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The MEVION S250-FIT is intended to deliver proton radiation treatment to patients with localized tumors or any other conditions susceptible to treatment by radiation. When the patient is in the fully seated position, the MEVION S250-FIT is indicated for treatment of patients with localized tumors and other conditions susceptible to treatment by radiation in the sites above the mid-chest or carina.

The S250-FIT Proton Beam Radiation Therapy Device is a proton beam radiation therapy system that provides a therapeutic proton beam for clinical treatment. It is designed to deliver a proton beam with the prescribed dose and three-dimensional dose distribution to the prescribed patient treatment site. The MEVION S250-FIT delivers radiation via a pencil beam (spot) scanning modality. In order to reach a target depth of 32cm in the patient, the accelerator is designed to produce a 230MeV beam.

This submission incorporates an optional subsystem for 2D image verification using oblique x-rays to verify patient alignment prior to beam delivery.

The S250-FIT is comprised of the following subsystems:

• Beam Generation System – generates the beam and directs it to the beam delivery system.
• Beam Delivery System – ensures that the therapeutic prescription parameters are properly delivered.
• Hardwired Safety System (HSS) – provides for system and beam delivery interlocking without the use of software
• Patient Positioning System – The Marie Device from Leo Cancer Care (K250970) allows for accurate and efficient positioning of the patient in a seated or perched position for treatment using an Upright Patient Positioner and 3D CT Scanner for Treatment Planning and Patient Registration.
• Structural Support/Alignment System – supports the beam generation and delivery systems and allows the fixed beam delivery to the single point in space (i.e., the Isocenter)
• System Software – controls the above subsystems (except the HSS) and provides interfaces to the system for the end-user.
• Verification Imaging Subsystem (Optional) – 2D Oblique X-ray System that provides a secondary means of verification for patient alignment to the end-user. A separate software package, Verity is used to generate a 3D reconstruction and displays images for alignment.

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The AlignRT Plus system is indicated for:

• Tracking respiratory motion throughout the simulation process to facilitate subsequent 4DCT reconstruction and coaching the patient in breathing techniques required for Deep-Inspiration Breath Hold (DIBH).
• Verification of patient identity for their radiation treatment session.
• Positioning and monitoring of patients during radiation delivery, relative to the setup isocenter and/or the prescribed treatment isocenter.
• Withholding the beam automatically during radiation delivery, as well as gating the beam based on the patient's respiratory motion.
• Performing quality assurance on MV, kV imagers, room lasers, and the treatment couch.
• Visualizing the Cherenkov signal associated with the radiation beam on entry and exit from the patient.
• Passing and receiving information to/from other systems associated with the radiotherapy treatment.

The AlignRT Plus system (K233622) is a combination of the devices AlignRT, AlignRT InBore, AlignRT Offline, AlignRT PDW, DoseRT, GateCT, GateRT and SimRT.

AlignRT Plus is a video-based three-dimensional (3D) surface imaging system used to monitor the patient's position in 3D before and during radiotherapy treatment. During each treatment session the patient's position is compared to the reference surface and offsets are displayed to the user. The system can be used to track patients' motion during tumour localization in the CT scanner in order to facilitate subsequent 4D CT reconstruction. The system can be used to track the breath hold level consistency throughout the simulation process. The system can also be used to track patient motion during treatment delivery for radiation therapy procedures and hold the beam when the patient is not in position. Use of optional accessories allows the user to:

• monitor the patient's position inside bore-based linacs; and/or
• to view the Cherenkov light emitted by the radiation beam as it enters and exits the patient's skin; and/or
• verify the patient's identity.

The system is non-invasive, does not require the use of body markers and produces no irradiation during the imaging process. The system mainly consists of advanced software, 3D cameras (≤6 cameras) and calibration tools. Each camera pod monitoring the patient's position will, however, project a pattern on the patient to acquire a 3D image of the patient.

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syngo.via RT Image Suite VC10 is a 3D and 4D image visualization, multimodality manipulation and contouring tool that helps the preparation of treatments such as, but not limited to those performed with radiation (for example, Brachytherapy, Particle Therapy, External Beam Radiation Therapy).

It provides tools to view existing contours, create, edit, modify, copy contours of regions of the body, such as but not limited to, skin outline, targets and organs-at-risk. It also provides functionalities to create simple geometric treatment plans. Contours, images and treatment plans can subsequently be exported to a Treatment Planning System.

The software combines the following digital image processing and visualization tools (not all of them might be available at each customer):

• Multimodality viewing and contouring of anatomical, functional, and multi parametric images such as but not limited to CT, PET, PET/CT, MRI, Linac CBCT images
• Multiplanar reconstruction (MPR) thin/thick, minimum intensity projection (MIP), volume rendering technique (VRT)
• Freehand and semi-automatic contouring of regions-of-interest on any orientation including oblique
• Automated Contouring on CT and MR images, including known (diagnosed) brain metastases
• Creation of contours on images supported by the application without prior assignment of a planning CT or planning MR
• Manual and semi-automatic registration using rigid and deformable registration
• Supports the user in comparing, contouring, and adapting contours based on datasets acquired with different imaging modalities and at different time points
• Supports multi-modality image fusion
• Visualization and contouring of moving tumors and organs
• Management of points of interest including but not limited to the isocenter
• Creation of simple geometric treatment plans
• Generation of a synthetic CT based on pre-defined MR acquisitions

syngo.via RT Image Suite VC10 is an image analysis and radiation therapy preparation software that provides multimodality image viewing, registration, segmentation, synthetic CT generation, and patient marking workflows. Within the Medical device syngo.via RT Image Suite VC10 the name 'CT Sim&Go' is used. CT Sim&GO is the name used for syngo.via RT Image Suite VC10 when it is deployed in the CT scanner workflow. CT Sim&GO is not a device of its own. CT Sim&GO is a subset of syngo.via RT Image Suite VC10 functionalities that can be accessed from the CT scanner workplace. More precisely it comprises the Patient Marking and Beam Placement modules of syngo.via RT Image Suite VC10. The current submission includes modifications affecting the following functionalities:

MR Autocontouring: The AI‑based MR autocontouring functionality has been introduced to include segmentation of previously diagnosed brain metastases, in addition to MR‑based segmentation of brain OARs and male pelvis OARs. The metastasis‑specific model is restricted to identification of metastases already diagnosed by a clinician, and the software does not provide diagnostic capability or detect new or unknown lesions. These contours remain fully editable by the user.

CT Autocontouring: syngo.via RT Image Suite VC10 includes 29 new organs and structures for deep‑learning–based CT autocontouring. The underlying DL architecture is unchanged; however, additional segmentation guidelines and organ‑coverage expansion were incorporated.

Improvements to Isocenter Definition & Patient Marking: The patient‑marking workflow includes semi‑automated isocenter estimation for breast (originally cleared under K192065) and vertebral regions (originally cleared under K220783). New capability to send the coordinates from a movable laser positioning system to the syngo.via RT Image Suite VC10, enabling a workflow of setting isocenter directly on the skin and transferring it to the planning images.

Synthetic CT: The synthetic CT feature was updated to include a new 3D deep‑learning–based algorithm for brain and pelvis, replacing the prior 2D model and improving HU accuracy and geometric fidelity.

Here's a breakdown of the acceptance criteria and study details for the syngo.via RT Image Suite VC10 device, based on the provided 510(k) summary:

Acceptance Criteria and Device Performance for syngo.via RT Image Suite VC10

1. Table of Acceptance Criteria and Reported Device Performance

The device includes several AI-based auto-contouring functionalities and a synthetic CT feature. The acceptance criteria and reported performance vary by feature.

1.1 CT Auto-Contouring (Existing Organs)

1.2 CT Auto-Contouring (New Organs)

| Structure Group | Acceptance Criteria | Reported Device Performance (Mean DICE / Mean ASSD) |
| :------------------------ | :------------------------------- |
| New Head and Neck OARs | 1. Statistical non-inferiority of the Dice score compared with a reference device (lower 95th percentile confidence bound > mean reference performance - 10% margin). 2. Statistical non-inferiority of the ASSD score compared with a reference device (upper 95th percentile confidence bound < mean reference performance + Std.Dev). 3. Average user evaluation of 3 or higher (on a 4-point scale for time savings). | DICE: 0.68 (Lacrimal Gland Right) - 0.94 (Humeral Head Right) ASSD (mm): 0.7 (Humeral Head Left) - 1.15 (Lacrimal Gland Right) |
| New Thorax OARs | 1. Statistical non-inferiority of the Dice score compared with a reference device (lower 95th percentile confidence bound > mean reference performance - 10% margin). 2. Statistical non-inferiority of the ASSD score compared with a reference device (upper 95th percentile confidence bound < mean reference performance + Std.Dev). 3. Average user evaluation of 3 or higher (on a 4-point scale for time savings). | DICE: 0.28 (CA Left Circumflex) - 0.75 (N2 Station 3A) ASSD (mm): 1.19 (N1 Station 10 Left) - 5.17 (N2 Station 9 Left) |
| New Pelvis OARs | 1. Statistical non-inferiority of the Dice score compared with a reference device (lower 95th percentile confidence bound > mean reference performance - 10% margin). 2. Statistical non-inferiority of the ASSD score compared with a reference device (upper 95th percentile confidence bound < mean reference performance + Std.Dev). 3. Average user evaluation of 3 or higher (on a 4-point scale for time savings). | DICE: 0.92 (Sacrum) - 0.95 (Femoral Head Left, Hip Bone Right) ASSD (mm): 0.4 (Hip Bone Right) - 2.92 (Bowel Bag) |

1.3 CT Auto-Contouring (Subgroup Analysis for Photon-Counting CT)

1.4 MR Auto-Contouring (Brain Metastasis)

1.5 MR Auto-Contouring (Brain and Pelvis OARs)

1.6 Synthetic CT (Pelvis and Brain)

Acceptance criteria for Geometric Fidelity and HU Accuracy are mentioned but not specifically detailed in the provided text. The study states "The testing ensures the quantitative performance of the resulting synthetic CT. Analysis was performed on geometric fidelity and HU accuracy."

1.7 Semi-automated isocenter estimates of vertebrae

Acceptance criterion is "percentage of cases which need corrections" but the specific threshold is not detailed. The study mentions "Analysis was performed on percentage of cases which need corrections." and that it was cleared under K220450.

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

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

• Number of Experts: Not explicitly stated as a single number. The document mentions "an expert team" for most ground truth annotations.
• Qualifications: "expert clinicians using well-established international contouring guidelines" and "rigorous independent quality assurance reviews." For spine landmarks, "an expert team" was used. Specific specialties (e.g., radiologist, radiation oncologist) or years of experience are not provided.

4. Adjudication Method for the Test Set

The document consistently states that ground truth contours created by expert teams underwent "rigorous independent quality assessment" or "rigorous independent quality assurance reviews." This implies an adjudication process where an independent expert or team reviewed and likely corrected or confirmed the initial expert annotations. However, the specific method (e.g., 2+1, 3+1, etc.) is not detailed.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and the effect size

• MRMC Study: No, a formal MRMC comparative effectiveness study comparing human readers with AI vs. without AI assistance was not reported in this summary.
  • The user evaluation for new organs in auto-contouring (CT and MR OARs) included a "four-point scale to evaluate each contour in the context of time savings compared to contouring from scratch," which assesses user perception of usefulness but is not an MRMC comparative effectiveness study.
• Effect Size: Therefore, no effect size for human reader improvement with AI assistance is provided.

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

Yes, extensive standalone algorithm performance testing was done for all AI-based features. The metrics reported (DICE, ASSD, Hausdorff Distance, Sensitivity, False Positive Rate, HU accuracy, geometric fidelity) are all measures of the algorithm's performance without human intervention after the contour is generated. The contours are stated to be "fully editable by the user," but the reported metrics reflect the raw output of the algorithms.

7. The Type of Ground Truth Used

• CT auto-contouring, MR brain metastasis, MR brain OAR, MR pelvis OAR: "Manual ground-truth segmentations were annotated by an expert team based on well accepted international contouring guidelines, followed by a rigorous independent quality assessment." This indicates expert consensus (or at least expert-generated and quality-assured) ground truth.
• Synthetic CT: Ground truth would likely be the actual (e.g., diagnostic-quality) CT images used for comparison, with analysis performed on "geometric fidelity and HU accuracy."
• Semi-automated isocenter estimates of vertebrae: "Manual ground-truth of spine landmarks were annotated by an expert team, followed by a rigorous independent quality assessment." This is also expert consensus.

8. The Sample Size for the Training Set

The document mentions "Training datasets consisted of curated, multicenter CT and MR image collections with expert-annotated reference standards and standardized preprocessing." However, the exact sample size for the training set is not explicitly provided in this summary for any of the features. It only states that "All test datasets were independent of training datasets."

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

The ground truth for the training set was established similarly to the test set: "curated, multicenter CT and MR image collections with expert-annotated reference standards and standardized preprocessing." This implies expert annotation, consistent with how the test set ground truth was created.

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The Monaco system is used to make treatment plans for patients with prescriptions for external beam radiation therapy. The system calculates dose for photon, electron, and proton treatment plans and displays, on-screen and in hard-copy, two- or three-dimensional radiation dose distributions inside patients for given treatment plan set-ups. The Monaco product line is intended for use in radiation treatment planning. It uses generally accepted methods for:

• contouring
• image manipulation
• simulation
• image fusion
• plan optimization
• QA and plan review

The Monaco RTP System accepts patient diagnostic imaging data from CT and MR scans, and source dosimetry data, typically from a linear accelerator. The system then permits the user to display and define (contour) the target volume to be treated and critical structures which must not receive above a certain level of radiation, on these diagnostic images. Based on the prescribed dose, the user, a Dosimetrist or Medical Physicist, can then create multiple treatment scenarios involving the number, position(s) and energy of radiation beams and the use of a beam modifier (MLC, block, etc.) between the source of radiation and the patient to shape the beam. Monaco RTP system then produces a display of radiation dose distribution within the patient, indicating not only doses to the target volume but to surrounding tissue and structures. The optimal plan satisfying the prescription is then selected, one that maximizes dose to the target volume while minimizing dose to surrounding healthy volumes.

The parameters of the plan are output for later reference and for inclusion in the patient file. Monaco planning methods and modalities:

• Intensity Modulated Radiation Treatment (IMRT) planning
• Electron, photon and proton treatment planning
• Planning for dynamic delivery methods (e.g., dMLC, dynamic conformal)
• Volumetric Modulated Arc Therapy (VMAT)
• Stereotactic planning and support of cone-based stereotactic
• 3D conformal planning
• Distributed planning configurations (e.g., for conventional linac)
• Adaptive planning capabilities (e.g., for MR-Linac & conventional linac)
• Auto planning features (e.g., for conventional linac)

Monaco basic systems tools, characteristics, and functions:

• Plan review tools
• Manual and automated contouring tools (Segmentation component for MR images)
• DICOM connectivity
• Windows operating system
• Simulation
• Support for a variety of beam modifiers (e.g. MLCs, blocks, etc.)
• Standardized uptake value (SUV)
• Specialty Image Creation (MIP, MinIP, and Avg)
• Monaco dose and Monitor Unit (MU) calculation
• Dose calculation algorithms for electron, photon, proton planning

Monaco is programmed using C, C++ and C# computer programming languages. Monaco runs on Windows operating system and off-the-shelf computer server/hardware.

The provided FDA 510(k) clearance letter and summary for the Monaco RTP System (6.3) outlines the acceptance criteria and a study supporting the substantial equivalence of the new features. Here's a breakdown of the requested information:

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

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

• AI-based segmentation component:
  • Female Pelvis Intact & Hysterectomy models: 529 images (joint image set).
  • Male Pelvis model: 250 images.
  • Head & Neck model: 1862 images.
• Data Provenance: Not explicitly stated regarding country of origin or whether the data was retrospective or prospective.

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

• AI-based segmentation component: "reference contours produced by qualified users" and "clinical delineation." The exact number or specific qualifications (e.g., radiologist with X years of experience) of these experts are not specified in the provided document.

4. Adjudication method for the test set

• AI-based segmentation component: For the qualitative analysis, it states "a conclusion that the automatically generated structures provided a valuable starting point for clinical delineation." This implies human review and evaluation. However, a formal adjudication method like "2+1" or "3+1" is not explicitly mentioned.
• For other features (Auto-planning, Adaptive planning, Interoperability), reviews mention evaluation within the "clinical workflow" and determination of "suitability for clinical use," but a specific adjudication method beyond internal reviews is not detailed.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• A formal MRMC comparative effectiveness study to quantify human reader improvement with AI assistance is not mentioned in the provided text. The AI component was evaluated in a standalone manner for its segmentation accuracy, and qualitatively for its utility as a "starting point for clinical delineation."

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

• Yes, for the AI-based segmentation component, a standalone algorithm-only performance evaluation was done using metrics like Average Hausdorff Distance (AVD), DICE, and AUC.

7. The type of ground truth used

• For the AI-based segmentation component, the ground truth for the test set involved "reference contours produced by qualified users" and "clinical delineation." This implies expert consensus/delineation rather than pathology or outcomes data.
• For other features, "clinically acceptable treatment plans" and "suitable for clinical use" imply evaluation against accepted clinical standards, likely by qualified personnel.

8. The sample size for the training set

• The training set sample sizes are indicated for the AI-based segmentation models:
  • Female Pelvis Intact & Hysterectomy: 529 images (joint image set used for training).
  • Male Pelvis: 250 images (used for training).
  • Head & Neck: 1862 images (used for training).

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

• The document implies that the training data for the AI-based segmentation models would have been expertly annotated to establish the ground truth, given the mention of "reference contours produced by qualified users" for evaluation. However, the exact method for establishing ground truth for the training set is not explicitly detailed beyond this inference.

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TrueFit Bolus is indicated for and intended to be placed on the patient's skin as an accessory to attenuate and/or compensate external beam (photon or electron) radiation during prescribed radiation therapy for the treatment of cancer or other non-malignant tissue conditions for which radiation therapy is indicated.

The device is for a single patient's use only and can be reused throughout the entirety of the treatment course.

The device is designed by the radiation therapy professional using patient imaging data as input and must be verified and approved by the trained radiation therapy professional prior to use.

The device is restricted to sale by or on the order of a physician and is by prescription only.

TrueFit Bolus is a 3D printed patient-matched radiation therapy accessory that expands the application of external beam radiation therapy by providing a patient-specific fit.

Patient imaging data from the treatment planning system (TPS) are used as inputs to generate digital design of the radiation therapy bolus (TrueFit) by 3D Bolus Software Application (K213438), previously developed by Adaptiiv. The resulting output Stereolithography (STL) file is compatible with the third-party 3D printers. A TrueFit Bolus can be 3D-printed using MJF with polyamide or polyurethane, or SLA with methacrylate photopolymer resin, based on the user's preference.

The bolus is used in radiation therapy when a patient requires the total prescription dose to be delivered on or near the skin surface. The bolus acts as a tissue-equivalent material placed on the patient skin to account for the buildup region of the treatment beam.

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MI View&GO is a medical diagnostic application for viewing, manipulation, quantification, analysis and comparison of medical images with one or more time-points. MI View&GO supports functional data, such as positron emission tomography (PET) or nuclear medicine (NM), as well as anatomical datasets, such as computed tomography (CT) or magnetic resonance (MR).

MI View&GO is intended to be utilized by appropriately trained health care professionals to aid in the management of diseases associated with oncology, cardiology, neurology, and organ function. The images and results produced by MI View&GO can also be used by the physician to aid in radiotherapy treatment planning.

MI View&GO is a software-only medical device which will be delivered in conjunction with Siemens SPECT/CT and PET/CT scanners. MI View&GO software provides additional specific capabilities for handling of PET and SPECT as well as CT and MR data directly at the acquisition console.

The MI View&GO software integrates molecular imaging more efficiently in the clinical environment by providing an interface for its users to review, post-process and read medical images immediately after acquisition. The purpose of the MI View&GO is to allow the technologist and reading physician to:

• Review acquired and reconstructed images at the scanner console
• Determine that the acquired data is of sufficient quality for reading, so the patient can be released.
• Prepare images for reading
• Perform a basic read

Here's an analysis of the acceptance criteria and study detailed in the provided FDA 510(k) clearance letter for MI View&GO, structured according to your requested points:

Acceptance Criteria and Device Performance Study for MI View&GO (K254016)

1. Acceptance Criteria and Reported Device Performance

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

• Improved Lung Segmentation:
  • Sample Size: 20 patients.
  • Data Provenance:
    • Retrospective.
    • Half of the patients were new, and the other 50% were randomly selected from the predicate testing cohort.
    • 50% of patients were from the US.
    • All patients from Siemens Scanner.
• Improved PERCIST Liver Algorithm (binary liver mask input):
  • Sample Size: 20 patients.
  • Data Provenance:
    • Patients obtained from clinical partners in Europe and USA.
    • Randomly selected with stratification.
    • All subjects from Siemens Scanner.
• Improved PERCIST Liver Algorithm (Reference Region Placement & Intersection with Suspicious Uptake Masks):
  • Sample Size: 129 subjects for the "intersection with suspicious uptake masks" analysis.
  • Data Provenance: Not explicitly stated for the "reference region placement" analysis, but implied to be from the same or similar source as the 129 subjects.

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

• Improved Lung Segmentation: Not explicitly stated. The ground truth for segmentation metrics (Dice, ASSD) is typically established by manual segmentation performed by experts, but the number of experts and their qualifications are not detailed in this document.
• Improved PERCIST Liver Algorithm (Reference Region Placement):
  • Number of Experts: Two expert readers.
  • Qualifications: "Expert readers" is mentioned, but specific qualifications (e.g., radiologist 10 years experience) are not provided.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks):
  • Number of Experts: One expert reader.
  • Qualifications: "Expert reader" is mentioned; specific qualifications are not provided.

4. Adjudication Method for the Test Set

• Improved Lung Segmentation: Not explicitly mentioned. For segmentation ground truth derived from multiple experts, methods like consensus or averaging are common, but not specified here.
• Improved PERCIST Liver Algorithm (Reference Region Placement): Semi-automatic evaluation by two expert readers. The document states the subject device algorithm was compared to this "reference standard," implying this semi-automatic output was considered the ground truth. No explicit adjudication method (like 2+1) is described for resolving differences between the two experts, if they occurred.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Identified by "an expert reader." This implies a single expert's identification served as the ground truth. No adjudication mentioned.

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

• No, a formal MRMC comparative effectiveness study involving human readers with and without AI assistance is not described in this document.
• The studies conducted focus on the algorithm's performance against historical data, expert interpretations, or comparing an improved algorithm to a predicate algorithm.

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

• Yes, standalone performance studies were conducted for specific features:
  • Improved Lung Segmentation: The Dice coefficient and ASSD evaluation was a standalone algorithmic performance assessment against presumed expert-derived ground truth.
  • Improved PERCIST Liver Algorithm (binary liver mask input): The Dice coefficient and ASSD evaluation for the liver mask was a standalone algorithmic performance assessment.
  • Improved PERCIST Liver Algorithm (Reference Region Placement): The comparison of the algorithm's results to the semi-automatic evaluation by two expert readers is a standalone algorithm assessment, where the expert input constitutes the ground truth.
  • Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): This was a standalone algorithmic evaluation of how often the algorithm's PERCIST VOIs intersected suspicious uptake areas identified by an expert.

7. The Type of Ground Truth Used

• Improved Lung Segmentation: Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD, which compare algorithm output to a gold standard segmentation).
• Improved PERCIST Liver Algorithm (binary liver mask input): Likely expert consensus/manual segmentation (implied by Dice coefficient and ASSD for the liver mask).
• Improved PERCIST Liver Algorithm (Reference Region Placement): Expert semi-automatic evaluation from two expert readers. These semi-automatic outputs were treated as the reference standard.
• Improved PERCIST Liver Algorithm (Intersection with Suspicious Uptake Masks): Expert identification of suspicious tracer uptake masks by a single expert reader.

8. The Sample Size for the Training Set

• Not explicitly stated in the document. The document mentions that the lung lobe segmentation algorithm was "re-trained with additional data" and that there was "No overlap of patients between training, tuning, and test cohorts," but does not provide details on the training set's size.

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

• Not explicitly stated in the document. For machine learning models, ground truth for training data is typically established through expert labeling (e.g., manual segmentation, disease annotation), but the specifics are not provided here.

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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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