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The LAP LUNA 3D system is used to support reproducible patient positioning and monitor patient surface motion during radiotherapy and radiosurgery treatments and during CT simulation for radiation therapy planning. LAP LUNA 3D may also be used to guide patients through breath-holds. The LAP LUNA 3D system can be used for all patients, undergoing radiotherapy and radiosurgery treatments and radiosurgery treatments and CT simulation for radiation therapy planning.

The LUNA 3D optical camera system captures the current 3D patient skin surface with one or multiple camera pods. The software calculates the spacial deviations between the captured live surface and a reference surface within a selected region of interest using a registration algorithm. Reference surfaces may be generated with the optical camera system or may be imported using data received from Treatment Planning Systems (TPS). Based on the registration results the user can adjust the patient position for reproducible patient positioning relative to the treatment isocenter. During the imaging (simulation) and treatment delivery process, the system continuously calculates the deviations between live- and reference surface for patient motion monitoring to ensure that the patient position remains within pre-defined tolerances.

Here's an analysis of the acceptance criteria and study information for the LUNA 3D device based on the provided FDA 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The FDA 510(k) summary provides limited detailed acceptance criteria and mostly focuses on comparison to a predicate device. The performance claims primarily relate to accuracy.

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

The provided summary does not specify a sample size for any clinical test set or the data provenance (e.g., country of origin, retrospective/prospective). The performance testing described is "non-clinical performance testing" which typically refers to bench testing rather than studies involving human subjects or clinical data.

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

The provided summary does not mention any test set requiring expert ground truth or the involvement of experts for this purpose. The performance testing relies on a "calibration phantom."

4. Adjudication Method for the Test Set

As no expert-derived ground truth or clinical test set is described, no adjudication method is mentioned or applicable in the provided document.

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

A MRMC comparative effectiveness study was not performed or described in the provided summary. The summary focuses on the device's standalone performance and comparison of technical characteristics to a predicate device, not on human reader improvement with or without AI assistance.

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

Yes, a standalone performance assessment was conducted. The "non-clinical performance testing in terms of warm-up drift, latency, and multi-camera accuracies of the camera pods" and the reported "positioning accuracy" and "respiratory tracking" metrics (using a calibration phantom) reflect the algorithm's performance without direct human interaction during the measurement process. The device's function is to provide information for humans to use, but the core measurements are automated.

7. Type of Ground Truth Used

The ground truth for the non-clinical performance testing was established using a calibration phantom. This is a physical object with known dimensions and properties used to assess the accuracy of the imaging system.

8. Sample Size for the Training Set

The provided summary does not specify a sample size for any training set. Given that this is a 510(k) for a medical charged-particle radiation therapy system accessory and not a purely AI/ML enabled diagnostic device, details on training data are often less emphasized in the summary unless substantial AI model development is a primary feature. The LUNA 3D is described as an "optical camera system" with "software calculates the spacial deviations... using a registration algorithm," which implies algorithms but not necessarily deep learning that would require a large training set in the typical sense.

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

As no training set is described, no information on how its ground truth was established is provided.

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The THALES 3D MR SCANNER system is designed to measure the characteristic radiation beam for an irradiation unit. The beam data is stored in the software program and can be exported from there for use during commissioning in a treatment planning system (TPS) or for quality assurance. The THALES 3D MR SCANNER system can be used with irradiation units with integrated magnetic resonance imaging and a magnetic field of less than or equal to 0.35 tesla.

The THALES 3D MR SCANNER system consists of a water phantom with a carriage system, a manual control unit and a software program. A water basin with the corresponding pump, a control unit with an electrometer, and a motor controller are integrated in the carriage system. The software program is used to control the three axes of the water basin, perform measurements and analyse measured data.

The software program has the following properties: Acquisition of measured data, analysis, and recording of photon beams with the water phantom. Generation and export of beam data libraries for a treatment planning system (TPS) for commissioning of irradiation units. The software program is equipped with a central database and/or a file system for central storage of all recorded data for different irradiation units within a large hospital network with different sites. The software program offers the option of defining different user levels to support the typical clinical workflows.

Here's an analysis of the provided text regarding the acceptance criteria and study for the THALES 3D MR Scanner:

Important Note: The provided FDA 510(k) summary focuses on demonstrating substantial equivalence to a predicate device, not necessarily on a comprehensive efficacy study with predefined acceptance criteria for its performance metrics as one might see in a clinical trial. The "acceptance criteria" here are more about meeting safety, electromagnetic compatibility, and dosimeter standards, and showing that its operational parameters are comparable or superior without introducing new risks. There is no information about a comparative effectiveness study (MRMC) with human readers, nor a standalone algorithm performance study as would be applicable for AI/CADe devices.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not describe a "test set" in the context of an AI/ML algorithm or a specific patient-data based study. Instead, it details performance characteristics of a physical measurement device (water phantom) for radiation therapy. The performance testing refers to evaluating the device's physical and electrical characteristics against standards (IEC, ASTM). Therefore, there is no information about a sample size or data provenance related to patient data. The "test set" implies physical testing of the device itself.

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

Not applicable. The ground truth for this device's performance is established by engineering and physics standards (IEC, ASTM) for safety, compatibility, and measurement accuracy, not by human expert consensus on clinical data.

4. Adjudication Method for the Test Set

Not applicable. There's no clinical data or expert review process described that would require an adjudication method.

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

No. The document makes no mention of an MRMC study or any study involving human readers or comparative effectiveness in a clinical workflow with or without AI assistance. This device is a measurement tool, not an AI diagnostic or assistive tool in the traditional sense.

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

No. This is not an AI algorithm but a physical device with integrated software to control movement, acquire, analyze, and export measurement data. Its "performance" refers to how accurately it physically measures radiation beams, not its ability to interpret images or suggest diagnoses.

7. The Type of Ground Truth Used

The ground truth used for validating the device's performance comes from established engineering and medical device standards:

• Electrical and mechanical safety: IEC 61010
• EMC: IEC 60601-1-2
• Dosimeter requirements: IEC 60731
• MRI safety: Applicable ASTM standards
• Performance metrics (e.g., position accuracy, electrometer resolution) are likely validated against manufacturer-defined specifications derived from engineering principles and comparison to the predicate device.

8. The Sample Size for the Training Set

Not applicable. This device is not an AI/ML model that requires a "training set" of data in the conventional sense. Its software controls hardware and processes raw measurement data based on established physics, not learned patterns from a "training set."

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

Not applicable, as there is no training set for an AI/ML algorithm.

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The DORADOnova MR3T system assists in reproducible patient positioning during radiation therapy planning and is utilized during treatment planning workflow. The DORADOnova MR3T system projects visual laser beams which can be positioned at specified coordinates via a control system. The position coordinates are typically chosen by the operator or provided by an external system (e.g. treatment planning system). The DORADOnova MR3T system is allowed to be used in MR environment with a magnetic field, less or equal to 3 Tesla.

The LAP moving laser positioning system contains laser devices and a control system. The laser system is provided in a configuration that enables the movement of two projection planes by using an appropriate composition of motorized and fixed laser devices. The installation of the system is realized in a combined bridge solution.

This FDA 510(k) summary (K173926) describes the DORADOnova MR3T system, a medical device used for reproducible patient positioning during radiation therapy planning. The document primarily focuses on demonstrating substantial equivalence to predicate devices rather than providing a detailed clinical study with acceptance criteria for an AI/algorithm-driven device.

Therefore, many of the requested elements for describing the acceptance criteria and the study that proves the device meets them (especially those related to AI/algorithm performance, ground truth establishment, expert adjudication, and MRMC studies) are not applicable to this specific device and document, as it is a laser positioning system, not an AI-powered diagnostic or therapeutic device.

However, based on the provided text, I can extract the relevant performance specifications and how they were tested.

Device Name: DORADOnova MR3T (LAP moving laser positioning system)

Device Type: Medical charged-particle radiation therapy system (21 CFR 892.5050, Class II) - Specifically, a laser positioning system.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a physical positioning device and not an AI/algorithm, "acceptance criteria" are tied to its physical specifications and safety standards, largely demonstrated through bench testing and compliance with recognized standards.

Study Details for DORADOnova MR3T Performance:

This device's performance demonstration relies on engineering specifications, adherence to international safety standards, and bench testing, rather than a clinical study in the typical sense for AI/diagnostic devices.

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

   • Not applicable in the typical AI/diagnostic study sense.
   • Performance testing described is focused on physical properties, safety, and compatibility. Tests were likely conducted on a defined number of device units or prototypes. Data provenance is implied to be from internal company testing conducted in Germany (location of LAP GmbH Laser Applikationen).

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

   • Not applicable. Ground truth for device performance (e.g., accuracy, resolution, safety compliance) is established through standardized engineering measurements and compliance with regulatory standards, not expert clinical interpretation of data.

3. Adjudication Method for the Test Set:

   • Not applicable. This falls under engineering and regulatory compliance testing, not clinical diagnosis where adjudication is needed.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done:

   • No. This is not an AI-assisted diagnostic or CAD device requiring MRMC studies. Its function is to project light for patient positioning.

5. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was Done:

   • Not applicable. This is a physical system with an operator, not an AI algorithm. Its performance directly relates to its ability to project lasers with specified accuracy.

6. The Type of Ground Truth Used:

   • Engineering/Physical Measurement Ground Truth: For parameters like accuracy, resolution, and line width, the ground truth is established by precise metrological measurements using calibrated equipment.
   • Standard Compliance Ground Truth: For safety (electrical, laser, EMC, MRI), the "ground truth" is adherence to the specified international and national standards (IEC 60601-1, IEC 60601-1-2, IEC 60825-1, 21 CFR 1040.10, ASTM standards for MRI safety).

7. The Sample Size for the Training Set:

   • Not applicable. This device does not use machine learning or AI models that require a "training set."

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

   • Not applicable, as there is no training set.

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