-clinical and clinical settings, and it has been shown that this device performs within its design specifications and industry-specific guidelines. Performance testing included extensive benchmarking of the dose calculation algorithm (its individual components and as an integrated whole). Performance testing also included confirmation of the computed dose volume histogram data and comparison tools such as dose different, distance-to-agreement, and gamma index analysis. Based on the results of this performance testing, Model 1212 3DVH is as safe, as effective, and performs as well or better than the predicate device."

Without specific numerical criteria or results, a table cannot be constructed.

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

The document states: "3DVH has been tested in non-clinical and clinical settings". However, it does not provide any details about the sample size used for the test set or the data provenance (e.g., country of origin, retrospective or prospective nature).

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

The document does not mention the use of experts or the establishment of ground truth for a test set in the context of device performance testing. The intended use specifies that a "qualified clinician makes the decision whether the TDD along with its accessories... is capable of delivering the treatment as prescribed," but this refers to the ultimate clinical decision-making, not the evaluation of the device's algorithmic performance against a ground truth during testing.

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

Since there is no mention of experts establishing a ground truth for a test set, there is no information about an adjudication method.

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:

The document describes the 3DVH as a "software application that creates an estimated patient dose distribution" and "intended to estimate the dosimetric impacts." It does not describe an MRMC comparative effectiveness study involving human readers or any assessment of human improvement with or without AI assistance. The device's function is to provide an estimated dose volume, which a clinician then uses for decision-making, but its performance is evaluated on its accuracy in generating that estimate, not on how it changes human interpretation in a comparative effectiveness study.

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

The performance testing described primarily evaluates the algorithm itself. The text states: "Performance testing included extensive benchmarking of the dose calculation algorithm (its individual components and as an integrated whole)." This strongly implies that standalone (algorithm only) performance was assessed.

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

The document implies that the ground truth for evaluating the dose calculation algorithm would be physical dose measurements or highly accurate simulated dose distributions that the algorithm aims to match. It mentions the "use of DICOM input data" and that the algorithm uses "measured data to make perturbations to the TPS patient dose volume." The goal is to produce an "estimated patient dose volume." Therefore, the ground truth would likely be external reference dose datasets or actual physical measurements.

8. The sample size for the training set:

The document does not provide any information regarding a training set size.

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

Since there is no mention of a training set, there is no information on how its ground truth was established.

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Model 1177 MapCHECK 2 is a two-dimensional (2D) radiotherapy beam dosimetry QA system intended for the measurement of radiation dose distributions for the purpose of comparison with a simulated dose distribution in the same phantom geometry as calculated by the treatment planning system (TPS).

ArcCHECK, Model 1220 is a three-dimensional (3D) radiotherapy beam dosimetry QA system intended for the measurement of radiation dose distributions for the purpose of comparison with a simulated dose distribution in the same phantom geometry as calculated by the treatment planning system (TPS).

The Model 1177 MapCHECK 2 and Model 1220 ArcCHECK devices are diode detector arrays that allow the user to perform radiation therapy delivery quality assurance (QA) and dosimetry.

The MapCHECK 2 is a two-dimensional detector array intended to measure radiation dose distribution. The 1527 diode detectors are embedded in polymethyl methacrylate (PMMA) phantom in an array size of 32 cm, with a detector spacing of 7.07 mm and a weight of 7.1 kg. With the provided software installed on the user's computer and connected to the MapCHECK 2 with an 8 pin DIN power/data conduit cable, the software provides the ability for the user to perform QA analysis of a patient's radiation therapy plan prior to treatment.

The ArcCHECK is a three-dimensional cylindrical detector array designed for coherent measurement geometry during rotational treatment delivery. The 1386 diode detectors are embedded in a PMMA phantom on a cylindrical geometric surface with an array size of 21 cm diameter x 21 cm length, with a detector spacing of 10 mm and a weight of 16 kg. This cylindrical array allows for dosimetry measurements to be made from all gantry angles as the therapy beam rotates about the diode array. With the provided software installed on the user's computer and connected to the ArcCHECK with an 8 pin DIN power/data conduit cable, the software provides the ability for the user to perform QA analysis of a patient's radiation therapy plan prior to treatment.

Both MapCHECK 2 and ArcCHECK use the same software application that includes functions for array and dose calibration; measurement and display of the spatial distribution of the dose resulting from delivery of a radiation treatment plan; saving the measurement; importing the treatment planning system (TPS) calculated dose distribution; comparing the measured and planned dose distributions using the analysis methods of gamma or dose difference and distance to agreement (DTA) with user specified analysis criteria; and a report of this analysis that includes percent pass rates.

The provided document describes the Sun Nuclear MapCHECK 2 and ArcCHECK devices, which are radiation therapy dosimetry quality assurance (QA) systems. However, it does not explicitly state quantitative acceptance criteria or a detailed study proving the device meets those criteria.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device (RIT113 Film Analysis System, K935928) based on intended use, performance testing, safety, and effectiveness.

Here's a breakdown of the information that can be extracted or inferred:

1. Table of Acceptance Criteria and Reported Device Performance:

No specific quantitative acceptance criteria (e.g., pass rates for gamma analysis, dose difference thresholds) are provided in the document. The performance is described qualitatively as having "correlation with the actual treatment plan" and "good correlation" when compared to film dosimetry devices.

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

• Sample Size: Not explicitly stated. The document mentions "known patient treatment plan outputs" and "tests were also performed to compare the results... with film dosimetry devices." This suggests a test set was used, but its size is not quantified.
• Data Provenance: The tests were conducted in "non-clinical and clinical settings," but no specific country of origin is mentioned. It discusses "patient treatment plan outputs," implying patient-specific data, but it's unclear if this refers to retrospective clinical data or simulated patient plans. The design suggests these are phantom-based measurements against planned data, not direct measurements on patients.

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

• This information is not provided in the document. The "ground truth" for the device's comparison is the "simulated dose distribution... as calculated by the treatment planning system (TPS)" and "film dosimetry devices." This implies the "ground truth" is derived from computational models and established dosimetry methods rather than expert human interpretation of image data.

4. Adjudication Method for the Test Set:

• Not applicable/Not provided. The testing described involves comparing a measured dose distribution from the device to a calculated dose distribution from a TPS or a measured distribution from a film dosimetry device. This is an objective, quantitative comparison (e.g., gamma analysis, dose difference, DTA) rather than a subjective human adjudication process.

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 document describes a device for objective dose measurement and QA in radiation therapy, not an AI-assisted diagnostic tool that would involve human readers or interpretation of complex medical images. Therefore, an MRMC comparative effectiveness study involving human readers and AI assistance is not relevant to this device and was not performed.

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

• This device essentially operates in a "standalone" manner in terms of its measurement capabilities. It measures the radiation dose distribution, and its software then provides quantitative comparisons (e.g., gamma analysis, dose difference). The output (pass rates, dose maps) is then presented to a human user for review and decision-making. The core measurement and comparison functionality within the software can be considered an "algorithm only" performance, as it objectively compares two dose distributions based on predefined criteria. The human's role is to define the comparison criteria (e.g., gamma parameters) and interpret the results, but the device's primary function is the automated measurement and comparison.

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

• The ground truth used for performance evaluation is primarily:
  • Treatment Planning System (TPS) calculated dose distributions: The device's measurements are compared against the expected dose distributions generated by the TPS.
  • Film dosimetry results: Comparisons were also made against measurements from established film dosimetry devices, which themselves serve as a reference for dose distribution measurement.

8. The sample size for the training set:

• Not applicable/Not provided. The MapCHECK 2 and ArcCHECK are hardware devices with associated software for measurement and analysis, not machine learning or AI algorithms that require a "training set" for model development.

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

• Not applicable/Not provided. As stated above, these devices do not involve machine learning and therefore do not have a "training set" in the conventional sense.

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