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ExacTrac Dynamic is intended to position patients at an accurately defined point within the treatment beam of a medical accelerator for stereotactic radiosurgery or radiotherapy procedures, to monitor the patient position and to provide a beam hold signal in case of a deviation in order to treat lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

ExacTrac Dynamic (ETD) is a patient positioning and monitoring device used in a radiotherapy environment as an add-on system to standard linear accelerators (linacs). It uses radiotherapy treatment plans and the associated computed tomography (CT) data to determine the patient's planned position and compares it via oblique X-ray images to the actual patient position. The calculated correction shift will then be transferred to the treatment machine to align the patient correctly at the machine's treatment position. During treatment, the patient is monitored with a thermal-surface camera and X-ray imaging to ensure that there is no misalignment due to patient movement. Positioning and monitoring are also possible in combination with implanted markers. By defining the marker positions, ExacTrac Dynamic can position the patient by using X-rays and thereafter monitor the position during treatment.

Additionally, ExacTrac Dynamic features a breath-hold (BH) functionality to serve as a tool to assist respiratory motion management. This functionality includes special features and workflows to correctly position the patient at a BH level and thereafter monitor this position using surface tracking. Regardless of the treatment indication, a correlation between the patient's surface and internal anatomy must be evaluated with Image-Guided Radiation Therapy. The manually acquired X-ray images support a visual inspection of organs at risk (OARs). The aim of this technique is to treat the patient only during breath hold phases where the treatment target is at a certain position to reduce respiratory-induced tumor motion and to ensure a certain planned distance to OARs such as the heart. In addition to the X-ray based positioning technique, the system can also monitor the patient after external devices such as Cone-Beam CT (CBCT has been used to position the patient).

The ExacTrac Dynamic Surface (ETDS) is a camera-only platform without the X-ray system and is available as a configuration which enables surface-based patient monitoring. This system includes an identical thermal-surface camera, workstation, and interconnection hardware to the linac as the ETD system. The workflows supported by ETDS are surface based only and must be combined with an external IGRT device (e.g., CBCT).

The FDA 510(k) summary for Brainlab AG's ExacTrac Dynamic (2.0) and ExacTrac Dynamic Surface provides information regarding its performance testing to demonstrate substantial equivalence to its predicate device, ExacTrac Dynamic 1.1 (K220338).

Here is a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state a table of "acceptance criteria" side-by-side with "reported device performance" values for all aspects of the device. However, it does reference the AAPM Task Group 1472 guidelines as a reference for the accuracy of surface-based monitoring and indicates that specific tests aimed to verify that accuracy specifications were not negatively affected. From the "Bench Tests" section, we can infer the objectives of the tests and how performance was evaluated.

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

• Sample Size for Test Set: The document mentions the use of an "in-house phantom" for the Rigid Body Surface Monitoring Accuracy Test and "phantom treatment" for the Workflow & Accuracy Test. It does not provide specific numerical sample sizes (e.g., number of phantom instances, number of trials).
• Data Provenance: All testing appears to be retrospective (bench tests, phantom studies) and conducted internally (in-house phantom). There is no mention of data from human subjects or specific countries of origin.

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

This information is not provided in the document. The testing described focuses on device performance against physical standards (e.g., phantom, previous system performance) and referenced guidelines (AAPM Task Group 1472), rather than expert-established ground truth on clinical images.

4. Adjudication Method for the Test Set

This information is not provided in the document. Given the nature of the bench and phantom tests, an adjudication method by experts is not described as it would be for clinical image interpretation studies.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No, an MRMC comparative effectiveness study was not reported. The document states, "No clinical testing was required for the subject device." The testing described focuses on the device's physical performance, accuracy, and workflow.

6. Standalone Performance (Algorithm Only without Human-in-the-Loop)

The described tests, specifically the "Rigid Body Surface Monitoring Accuracy Test," "Workflow & Accuracy Test ExacTrac Dynamic," "Response Time Measurement," and "Verification of the Radiation Isocenter Calibration," appear to evaluate the device's inherent performance characteristics, often in a controlled phantom environment. This implies a focus on the standalone capabilities of the system, even though its ultimate use is in assisting human operators in a radiotherapy setting. The "Beam-off signal" in response to movement is an automated system response, indicating standalone algorithmic functioning.

7. Type of Ground Truth Used

The ground truth for the performance tests appears to be:

• Physical Phantoms: An "in-house phantom" for surface monitoring accuracy and "phantom treatment" for workflow and accuracy.
• Established Reference System Performance: Comparison to the "previous, well-established Radiation Isocenter Calibration in ETD 1.1."
• Industry Guidelines: Reference to "quality assurance guidelines for non-radiographic radiotherapy localization and positioning systems, that were defined by AAPM Task Group 1472."
• Expected System Behavior: Verification of expected responses (e.g., "Beam-off signal") to phantom movement.

8. Sample Size for the Training Set

This information is not provided in the document. The 510(k) summary focuses on verification and validation testing, not the development or training of specific algorithms that would require a "training set" in the context of machine learning.

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

As no training set is mentioned (see point 8), this information is not applicable/provided.

Ask a specific question about this device

ExacTrac Dynamic is intended to position patients at an accurately defined point within the treatment beam of a medical accelerator for stereotactic radiosurgery or radiotherapy procedures, to monitor the patient position and to provide a beam hold signal in case of a deviation in order to treat lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

ExacTrac Dynamic is a patient positioning device used in a radiotherapy environment as an addon system to standard linear accelerators. It uses patient planning and CT data to determine the patient's planned position and compares it via oblique x-ray images to the actual patient position. The calculated correction shift will then be transferred to the treatment machine to align the patient correctly at the machine's treatment position. During treatment is monitored with a surface camera and X-ray to ensure no misalignment due to patient movement.

ExacTrac Dynamic 1.1.2 is a modification of the previously cleared device ExacTrac Dynamic 1.1 that features a Deep Inspiration Breath-Hold (DIBH) functionality to treat breast cancer. This functionality helps correctly position the patient to a deep inspiration breath-hold level and then to monitor this position using surface tracking and x-ray positioning technology. The aim of this technology is to treat the patient only during breath-hold phases where the breast is at a defined position with a maximum distance to critical structures like the heart. Additionally the surface tracking functionality was extended, which monitors the patient after an initial 3rd party positioning.

The main functionalities has remained same for the Subject Device. The modifications are done on a specification level to implement additional measures.

The provided text describes a 510(k) premarket notification for the Brainlab AG ExacTrac Dynamic (1.1.2) device. This submission outlines that the device is a modification of a previously cleared predicate device (ExacTrac Dynamic 1.1) and primarily focuses on addressing identified software bugs and extending existing functionality (Deep Inspiration Breath-Hold and surface tracking).

Crucially, the document states: "In order to address the identified bugs, certain specifications and tests related to the bug fixes (as detailed in Section 4) with ExacTrac Dynamic 1.1.2 were modified to include additional measures. These modified specifications were verified via incremental tests. All tests were passed."

It also explicitly states: "The bug fix did not require any change to the existing software architecture." and "There was no change of intended use, technological characteristics or typical users."

Therefore, the information required to fully answer your request (acceptance criteria and a study that proves the device meets the acceptance criteria, as detailed in your bullet points) is largely absent from this 510(k) summary.

This type of 510(k) submission, focused on minor software modifications (bug fixes) to an already cleared device, typically relies on verification and validation (V&V) testing to demonstrate that the bug fixes have been successfully implemented and have not introduced new issues or adversely affected existing functionalities. It does not typically involve the kind of extensive clinical performance study (like an MRMC study with human readers, or a standalone algorithm performance study with a large, adjudicated test set) that would be conducted for a new AI/ML-driven diagnostic device or a device with significant functional changes.

Here's an analysis based on the provided text, highlighting what is missing:

Absence of Clinical Performance Study Details:

The document explicitly states that the modifications were "verified via incremental tests" and that "All tests were passed." This indicates a focus on engineering and software testing rather than a clinical performance study involving human readers or a large-scale, algorithm-only performance study against clinical ground truth. The acceptance criteria described are implicitly tied to the successful resolution of the identified bugs and the continued proper functioning of the device features.

The document lists numerous bug fixes, primarily concerning software behavior, data handling, and specific use case scenarios (e.g., DIBH beam control, patient deletion, X-ray triggering). The "performance data" section (Section 3) is very brief and refers back to the bug fixes and incremental tests rather than presenting a detailed study.

Detailed Breakdown of Missing Information as per your Request:

Given the nature of this 510(k) for bug fixes, many of your requested points are not applicable or the information is not present in the provided document.

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

   • Acceptance Criteria: Not explicitly laid out in a table in the provided text. The implicit acceptance criteria are that the bug fixes resolve the identified issues and that other functionalities remain unaffected and continue to meet their original design specifications.
   • Reported Device Performance: No quantitative performance metrics (e.g., sensitivity, specificity, accuracy, or precise shift measurements) are reported. The performance is implied by the passing of "incremental tests" for the bug fixes.

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

   • Sample Size: Not specified. "Incremental tests" suggest a focused set of tests for each bug, likely using synthetic data or a limited set of real-world scenarios to reproduce and verify the fix for each bug. This is not a "test set" in the sense of a large, independent clinical dataset.
   • Data Provenance: Not specified. Given this is a software update for an existing device, the testing would likely involve internal company testing environments.

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):

   • Number of Experts/Qualifications: Not applicable and not mentioned. The "ground truth" for bug fixes is the correct software behavior as defined by the design specifications and user requirements, not a clinical diagnosis established by experts.

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

   • Adjudication Method: Not applicable. This concept is for establishing ground truth in clinical image interpretation studies, not for software bug verification.

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 type of study focuses on clinical benefit and human-AI interaction for diagnostic or interpretive tasks. ExacTrac Dynamic is a patient positioning and monitoring system for radiation therapy, not a diagnostic imaging AI. The changes here are bug fixes, not enhancements that would directly impact a human reader's diagnostic performance.

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

   • Standalone Performance: No quantitative standalone performance data is presented in the context of clinical metrics (e.g., accuracy of positioning, false positives/negatives for beam hold signals). The "performance data" refers to the successful passing of internal verification tests for bug fixes.

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

   • Type of Ground Truth: For bug fixes, the "ground truth" is the predefined correct behavior of the software feature according to its specifications. This is established during the software development and design phase, not through external clinical adjudication.

8. The sample size for the training set:

   • Training Set Sample Size: Not applicable and not mentioned. This device (or this specific update) does not describe using machine learning models that require a "training set" in the typical sense of AI/ML development for image analysis. The "Deep Inspiration Breath-Hold" functionality and "surface tracking" are described as features, but the document does not indicate that new ML models were trained as part of this specific update. The changes are described as "bug fixes" and "modified specifications."

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

   • Ground Truth for Training Set: Not applicable, as there's no mention of a training set for machine learning.

Conclusion:

This 510(k) summary for ExacTrac Dynamic (1.1.2) is a "minor change" submission focusing on software bug fixes to an already cleared predicate device. It explicitly highlights that there were no changes to the intended use, technological characteristics, or typical users. As such, the supporting documentation provided in the snippet is consistent with the type of verification and validation (V&V) typically conducted for such modifications, which relies on internal engineering and software testing ("incremental tests") to ensure the fixes are effective and do not introduce new issues, rather than large-scale clinical performance studies. Therefore, the detailed information requested regarding clinical study design, sample sizes, expert ground truth, and AI/ML training is not present in this particular FDA submission document snippet.

Ask a specific question about this device

ExacTrac Dynamic is intended to position patients at an accurately defined point within the treatment beam of a medical accelerator for stereotactic radiosurgery or radiotherapy procedures, to monitor the patient position and to provide a beam hold signal in case of a deviation in order to treat lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

ExacTrac Dynamic is a patient positioning device used in a radiotherapy environment as an addon system to standard linear accelerators. It uses patient planning and CT data to determine the patient's planned position and compares it via oblique x-ray images to the actual patient position. The calculated correction shift will then be transferred to the treatment machine to align the patient correctly at the machine's treatment position. During treatment is monitored with a surface camera and X-ray to ensure no misalignment due to patient movement.
ExacTrac Dynamic 1.1 is a modification of the previously cleared device ExacTrac Dynamic 1.0 that additionally features a Deep Inspiration Breath-Hold (DIBH) functionality to treat breast cancer. This functionality helps correctly position the patient to a deep inspiration breath-hold level and then to monitor this position using surface tracking and x-ray positioning technology. This functionality was not included in ExacTrac Dynamic 1.0. The aim of this technology is to treat the patient only during breath-hold phases where the breast is at a defined position with a maximum distance to critical structures like the heart. Additionally, the surface tracking functionality was extended, which monitors the patient after an initial 3rd party positioning.

The provided text describes the 510(k) submission for Brainlab AG's ExacTrac Dynamic 1.1, a medical device for patient positioning and monitoring during radiation therapy. The key new features in version 1.1 are Deep Inspiration Breath-Hold (DIBH) functionality and an extended "Surface Only" tracking mode.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

The document does not present a formal table of acceptance criteria with corresponding reported device performance values. Instead, it states the objectives for the clinical investigation of the new DIBH feature, which can be interpreted as acceptance criteria.

Table of Acceptance Criteria and Reported Device Performance

Note: The document explicitly states, "All acceptance criteria for the successful completion of the study were passed." This implies that the device performance met or exceeded these criteria. However, specific numerical results or confidence intervals are not provided in this summary.

Study Details

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

• Test Set (Clinical Investigation for DIBH):
  • Study Population: 13 female subjects. These subjects were divided into:
    • Patient Population: Women diagnosed with breast cancer indicated for radiation therapy with DIBH, currently or previously treated with this technique. (Retrospective/Prospective unclear but likely prospective for the study itself).
    • Volunteer Population: Healthy women resembling breast cancer patients concerning physiognomy and age, able to perform deep and long DIBHs. (Prospective).
  • Data Provenance: The document does not explicitly state the country of origin but implies a single-center clinical investigation ("single-center Clinical Investigation" on page 10). Given the manufacturer is German, it's possible the study was conducted in Germany or a location aligned with their operations. The data collection was prospective for the purposes of this clinical investigation.

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

• The document does not provide information on the number of experts or their qualifications used to establish ground truth for the clinical investigation. The "ground truth" for the DIBH study appears to be based on the device's ability to reproduce a target DIBH state and achieve a certain pre-positioning accuracy, which would likely be measured against independent, precise measurements by the study team, rather than expert interpretation of images. However, if any such expert review was involved, it's not detailed here.

4. Adjudication Method for the Test Set

• The document does not describe any adjudication method for the test set. The clinical investigation appears to focus on quantitative measurements of DIBH reproduction and pre-positioning accuracy, rather than subjective assessments requiring adjudication.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• No, an MRMC comparative effectiveness study was not done according to the provided text. The study described focuses on the device's ability to achieve specific accuracy targets for DIBH and pre-positioning, and does not compare human readers with or without AI assistance. The device is a patient positioning and monitoring system, not primarily an image interpretation AI.

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

• The clinical investigation evaluates the "surface camera system-based components of the ETD DIBH module" in assisting human patients. While parts of the system operate algorithmically, the "clinical investigation" context implies the system's performance in aiding human execution of DIBH. However, the accuracy tests (e.g., "Accuracy Test: The test objective is to verify that accuracy specifications for positioning and monitoring of ExacTrac Dynamic are not affected by the selected workflow, treatment parameters and different phantom positions.") strongly suggest standalone algorithm performance was evaluated using phantoms in the general "Performance Data" section. Specific standalone results from these phantom tests are not detailed beyond the statement that requirements were met.

7. The Type of Ground Truth Used

• For the DIBH clinical investigation, the ground truth was based on quantitative measurements related to:
  • Reproduction of "a defined state of deep inspiration breath-hold" within +/- 3 mm, likely measured by a highly accurate external reference.
  • Translational accuracy of pre-positioning "better than 6 mm," also presumably measured against a reference.
• For the "Accuracy Test" mentioned in the Performance Data section, the ground truth was likely established against known, precise phantom positions and deviations.

8. The Sample Size for the Training Set

• The document does not provide any information about the sample size for a training set. This submission is for a modification (ExacTrac Dynamic 1.1) of an existing device (ExacTrac Dynamic 1.0). While the device uses "Deep Inspiration Breath-Hold (DIBH) functionality" which "helps correctly position the patient to a deep inspiration breath-hold level and then to monitor this position using surface tracking and x-ray positioning technology," it doesn't explicitly state that this new functionality is based on a machine learning model that required a specific training set from patient data. If it uses internal algorithms, those would have been developed and tested, but "training set" details are not discussed.

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

• Since a training set is not explicitly mentioned (as the new features are described as functionalities, not necessarily trained AI models in the conventional sense), no information is provided on how its ground truth was established. If underlying algorithms were "trained," the method would depend on the type of algorithm (e.g., precise measurements on phantoms, or expert annotations if it involved image recognition/assessment).

Ask a specific question about this device

ExacTrac Dynamic is intended to position patients at an accurately defined point within the treatment beam of a medical accelerator for stereotactic radiosurgery or radiotherapy procedures, to monitor the patient position and to provide a beam hold signal in case of a deviations in order to treat lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

ExacTrac Dynamic is a patient positioning device used in a radiotherapy environment as an addon system to standard linear accelerators. It uses patient planning and CT data to determine the patient's planned position and compares it via oblique x-ray images to the actual patient position. The calculated correction shift will then be transferred to the treatment machine to align the patient correctly at the machine's treatment position. During treatment is monitored with a surface camera and X-ray to ensure no misalignment due to patient movement.

ExacTrac as a medical device consists of Hardware and Software. Together they are ExacTrac Dynamic.

ExacTrac Dynamic 1.0.3 UDI DI: 04056481142506 ExacTrac Dynamic has no variants.

ExacTrac Dynamic shall be installed in a treatment room within a hospital radiation therapy department.

ExacTrac uses X-ray images acquired with two X-ray tubes and two amorphous silicon detectors to compare the current patient position with the previously planned patient position, based on CT volumetric scans.

The current patient position is monitored using a surface monitoring system.

If necessary, the patient position is corrected using 3rd party patient treatment tables with or without pitch and roll correction possibilities.

Stereotactic radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT) are a highly accurate form of the radiation therapy initially developed to treat small (benign or malignant) tumors and functional abnormalities of the brain.

During treatment a high dose of radiation is delivered within millimeter accuracy to the planned target volume (PTV) while minimizing the dose to the surrounding healthy tissue and organs at risk (OARs). For an accurate dose delivery a precise patient positioning is necessary and an eventual re-positioning of the patient in case of a patient movement during the treatment. The patient positioning system is based on two non-coplanar x-ray 2D image acquisition, that is accurately aligned to the volumetric planning CT (from the plan), the transformation matrix (of the 2D or 3D alignment) is sent to the treatment couch moving the patient to the planned iso-center of the treatment delivery system (Linac). During the treatment delivery the patient movement is monitored via a (3D) surface and a thermal information.

Additionally, an x-ray based position acquisition may be acquired and compared with the planned patient position. Quality assurance procedure are included to ensure a calibration of ExacTrac to itself and to the LINAC

The provided document does not contain the specifics of a study evaluating the device against acceptance criteria in the way typically required for machine learning or AI-driven medical devices. The K211939 submission primarily deals with a bug fix for an existing device (ExacTrac Dynamic 1.0) and focuses on regulatory compliance, electrical safety, EMC, and software verification/validation, rather than a clinical performance study with a test set, ground truth, and expert adjudication.

The "Performance Data" section lists "Essential Performance in detail" which could be interpreted as acceptance criteria, but no study is described to prove these criteria were met with specific quantitative results.

However, I can extract the "acceptance criteria" (from the "Essential Performance in detail" section) and describe what a study would need to look like based on the information provided, even if the detailed study results are not present in this document.

Here's a breakdown assuming the "Essential Performance in detail" are the acceptance criteria, and then explaining what data is missing:

Acceptance Criteria and Reported Device Performance

Based on the "Essential Performance in detail" section, the following can be inferred as acceptance criteria:

Table 1: Inferred Acceptance Criteria and Stated Performance

Important Note: The document presents the accuracy and timing requirements directly within the description of the "Essential Performance," rather than providing a separate table of acceptance criteria followed by a table of results from a performance study demonstrating that these criteria have been met. This suggests that the listed values are the expected or designed performance which the bug fix (K211939) did not alter, and that the original predicate device (K201276) would have been cleared based on demonstrating these values previously. The current submission focuses on demonstrating that the bug fix did not negatively impact these performances.

Details of the Study (Based on typical requirements, but largely missing from the provided document Specifics):

The provided document does not describe a detailed performance study with a test set, ground truth, or expert adjudication that quantitatively assesses the 1mm accuracy or 3-second beam hold for the ExacTrac Dynamic (K211939) as if it were a new device needing to prove these metrics. Instead, it refers to "Various verification and validation tests" and states that "The Subject device has passed all the necessary tests and thereby is considered safe and effective for its intended use." The submission is for a bug fix to an already cleared device, implying that the detailed performance validation would have been done for the predecessor (K201276).

Therefore, the following answers are based on what would typically be required for such a device to gain clearance or what could be inferred/assumed given the nature of the device, but the specific details are not explicitly stated in the provided text.

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

• Sample Size: Not specified in the provided document. For a precision device like this, the "test set" would likely involve phantoms or controlled patient movements, rather than a large clinical image dataset like for an AI diagnostic device. The "accuracy of 1mm" suggests a direct measurement against a known standard.
• Data Provenance: Not specified. Given the nature of a medical device manufacturer, testing would most likely be conducted in a controlled lab or clinical environment, possibly internally or by a contracted lab. The manufacturer (Brainlab AG) is based in Germany, so the primary testing might have occurred there, or potentially at US sites for clinical validation. The document implies these are technical performance specifications rather than clinical data.

3. Number of Experts and Qualifications for Ground Truth:

• Number of Experts: Not applicable/specified. For a device measuring physical displacement, ground truth would be established by precisely calibrated measurement tools (e.g., optical tracking systems, high-precision CMMs, or verified phantom positions) rather than human experts interpreting images.
• Qualifications of Experts: N/A, as ground truth would be physical measurements.

4. Adjudication Method for the Test Set:

• Method: Not applicable. Adjudication methods (like 2+1, 3+1) are used to resolve disagreements among human experts when establishing ground truth for subjective interpretations (e.g., classification of medical images). For a device performing precise physical positioning/monitoring, the "ground truth" is typically a direct physical measurement with a known, higher accuracy.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:

• MRMC Study Done? No. This type of study assesses how AI assistance impacts human reader performance (e.g., radiologists interpreting images). ExacTrac Dynamic is a physical positioning and monitoring system, not an AI image interpretation tool. Its "AI" component would be in its tracking algorithms, not in assisting human diagnostic reading.
• Effect Size of Improvement with AI: Not applicable for this device type.

6. Standalone (Algorithm-Only) Performance:

• Standalone Performance Done? Yes, effectively. The device's stated "accuracy of 1mm" for positioning and monitoring (criteria i-iv) and "3-second beam hold" (criterion v) are standalone performance metrics of the algorithm and hardware working together. The device's function is to autonomously (or with user-configured tolerances) perform these actions. There isn't a human-in-the-loop directly influencing the "1mm accuracy" calculation or the "3-sec beam hold" trigger.

7. Type of Ground Truth Used:

• Type of Ground Truth: Likely physical measurements from highly accurate, independent reference systems or phantoms with precisely known geometries. For example:
  • For positional accuracy (1mm), a calibrated phantom moved by a precision stage, with its true position simultaneously measured by an external optical tracking system with sub-millimeter accuracy, would serve as ground truth.
  • For beam hold time, a timestamped signal from the LINAC triggered by a known deviation, compared to a timestamp from the ExacTrac system, would be used.

8. Sample Size for the Training Set:

• Sample Size: Not applicable/specified. This is not described as a deep learning/AI device trained on a large image dataset in the context of generating diagnostic insights. It's a precise medical device based on pre-programmed algorithms for image registration, surface tracking, and real-time computation of shifts and deviations. While internal algorithms might have been "trained" or optimized during development, it would not be in the typical sense of a "training set" for a sophisticated AI model like an image classifier. The "training" would be more about calibration and refinement of image processing and control loop parameters.

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

• How Ground Truth Was Established: Not applicable/specified. As per point 8, the concept of a "training set" and associated ground truth is not detailed for this type of device in the provided document. The device's underlying principles are physics-based (X-ray geometry, surface topography) rather than statistical learning from a vast dataset of medical images.

Ask a specific question about this device

ExacTrac Dynamic is intended to position patients at an accurately defined point within the treatment beam of a medical accelerator for stereotactic radiosurgery or radiotherapy procedures, to monitor the patient position and to provide a beam hold signal in case of a deviations in order to treat lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

ExacTrac Dynamic is a patient positioning device used in a radiotherapy environment as an add-on system to standard linear accelerators. It uses patient planning and CT data to determine the patient's planned position and compares it via oblique x-ray images to the actual patient position. The calculated correction shift will then be transferred to the treatment machine to align the patient correctly at the machine's treatment position. During treatment the patient is monitored with a surface camera and X-ray to ensure no misalignment due to patient movement.

ExacTrac as a medical device consists of Hardware and Software. Together they are ExacTrac Dynamic.

ExacTrac uses X-ray images acquired with two X-ray tubes and two amorphous silicon detectors to compare the current patient position with the previously planned patient position, based on CT volumetric scans.

The current patient position is monitored using a surface monitoring system.

If necessary, the patient position is corrected using 3rd party patient treatment tables with or without pitch and roll correction possibilities.

Stereotactic radiosurgery (SRS) and Stereotactic Body Radiation Therapy (SBRT) are a highly accurate form of the radiation therapy initially developed to treat small (benign or malignant) tumors and functional abnormalities of the brain.

During treatment a high dose of radiation is delivered within millimeter accuracy to the planned target volume (PTV) while minimizing the dose to the surrounding healthy tissue and organs at risk (OARs). For an accurate dose delivery a precise patient positioning is necessary and an eventual re-positioning of the patient in case of a patient movement during the treatment. The patient positioning system is based on two non-coplanar x-ray 2D image acquisition, that is accurately aligned to the volumetric planning CT (from the plan), the transformation matrix (of the 2D or 3D alignment) is sent to the treatment couch moving the patient to the planned isocenter of the treatment delivery system (Linac). During the treatment delivery the patient movement is monitored via a (3D) surface and a thermal information.

Additionally, an x-ray based position acquisition may be acquired and compared with the planned patient position. Quality assurance procedure are included to ensure a calibration of ExacTrac to itself and to the LINAC

The provided text describes the 510(k) premarket notification for the Brainlab AG ExacTrac Dynamic device. It outlines the device's intended use, description, and performance data.

Here's an analysis of the acceptance criteria and study proving the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The document summarizes the essential performance characteristics with specific accuracy requirements.

2. Sample Size and Data Provenance

The document does not specify the sample size used for the test set or the data provenance (e.g., country of origin, retrospective/prospective). It generally states that "Various verification and validation tests were carried out."

3. Number of Experts used for Ground Truth and Qualifications

The document does not provide any information regarding the number of experts used to establish ground truth or their qualifications.

4. Adjudication Method for the Test Set

The document does not mention any adjudication method (e.g., 2+1, 3+1, none) used for the test set.

5. Multi Reader Multi Case (MRMC) Comparative Effectiveness Study

There is no indication that an MRMC comparative effectiveness study was done to evaluate how human readers improve with AI vs. without AI assistance. The device is described as a patient positioning and monitoring system, not primarily an AI-assisted diagnostic tool for human readers. The change was a bug fix, not an AI performance improvement.

6. Standalone (Algorithm Only) Performance

The document describes the device's standalone performance in terms of its ability to calculate shifts, display deviations, and indicate deviations/beam holds with 1mm accuracy and within 3 seconds. The "Performance Data" section directly addresses the device's functionality. The tests mentioned ("Various verification and validation tests") would have assessed this standalone performance.

7. Type of Ground Truth Used

The type of ground truth used is implicitly physical accuracy/measurement. The criteria are based on achieving a 1mm accuracy for positioning and monitoring, and a 3-second response time for the beam hold. This would typically be verified against precisely controlled physical phantoms or established reference measurements rather than expert consensus on images or pathology.

8. Sample Size for the Training Set

The document does not mention any training set sample size. The current submission is for a modification (bug fix) to an existing predicate device (ExacTrac Dynamic 1.0). There is no indication of a new machine learning model being trained or requiring a training dataset.

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

Since there is no mention of a training set or new machine learning model in this specific submission, the method for establishing ground truth for a training set is not applicable or described. The submission focuses on a bug fix and verifying existing functionalities and safety standards.

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ExacTrac Dynamic is intended to position patients at an accurately defined point within the treatment beam of a medical accelerator for stereotactic radiosurgery or radiotherapy procedures, to monitor the patient position and to provide a beam hold signal in case of a deviations in order to treat lesions, tumors and conditions anywhere in the body when radiation treatment is indicated.

ExacTrac Dynamic is a patient positioning device used in a radiotherapy environment as an add-on system to standard linear accelerators. It uses patient planning and CT data to determine the patient's planned position and compares it via oblique x-ray images to the actual patient position. The calculated correction shift will then be transferred to the treatment machine to align the patient correctly at the machine's treatment position. During treatment the patient is monitored with a surface camera and Xray to ensure no misalignment due to patient movement.

ExacTrac as a medical device consists of Hardware and Software. Together they are ExacTrac Dynamic.

This document is a 510(k) summary for Brainlab AG's ExacTrac Dynamic, a patient positioning device for radiotherapy. It details the device's intended use, description, and performance data to demonstrate substantial equivalence to its predicate device, ExacTrac K120789.

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

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

The document lists "Essential Performance Characteristics" which are effectively the acceptance criteria for the device's key functionalities. The reported device performance is stated as meeting these criteria.

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

The document does not explicitly state the sample size used for performance testing (test set) or the data provenance (e.g., country of origin, retrospective/prospective). It generally states that "Various verification and validation tests were carried out on the System, Hardware and Software as applicable."

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 on the number or qualifications of experts used to establish ground truth for the test set. The performance criteria are defined in terms of measurable physical accuracies (e.g., 1mm accuracy), implying technical measurements rather than expert consensus on diagnostic images.

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

The document does not describe an adjudication method for the test set. Given the nature of the performance criteria (physical accuracy measurements), an adjudication process involving multiple human observers for a "test set" in the context of diagnostic interpretation is not applicable.

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 describe a multi-reader multi-case (MRMC) comparative effectiveness study. The device focuses on patient positioning and monitoring accuracy, not on diagnostic interpretation aided by AI for human readers. Therefore, an effect size of human reader improvement with or without AI assistance is not relevant to the scope of this submission.

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

The entire performance section (Section 4) focuses on the device's (ExacTrac Dynamic) direct performance metrics, such as accuracy of shift calculation, display of deviation, and beam hold signal timing. These are described as functionalities provided by the system itself. Since the device's primary function is automated patient positioning and monitoring based on imaging and feedback, it inherently describes "standalone" performance in terms of its ability to measure and react. The "human-in-the-loop" aspect comes from the user setting tolerances and using the information presented, but the reported accuracies are of the system's measurements and calculations.

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

The ground truth for the accuracy measurements (e.g., 1mm accuracy for shifts and deviations) appears to be physical measurement-based (e.g., phantom studies, engineering tolerances). The document states that the system compares the "actual patient position" (measured by X-ray images and surface monitoring) to the "previously planned patient position" (from CT volumetric scans). This implies that the ground truth for evaluating the device's accuracy would involve knowing the true physical position and comparing the device's output to that known position. This is typical for precision mechanical and image registration systems.

8. The sample size for the training set

The document does not mention or describe a "training set" in the context of machine learning or AI. ExacTrac Dynamic is described as using principles of X-ray imaging and comparison with CT data, and surface tracking. While there might be internal algorithms that could benefit from data, the document does not present it as an AI/ML device that undergoes explicit "training" in the modern sense. Therefore, no training set size is provided.

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

As no training set is mentioned (see point 8), this information is not provided in the document.

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