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MR Contour DL generates a Radiotherapy Structure Set (RTSS) DICOM with segmented organs at risk which can be used by trained medical professionals. It is intended to aid in radiation therapy planning by generating initial contours to accelerate workflow for radiation therapy planning. It is the responsibility of the user to verify the processed output contours and user-defined labels for each organ at risk and correct the contours/labels as needed. MR Contour DL is intended to be used with images acquired on MR scanners, in adult patients.

MR Contour DL is a post processing application intended to assist a clinician by generating contours of organ at risk (OAR) from MR images in the form of a DICOM Radiotherapy Structure Set (RTSS) series. MR Contour DL is designed to automatically contour the organs in the head/neck, and in the pelvis for Radiation Therapy (RT) planning of adult cases. The output of the MR Contour DL is intended to be used by radiotherapy (RT) practitioners after review and editing, if necessary, and confirming the accuracy of the contours for use in radiation therapy planning.

MR Contour DL uses customizable input parameters that define RTSS description, RTSS labeling, organ naming and coloring. MR Contour DL does not have a user interface of its own and can be integrated with other software and hardware platforms. MR Contour DL has the capability to transfer the input and output series to the customer desired DICOM destination(s) for review.

MR Contour DL uses deep learning segmentation algorithms that have been designed and trained specifically for the task of generating organ at risk contours from MR images. MR Contour DL is designed to contour 37 different organs or structures using the deep learning algorithms in the application processing workflow.

The input of the application is MR DICOM images in adult patients acquired from compatible MR scanners. In the user-configured profile, the user has the flexibility to choose both the covered anatomy of input scan and the specific organs for segmentation. The proposed device has been tested on GE HealthCare MR data.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided FDA 510(k) clearance letter for MR Contour DL:

1. Table of Acceptance Criteria and Reported Device Performance

Device: MR Contour DL

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EFAI HCAPSeg is a software device intended to assist trained radiation oncology professionals, including, but not limited to, radiation oncologists, medical physicists, and dosimetrists, during their clinical workflows of radiation therapy treatment planning by providing initial contours of organs at risk on non-contrast CT images. EFAI HCAPSeg is intended to be used on adult patients only.

The contours are generated by deep-learning algorithms and then transferred to radiation therapy treatment planning systems. EFAI HCAPSeg must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results generated. EFAI HCAPSeg is not intended to be used for decision making or to detect lesions.

EFAI HCAPSeg is an adjunct tool and is not intended to replace a clinician's judgment and manual contouring of the normal organs on CT. Clinicians must not use the software generated output alone without review as the primary interpretation.

EFAI RTSuite CT HCAP-Segmentation System, herein referred to as EFAI HCAPSeg, is a standalone software that is designed to be used by trained radiation oncology professionals to automatically delineate organs-at-risk (OARs) on CT images. This auto-contouring of OARs is intended to facilitate radiation therapy workflows.

The device receives CT images in DICOM format as input and automatically generates the contours of OARs, which are stored in DICOM format and in RTSTRUCT modality. The device does not offer a user interface and must be used in conjunction with a DICOM-compliant treatment planning system to review and edit results. Once data is routed to EFAI HCAPSeg, the data will be processed and no user interaction is required, nor provided.

The deployment environment is recommended to be in a local network with an existing hospital-grade IT system in place. EFAI HCAPSeg should be installed on a specialized server supporting deep learning processing. The configurations are only being operated by the manufacturer:

• Local network setting of input and output destinations;
• Presentation of labels and their color; ●
• Processed image management and output (RTSTRUCT) file management. ●

Here's a detailed breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

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ClearCheck is intended to assist radiation therapy professionals in generating and assessing the quality of radiotherapy treatment plans. ClearCheck is also intended to assist radiation treatment planners in predicting when a treatment plan might result in a collision between the treatment machine and the patient or support structures.

The ClearCheck Model RADCC V2 device is software that uses treatment data, image data, and structure set data obtained from supported Treatment Planning System and Application Programming Interfaces to present radiotherapy treatment plans in a user-friendly way for user approval of the treatment plan. The ClearCheck device (Model RADCC V2) is also intended to assist users to identify where collisions between the treatment machine and the patient or support structures may occur in a treatment plan.

It is designed to run on Windows Operating Systems. ClearCheck Model RADCC V2 performs calculations on the incoming supported treatment data. Supported Treatment Planning Systems are used by trained medical professionals to simulate radiation therapy treatments for malignant or benign diseases.

The provided text describes the acceptance criteria and study for the ClearCheck Model RADCC V2 device, which assists radiation therapy professionals in generating and assessing treatment plans, including predicting potential collisions.

Here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: The document does not explicitly state a specific numerical sample size for the test set used for the BED/EQD2 and Deformed Dose functionality validation. It mentions "all cases" for Deformed Dose and "a plan and plan sum" for BED/EQD2. This implies testing was done on an unspecified number of representative cases, but not a statistically powered cohort.
• Data Provenance: Not specified in the provided text. It does not mention the 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 Qualifications of Those Experts

• The document does not mention the use of experts to establish ground truth for the test set.
• For BED/EQD2, the ground truth was established by "values calculated by hand using the well-known BED / EQD2 formulas."
• For Deformed Dose, the ground truth was established by "known dose deformations."

4. Adjudication Method for the Test Set

• Not applicable as there is no mention of expert review or adjudication for the test set. Ground truth was established by calculation or "known" deformations.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study Was Done, If So, What Was the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance

• No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not performed. The document explicitly states: "no clinical trials were performed for ClearCheck Model RADCC V2." The device is intended to "assist radiation therapy professionals," but its impact on human reader performance was not evaluated through a clinical study.

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

• Yes, performance evaluations for the de novo functionalities (BED/EQD2 and Deformed Dose) appear to be standalone algorithm performance assessments. The device's calculations were compared against established mathematical formulas (BED/EQD2) or known deformations (Deformed Dose) without human intervention in the evaluation process.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• BED/EQD2: Ground truth was based on "values calculated by hand using the well-known BED / EQD2 formulas." This is a computational/mathematical ground truth.
• Deformed Dose: Ground truth was based on "known dose deformations." This implies a physically or computationally derived ground truth where the expected deformation results were already established.

8. The Sample Size for the Training Set

• The document does not specify a sample size for the training set. It primarily focuses on the validation of new features against calculated or known results, rather than reporting on a machine learning model's training data.

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

• The document does not provide information on how the ground truth for any training set was established. Given the nature of the device (software for calculations and collision prediction, building on predicate devices), it's possible that analytical methods and established physics/dosimetry principles form the basis, rather than a large labeled training dataset in the typical machine learning sense for image interpretation.

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