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Halcyon and Ethos radiotherapy system are indicated for the delivery of stereotactic radiosurgery and precision radiotherapy for lesions, tumors, and conditions anywhere in the body where radiation is indicated for adults and pediatric patients.

Halcyon and Ethos radiotherapy system with the HyperSight imaging feature produce kV CBCT anatomical images that can be used in the simulation and planning of radiation therapy.

Halcyon and Ethos Radiotherapy System are single energy medical linear accelerators (linacs) designed to deliver Image Guided Radiation Therapy and radiosurgery, using Intensity Modulated and Volumetric Modulated Arc Therapy techniques. They consist of the accelerator and patient support within a radiation shielded treatment room and a control console outside the treatment room.

An electron gun generates electrons which are accelerated by radio frequency (RF) power from a magnetron. The electrons strike a tungsten target producing photons (X-rays) for treatment and MV Imaging. The photons produced by the target are monitored and controlled by a pressurized ion chamber.

A beam collimation subsystem consisting of a primary and secondary collimator and two stacked multileaf collimators (MLCs) shapes the photon beam to define the treatment area.

X-Ray images of the patient are used by the treater to verify the correct treatment location. MV Imaging uses the treatment beam and a flat panel imager whereas kV imaging uses a high-capacity kV X-ray tube, a kV collimation system with full fan bowtie filter with movable y-blades to define the imaging beam size and to capture the image, a kV imager.

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The Elekta Medical Linear Accelerator (EMLA) is intended to be used for external beam radiation therapy (EBRT) treatments as determined by a licensed medical practitioner.

It is intended to assist a licensed medical practitioner in the delivery of EBRT to defined target volumes, while sparing surrounding normal tissue and critical organs from excess radiation.

Elekta Synergy and Elekta Harmony are the default entry-level configurations. It is intended to be used for single or multiple fractions using standard dose fractionation, hyperfractionation, and hypofractionation in all areas of the body where such treatment is indicated.

Elekta Infinity and Elekta Harmony Pro are the default mid-level configuration. It is intended to be used for single or multiple fractions using standard dose fractionation, hyperfractionation, hypofractionation and stereotactic delivery (stereotactic body radiation therapy – SBRT; stereotactic ablative radiotherapy – SABR) in all areas of the body where such treatment is indicated.

Versa HD and Elekta Evo are the default high-level configuration. It is intended to be used for single or multiple fractions using standard fractionation, hyperfractionation, hypofractionation and stereotactic delivery (stereotactic body radiation therapy – SBRT; stereotactic ablative radiotherapy – SABR; stereotactic radio surgery - SRS) in all areas of the body where such treatment is indicated and for the treatment of functional disorders, such as trigeminal neuralgia.

The EMLA is indicated for the delivery of curative and palliative intent EBRT to Adult and Pediatric patients with primary benign and malignant tumor and metastasis (or secondaries) anywhere in the body.

The Elekta Medical Linear Accelerator (EMLA) is an external beam, image guided Radiation Therapy device to assist a licensed practitioner in the delivery of ionizing radiation to a defined target volume. The system is located in a radiation-shielded treatment room and consists of several sub-systems, such as, the electron accelerator, beam shaping, imaging, computerized control systems and a treatment table to support the patient with accessories for patient positioning and set-up to deliver therapeutic treatments.

The EMLA is equipped with a MV portal imaging sub-system, i.e. iViewGT, and an optional kV imaging sub-system, i.e. XVI. The table is capable of linear and rotational movements.

The user interface controlling devices are located partly in the treatment room and partly in the control room.

The EMLA is made available in the following models: Elekta Synergy, Elekta Harmony, Elekta Infinity, Elekta Harmony Pro, Versa HD, Elekta Evo. The major differences are described in section VII.

The provided FDA 510(k) clearance letter and summary for the Elekta Medical Linear Accelerator (EMLA) describes performance testing for differences between the subject devices (new EMLA models) and the predicate devices (older EMLA models, K210500). The primary focus of the performance testing detailed in the summary is related to improvements in CBCT image quality and reconstruction.

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

1. Table of acceptance criteria and the reported device performance

The document does not explicitly present a table of quantitative acceptance criteria alongside corresponding reported device performance values. Instead, it describes general improvements and conformance to standards. The acceptance is implicitly based on meeting or exceeding the predicate device's performance, especially for the high-definition (HD) reconstruction.

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

• FDK based reconstruction function (Image Quality Evaluation): "acquired image quality phantom data" - Specific sample size not provided, likely laboratory phantom data.
• FDK based reconstruction function (Image Registration Accuracy): "phantom data and CT reference data" - Specific sample size not provided, likely laboratory phantom data.
• HD Reconstruction vs. FDK (Image quality comparison - clinical data sets): A total of 124 different clinical data sets.
• HD Reconstruction vs. FDK (Image registration accuracy - clinical patient CBCT projection data sets): A total of 13 different clinical patient CBCT projection data sets.
• HD Reconstruction vs. FDK (Clinical image quality evaluation - clinical patient data): Specific number of patients/data sets not explicitly stated, but clinical patient data was used for qualitative comparison.

Data Provenance: The document does not explicitly state the country of origin for the clinical data or explicitly state whether it was retrospective or prospective. Given it's clinical data sets for evaluating reconstruction, it's highly likely to be retrospective data collected from clinical operations.

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

The document states:

• For the FDK based reconstruction: "reconstruct a CBCT volume image which is suitable for visualizing anatomies to enable certain clinical judgment" - this implies expert judgment in assessment, but does not specify the number or qualifications.
• For the HD Reconstruction: "suitable for visualizing the pelvic anatomies to enable certain clinical judgment".
• "A clinical image quality evaluation was performed between HD Reconstruction function and FDK based reconstruction function, using clinical patient data, where user qualitatively compared image quality between the predicate device and the subject device and reported improved image quality." - This indicates qualitative evaluation by "user", but the number and specific qualifications (e.g., radiologist with X years of experience) are not defined. The document also mentions "Formal validation of the clinical workflows has been performed by competent and professionally qualified personnel" but does not specify them in relation to ground truth establishment for the test set.

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

The document does not describe a formal adjudication method (like 2+1 or 3+1 consensus) for establishing ground truth or evaluating the clinical image quality. It generally refers to qualitative comparison by "user" or "competent and professionally qualified personnel".

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 mentions "A clinical image quality evaluation was performed between HD Reconstruction function and FDK based reconstruction function, using clinical patient data, where user qualitatively compared image quality between the predicate device and the subject device and reported improved image quality." It also notes "A clinical survey shows a preference towards the HD Reconstruction of the subject device over the predicate."

This suggests a form of reader study or survey was conducted, where users (human readers) compare images from the legacy FDK method (without the new AI-ML component) to the HD Reconstruction (which "includes an AI-ML based component to estimate the scatter"). However, it's not explicitly labeled as a "multi reader multi case (MRMC) comparative effectiveness study" in the formal sense, and no quantitative effect size of improvement for human readers is provided. The improvement is described qualitatively (e.g., "improved image quality," "better uniformity and HU accuracy," "preference").

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

Yes, standalone algorithm performance evaluation was done for the FDK and HD reconstruction functions. This is evident from:

• "Image quality evaluation was performed for the FDK based reconstruction function, using acquired image quality phantom data, to evaluate uniformity, spatial resolution, low contrast visibility, and geometric accuracy in accordance with IEC 60601-2-68;"
• "Image quality comparison was performed between HD Reconstruction function and FDK based reconstruction function, using data acquired on phantom data, to evaluate uniformity, spatial resolution, low contrast visibility, and geometric accuracy in accordance with IEC 60601-2-68."
• "Image quality comparison was performed between HD Reconstruction function and FDK based reconstruction function, using a total of 124 different clinical data sets, to evaluate uniformity, Hounsfield Unit consistency, signal-to-noise ratio, contrast-to-noise ratio, and contrast consistency."

These evaluations measure the intrinsic performance of the reconstruction algorithms without explicit human interaction beyond setting up the evaluation.

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

The types of ground truth used include:

• Phantom Data: For evaluating image quality metrics (uniformity, spatial resolution, low contrast visibility, geometric accuracy) and image registration accuracy (against CT reference data). Phantoms often have known properties or are designed to allow for quantitative measurement of image accuracy.
• CT Reference Data: Used for comparing image registration accuracy. CT imaging is considered a high-fidelity reference.
• Clinical Patient Data: Used for comparing various image quality metrics (Hounsfield Unit consistency, signal-to-noise ratio, contrast-to-noise ratio, contrast consistency) and for qualitative "user" comparison of image quality. The "ground truth" for clinical image quality comparison would effectively be the subjective assessment of the users or experts performing the comparison against each other, and against the clinical utility standards.
• Simulated Monte Carlo data: Used to train the neural network component of the HD reconstruction that estimates scatter. This implies a simulated ground truth for scatter estimation.

8. The sample size for the training set

The document states: "It includes an AI-ML based component to estimate the scatter to enable its automatic removal from the projection images acquired by the imager ahead of the volume reconstruction. Simulated Monte Carlo data is used to train the network."

The specific sample size for the training set (i.e., the amount of simulated Monte Carlo data) for the AI-ML component is not provided.

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

The ground truth for the training set of the AI-ML component was established through Simulated Monte Carlo data. Monte Carlo simulations are a computational method that can model the physical interactions of radiation with matter, providing a "ground truth" for scatter estimation in this context.

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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).

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ChartCheck is intended to assist with the quality assessment of radiotherapy treatment plans and on treatment review.

The ChartCheck device is software that enables trained radiation oncology personnel to perform quality assessments of treatment plans and treatment chart reviews utilizing plan, treatment, imaging, as well as documentation data obtained from an Oncology Information System database(s).

ChartCheck contains 3 main components:

a. An agent service that is configured by the user to monitor an Oncology Information System (OIS) database. The agent watches for new treatment plans, treatment records, documentation, and images. The agent uploads data to a checking service.

b. A checking service that compares the treatment records to the treatment plan and calculates check states as new records are uploaded from the agent. The checking service processes on-treatment imaging data and interfaces with outside software platforms for dose calculation activities.

c. A web application accessed via a web browser that contains several components.
i. Chart checking mode, which allows a medical physicist to review treatment records and check state results, record chart check comments, and mark the chart check as approved.
ii. An image viewer that allows a medical physicist to review on-treatment imaging, on-treatment dose calculation results, and perform deformable registration editing.
iii. Settings mode, which allows an administrator to set check state colors, configure settings, define check state templates, set up check alerts, documentation generation, and billing settings.

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ART-Plan+'s indicated target population is cancer patients for whom radiotherapy treatment has been prescribed. In this population, any patient for whom relevant modality imaging data is available.

ART-Plan+'s includes several modules:

SmartPlan which allows automatic generation of radiotherapy treatment plan that the users import into their own Treatment Planning System (TPS) for the dose calculation, review and approval. This module is available for supported prescriptions for prostate only.

Annotate which allows automatic generation of contours for organs at risk, lymph nodes and tumors, based on medical practices, on medical images such as CT and MR images

AdaptBox which allows generation of synthetic-CT from CBCT images, dose computation on CT images for external beam irradiation with photon beams and assisted CBCT-based off-line adaptation decision-making for the following anatomies:

• Head & Neck
• Breast / Thorax
• Pelvis (male)

ART-Plan+ is not intended to be used for patients less than 18 years of age.

ART-Plan is a software platform allowing contour regions of interest on 3D images, to provide an automatic treatment plan and to help in the decision for the need for replanning based on contours and doses on daily images. It includes several modules:

Home: tasks and patient monitoring

Annotate including TumorBox: contouring of regions of interest

Smartplan: creation of an automatic treatment plan based on a planning CT and a RTSS

AdaptBox: helping tool to decide if a replanning is necessary. For this purpose, the module allows the user to generate a synthetic-CT from a CBCT image, to auto-delineate regions of interest on the synthetic-CT, to compute the dose on both planning CT and synthetic-CT and then define if there is a need for replanning by comparing volume and dose metrics computed on both images and over the course of the treatment. Those metrics are defined by the user.

Administration and Settings: preferences management, user account management, etc.

About: information about the software and its use, as well as contact details.

Annotate, TumorBox, Smartplan and AdaptBox are partially based on a batch mode, which allows the user to launch the operations of autocontouring and autoplanning without having to use the interface or the viewers. In that way, the software is completely integrated into the radiotherapy workflow and offer to the user a maximum of flexibility.

Annotate which allows automatic generation of contours for organs at risk (OARs), lymph nodes (LNs) and tumors, based on medical practices, on medical images such as CT and MR images:

OARs and LNs:

• Head and neck (on CT images)
• Thorax/breast (on CT images)
• Abdomen (on CT and male on MR images)
• Pelvis male (on CT and MR images)
• Pelvis female (on CT images)
• Brain (on CT images and MR images)

Tumor:

• Brain (on MR images)

SmartPlan which allows automatic generation of radiotherapy treatment plan that the users import into their own Treatment Planning System (TPS) for the dose calculation, review and approval. This module is available for supported prescriptions for prostate only.

AdaptBox which allows generation of synthetic-CT from CBCT images, dose computation on CT images for external beam irradiation with photon beams and assisted CBCT-based off-line adaptation decision-making for the following anatomies:

• Head & Neck
• Breast / Thorax
• Pelvis (male)

Here's a breakdown of the acceptance criteria and study details for ART-Plan+ (v3.1.0) based on the provided FDA 510(k) clearance letter:

Acceptance Criteria and Reported Device Performance

The ART-Plan+ device consists of three main modules: Annotate, SmartPlan, and AdaptBox. Each module has its own set of acceptance criteria.

Note: The document provides acceptance criteria and implicitly states that "all tests passed their respective acceptance criteria, thus showing ART-Plan + v3.1.0 clinical acceptability." However, it does not provide specific numerical reported device performance for each criterion. It only states that the criteria were met.

Annotate Module Performance Criteria

SmartPlan Module Performance Criteria

AdaptBox Module Performance Criteria

Study Details

1. Sample Sizes for the Test Set and Data Provenance

The document describes the test sets for each module and the overall data provenance:

• Overall Test Dataset: Total of 2040 patients (1413 EU patients and 627 US patients), representing 31% US data.
• Annotate Module: Total of 1844 patients (1254 EU patients and 590 US patients).
  • Provenance: Retrospective, worldwide population receiving radiotherapy treatments, with a specific effort to include US data (31% overall).
  • Minimum sample sizes for specific tests:
    • Non-regression testing (autosegmentation on CT/MR, or synthetic-CT from CBCT): Minimum 24 patients.
    • Qualitative evaluation of autosegmentation: Minimum 18 patients.
    • Quantitative evaluation of autosegmentation: Minimum 24 patients.
• SmartPlan Module: Total of 35 patients (25 EU patients and 10 US patients).
  • Provenance: Retrospective, worldwide population receiving radiotherapy treatments, with a specific effort to include US data.
  • Minimum sample size for quantitative and qualitative evaluation: Minimum 20 patients.
• AdaptBox Module: Total of 161 patients (134 EU patients and 27 US patients).
  • Provenance: Retrospective, worldwide population receiving radiotherapy treatments, with a specific effort to include US data. An independent dataset composed only of US patients was also used for quantitative validation of synthetic CT.
  • Minimum sample sizes for specific tests:
    • Dosimetric evaluations of synthetic CT: Minimum 15 patients.
    • Quantitative validation of synthetic CT: Minimum 15 patients (plus an independent US dataset).
    • Qualitative validation of deformation of planning CT: Minimum 10 patients.
    • Qualitative validation of deformable propagation of contours: Minimum 10 patients.

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

The document refers to "medical experts" and "clinicians" for qualitative evaluations and for performing manual contours. However, it does not specify the exact number of experts used for ground truth establishment for each test set or their specific qualifications (e.g., "Radiologist with 10 years of experience"). It only mentions that evaluations were performed by medical experts.

3. Adjudication Method for the Test Set

The document does not explicitly state an adjudication method like "2+1" or "3+1" for creating the ground truth or resolving disagreements among experts. For qualitative evaluations, it describes a rating scale (A, B, C) and acceptance based on a percentage of A+B ratings, implying individual expert review results were aggregated. For quantitative validations, ground truth seems to be established by comparison with "manual contours performed by medical experts" or "direct comparison with manual plans," but the process for defining these manual references is not detailed in terms of adjudication.

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

The document does not mention a Multi-Reader Multi-Case (MRMC) comparative effectiveness study to evaluate how human readers improve with AI vs. without AI assistance. The evaluations focus on the standalone performance of the AI modules or the clinical acceptability of outputs generated by the AI (e.g., auto-segmentations, auto-plans, synthetic CTs).

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

Yes, the studies described are primarily standalone (algorithm only) performance evaluations. The modules (Annotate, SmartPlan, AdaptBox) are tested for their ability to generate contours, treatment plans, or synthetic CTs, and these outputs are then compared against ground truth or evaluated for clinical acceptability by experts. While experts review the outputs for clinical acceptability, this is an evaluation of the algorithm's output, not a comparative study of human performance with and without AI assistance. The document states, "all the steps of the workflow where ART-Plan is involved have been tested independently," emphasizing the standalone nature of the module testing.

6. Type of Ground Truth Used

The ground truth varied depending on the module and test:

• Expert Consensus / Manual Delineation:
  • For Annotate's quantitative non-regression and quantitative evaluation, the ground truth was "manual contours performed by medical experts."
  • For SmartPlan's quantitative evaluation, the ground truth was "manual plans."
• Qualitative Expert Review:
  • For Annotate, SmartPlan, and AdaptBox qualitative evaluations, the ground truth was established by "medical experts" or "clinicians" assessing the clinical acceptability of the device's output against defined scales (A, B, C).
• Comparison to Standard Imaging/Analysis:
  • For AdaptBox's dosimetric evaluations, the synthetic CT performance was compared to "standard CT."
  • For AdaptBox's quantitative validation of synthetic CTs, a "direct comparison of anatomy and geometry with the associated CBCT" was performed.

7. Sample Size for the Training Set

The document does not specify the sample size for the training set for any of the modules. It only discusses the test set sizes.

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

The document briefly mentions "retraining or algorithm improvement" for existing structures and new structures for autosegmentation, but it does not describe how the ground truth for the training set was established. It only focuses on the validation of the new version's performance using dedicated test sets with specific ground truth methods. It implies that the underlying AI models (deep learning neural networks) were trained, but the details of that training process, including ground truth establishment, are not provided in this 510(k) summary.

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RayStation is a software system for radiation therapy and medical oncology. Based on user input, RayStation proposes treatment plans. After a proposed treatment plan is reviewed and approved by authorized intended users, RayStation may also be used to administer treatments.

The system functionality can be configured based on user needs.

RayStation is a software system for radiation therapy and medical oncology. Based on user input, RayStation proposes treatment plans. After a proposed treatment plan is reviewed and approved by authorized intended users, RayStation may also be used to administer treatments.

The system functionality can be configured based on user needs.

RayStation consists of multiple applications:

• The main RayStation application is used for treatment planning.
• The RayPhysics application is used for commissioning of treatment machines to make them available for treatment planning and used for commissioning of imaging systems.
• The RayTreat application is used for sending plans to treatment delivery devices for treatment and receiving records of performed treatments.

The device to be marketed, RayStation 2024A SP3, adds the RayTreat application compared with last cleared version, the predicate RayStation 2024A SP3 (without RayTreat), K240398.

The RayTreat application was previously cleared with RayStation 11B, K220141. Since then some RayTreat functions have been changed:

• RayTreat is now session focused
• Usability improvements
• Bug fixes

The RayStation applications are built on a software platform, containing the radiotherapy domain model and providing GUI, optimization, dose calculation and storage services. The platform uses three Microsoft SQL databases for persistent storage of the patient, machine and clinic settings data.

As a treatment planning system, RayStation aims to be an extensive software toolbox for generating and evaluating various types of radiotherapy treatment plans. RayStation supports a wide variety of radiotherapy treatment techniques and features an extensive range of tools for manual or semi-automatic treatment planning.

The RayStation applications are divided into modules, which are activated through licensing.

The RayTreat application
RayTreat manages treatment delivery. An approved plan can be assigned to fractions in a treatment course and sent to the treatment delivery device. Treatment records from the treatment delivery device are recorded and sent to RayCarePACS.

Note that all real-time monitoring of actual delivery is handled by treatment delivery device software, not by RayStation.

Scientific concepts that form the basis for the device and significant performance characteristics:

RayStation is a stand-alone software medical device intended for radiation therapy. Input to the device is patient, disease and treatment unit information, output from the device is one or more treatment plans. The treatment plans include treatment unit parameter settings for optimal beam arrangements, energies, field sizes, and ultimately fluence patterns to produce as safe and effective radiation dose distribution as the predicate.

The scientific concepts of a treatment planning system are patient and beam modeling, and algorithms for dose calculation and plan parameter optimization.

The patient model is a computerized representation of the patient tissue and densities, identifying the target regions and particular organs at risk. The model is based on medical images of the patient and must have the desired level of accuracy. Likewise, the beam modeling is a computerized representation of the treatment unit, defined by fluence type, energy distribution, machine specific geometry, and beam modifiers such as MLC, flattening filters, wedges etc. The algorithms for dose calculation and plan parameter optimization must take into account all geometries and materials that affect irradiation transport through the treatment unit and the patient. The optimization algorithm iterates treatment plan parameters until the desired treatment plan and dose distribution have been obtained. Also here, all steps must be done to the desired level of accuracy.

Significant physical characteristics of the device, material used, and physical properties:

The device is a standalone software medical device. It has no physical properties or materials. The device design information can be found in the subsection above "Device design information".

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ClearCalc is intended to assist radiation treatment planners in determining if their treatment planning calculations are accurate using an independent Monitor Unit (MU) and dose calculation algorithm.

The ClearCalc Model RADCA V2.6 device is software that uses treatment data, image data, and structure set data obtained from a supported Treatment Planning System and Application Programming interfaces to perform a dose and/or monitor unit (MU) calculation on the incoming treatment planning parameters. It is designed to assist radiation treatment planners in determining if their treatment planning calculations are accurate using an independent Monitor Unit (MU) and dose calculation algorithm.

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IDENTIFY is indicated for adult patients undergoing radiotherapy treatment simulation and/or delivery. IDENTIFY is indicated for positioning of patients, and for monitoring patient motion including respiratory patterns. It allows for data output to radiotherapy devices to synchronize image acquisition or treatment delivery with the acquired motion information.

IDENTIFY uses surface guidance technology to monitor patient motion during radiotherapy treatment simulation and delivery. Its high precision SGRT cameras support:

• Positioning of the patient for treatment delivery
• Monitoring of the patient position during treatment delivery
• Respiratory motion management during simulation and treatment delivery

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The primary function of ARTAssistant is to facilitate image processing with image registration and synthetic CT (sCT) generation in adaptive radiation therapy. This enables users to meticulously design ART plans based on the processed images.

ARTAssistant, is a standalone software which is positioned as an adaptive radiotherapy auxiliary system, aiming to provide a complete solution to assist the implementation of adaptive radiotherapy, helping hospitals to implement adaptive radiotherapy on ordinary image-guided accelerators based on the current situation. This system is mainly used to assist in the image processing of online adaptive radiotherapy, thereby helping users complete the design of the daily adaptive radiotherapy plan based on the processed images.

The product has three main functions on image processing:

1. Automatic registration: rigid and deformable registration, and
2. Image conversion: generation of synthetic CT from CBCT or MR, and
3. Image contouring: it can manual contour organs-at-risk, in head and neck, thorax, abdomen and pelvis (for both male and female) areas assisted contouring tools.

It also has the following general functions:

• Receive, add/edit/delete, transmit, input/export medical images and DICOM data;
• Patient management;
• Review of processed images.

Here's an analysis of the ARTAssistant device, focusing on its acceptance criteria and the study that proves it meets those criteria, based on the provided FDA 510(k) clearance letter:

There is no specific table of acceptance criteria or reported device performance for ARTAssistant directly included in the provided 510(k) summary. The summary primarily focuses on comparing ARTAssistant's technological characteristics to predicate and reference devices and describes the performance tests conducted rather than explicit pass/fail criteria or quantitative results against those criteria.

However, based on the performance test descriptions, we can infer the intent of the acceptance criteria and how the device performance was evaluated.

Inferred Acceptance Criteria and Reported Device Performance

Study Details:

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

• Automatic Rigid & Deformable Registration Functions:
  • Sample Size: Not explicitly stated, but implies a collection of "multi-modality image sets from different patients." The count of sets/patients is not provided.
  • Data Provenance: All fixed and moving images were generated in healthcare institutions in the U.S. Retrospective or prospective is not specified, but typically, such datasets are retrospective.
• Image Conversion Function:
  • Sample Size: 247 testing image sets.
  • Data Provenance: All test images were generated in the U.S. The data provenance is retrospective.
  • Patient Demographics: 57% male, 43% female. Ages: 21-40 (13%), 41-60 (44.1%), 61-80 (36.8%), 81-100 (6.1%). Race: 78% White, 12% Black or African American, 10% Other.
  • Cancer Types: Covers 6 cancer types (Intracranial tumor, nasopharyngeal carcinoma, esophagus cancer, lung cancer, liver cancer, cervical cancer) with specific distributions for both MR/CT and CBCT/CT test datasets.
  • Scanner Models:
    • CT: GE (28.3%), Philips (41.7%), Siemens (30%)
    • MR: GE (21.6%), Philips (56.9%), Siemens (21.6%)
    • CBCT: Varian (58.8%), Elekta (41.2%)
  • Slice Thicknesses: Distributed as 1mm (19%), 2mm (22.8%), 2.5mm (17.4%), 3mm (17%), 5mm (23.8%).

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

• The document does not explicitly state the number of experts or their qualifications used to establish ground truth for the test set.
• For the Image Conversion Dosimetric Accuracy, the AAPM TG-119 method is mentioned, which implies established phantom-based criteria or expert-derived dose distributions as a reference.
• For the Image Conversion Anatomic/Geometric Accuracy (Dice coefficient), the "segmentation results of each ROI on CBCT/MR" were compared, implying these "true" segmentations would likely have been established by qualified medical professionals, but this is not confirmed.

3. Adjudication Method for the Test Set:

• The document does not explicitly state an adjudication method (such as 2+1 or 3+1) for the test set. The evaluation methods described (NMI, HD, Gamma Pass Rate, Dice coefficient) are quantitative metrics compared against either a predicate device's output or established physical/dosimetric accuracy standards.

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

• No, an MRMC comparative effectiveness study was not explicitly mentioned or performed.
• The performance tests focused on the algorithm's standalone performance in comparison to either a predicate device's algorithm or established accuracy standards, not on how human readers improve with AI assistance.

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

• Yes, a standalone performance evaluation was conducted. The described "Performance Test Report on Rigid Registration Function," "Performance Test Report on Deformable Registration Function," and "Performance Test Report on Image Conversion Function" all relate to the algorithm's direct output and quantitative measurements without human intervention being part of the primary performance evaluation.

6. The Type of Ground Truth Used:

• For Rigid and Deformable Registration: The ground truth for comparison was the performance metrics (NMI and HD) of the predicate device (AccuContour, K221706). This indicates a comparative ground truth rather than an absolute biological or pathological ground truth.
• For Image Conversion (Dosimetric Accuracy): The ground truth was based on the AAPM TG-119 method, implying a phantom-based or established dosimetric standard against which the sRTDose was compared to the RTDose derived from true CT.
• For Image Conversion (Anatomic/Geometric Accuracy): The ground truth was the segmentation results of ROIs on the original CBCT/MR images, against which the segmentations on the sCT images were compared using the Dice similarity coefficient. This suggests expert consensus or manually established contours on the original images as ground truth.

7. The Sample Size for the Training Set:

• For the deep learning model for image conversion: There were 560 training image sets.
• The document does not specify training set sizes for the rigid or deformable registration algorithms.

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

• For the deep learning model for image conversion: The document does not explicitly detail how the ground truth for the 560 training image sets was established. Given the nature of synthetic CT generation, the "ground truth" for training would typically involve pairs of input images (e.g., MR/CBCT) and corresponding reference CT images. This would likely be derived from clinical scans, potentially aligned and processed for model training, but the process of establishing the "correctness" of these pairs (e.g., precise anatomical alignment, image quality) is not elaborated upon.
  • Data Provenance (Training Set): The training image set source is from China.

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The PlanOne is a software system used to plan radiotherapy treatments for patients with malignant or benign diseases. PlanOne is used to plan external beam irradiation with photon and proton beams. The intended users of PlanOne shall be clinically qualified radiation therapy staff trained in using the system.

The Cosylab Treatment Planning System (PlanOne) is responsible for creating machine instructions (treatment plans) for radiotherapy. It's a complex piece of software, integrating detailed physics (dose calculation), mathematics (plan optimization) and graphical (contouring) algorithms.

The PlanOne has to import 3D image datasets of patient anatomy, usually CT images. In the first stage of the planning, the tumor and critical structures have to be identified by the user. The process is called contouring. In the second stage, the 3D image and the contours are taken along with prescription input to calculate a treatment plan. The treatment plan includes machine instructions on how to deliver radiation.

To produce an appropriate treatment plan, the PlanOne computes the expected dose distribution in the patient's anatomy, taking into account relative electron density and particle stopping material properties at specific voxels (pixels). The PlanOne also helps to navigate beam placement based on avoiding critical structures that are more sensitive to radiation in an effort to reduce collateral damage from the therapy. The PlanOne may optimize beam shape and intensity to meet the user set objectives. This may include automated, complex programming for multi-leaf collimator (MLC) leaf sequencing to shape the beam around critical structures during dose delivery. In particle therapy instead of shaping MLC, the PlanOne determines the appropriate spot placement and weight in each beam direction.

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