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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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The MOZI Treatment Planning System (MOZI TPS) is used to plan radiotherapy treatments with malignant or benign diseases. MOZI TPS is used to plan external beam irradiation with photon beams.

The proposed device, MOZI Treatment Planning System (MOZI TPS), is a standalone software which is used to plan radiotherapy treatments (RT) for patients with malignant or benign diseases. Its core functions include image processing, structure delineation, plan design, optimization and evaluation. Other functions include user login, graphical interface, system and patient management. It can provide a platform for completing the related work of the whole RT plan.

The provided text describes the performance data for the MOZI TPS device, focusing on its automatic contouring (structure delineation) feature. Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided document:

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

The primary acceptance criterion mentioned for structure delineation (automatic contouring) is based on the Mean Dice Similarity Coefficient (DSC). The study aimed to demonstrate non-inferiority compared to a reference device (AccuContour™ - K191928). While explicit thresholds for "acceptable" Mean DSC values are not given as numerical acceptance criteria in the table below, the text states "The result demonstrated that they have equivalent performance," implying that the reported DSC values met the internal non-inferiority standard set by the manufacturer against the performance of the reference device.

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

• Test Set Sample Size: 187 image sets (CT structure models).
• Data Provenance: The testing image source is from the United States. The data is retrospective, as it consists of existing CT datasets.
  • Patient demographics: 57% male, 43% female. Ages: 21-30 (0.3%), 31-50 (31%), 51-70 (51.3%), 71-100 (14.4%). Race: 78% White, 12% Black or African American, 10% Other.
  • Anatomical regions: Head and Neck (20.3%), Esophageal and Lung (Thorax, 20.3%), Gastrointestinal (Abdomen, 20.3%), Prostate (Male Pelvis, 20.3%), Female Pelvis (18.7%).
  • Scanner models: GE (28.3%), Philips (33.7%), Siemens (38%).
  • Slice thicknesses: 1mm (5.3%), 2mm (28.3%), 2.5mm (2.7%), 3mm (23%), 5mm (40.6%).

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

• Number of experts: Six
• Qualifications of experts: Clinically experienced radiation therapy physicists.

4. Adjudication method for the test set

• Adjudication method: Consensus. The ground truth was "generated manually using consensus RTOG guidelines as appropriate by six clinically experienced radiation therapy physicists." This implies that the experts agreed upon the ground truth for each case.

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

• MRMC Study: No, a multi-reader, multi-case comparative effectiveness study was not performed to assess human reader improvement with AI assistance. The study focused on the standalone performance of the AI algorithm (automatic contouring) and its comparison to a reference device.

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

• Standalone Performance: Yes, a standalone performance evaluation of the automatic segmentation algorithm was performed. The reported Mean DSC values are for the MOZI TPS device's auto-segmentation function without direct human-in-the-loop interaction during the segmentation process. The comparison to the reference device AccuContour™ (K191928) was also a standalone comparison.

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

• Type of Ground Truth: Expert consensus. The ground truth was "generated manually using consensus RTOG guidelines as appropriate by six clinically experienced radiation therapy physicists."

8. The sample size for the training set

• Training Set Sample Size: 560 image sets (CT structure models).

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

• The document states that the training image set source is from China. It does not explicitly detail the method for establishing ground truth for the training set. However, given that the ground truth for the test set was established by "clinically experienced radiation therapy physicists" using "consensus RTOG guidelines," it is highly probable that a similar methodology involving expert delineation and review was used for the training data to ensure high-quality labels for the deep learning model. The statement that "They are independent of each other" (training and testing sets) implies distinct data collection and ground truth establishment processes, but the specific details for the training set are not provided.

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AVIEW RT ACS provides deep-learning-based auto-segmented organs and generates contours in RT-DICOM format from CT images which could be used as an initial contour for the clinicians to approve and edit by the radiation oncology department for treatment planning or other professions where a segmented mask of organs is needed.

• a. Deep learning contouring from four body parts (Head & Neck, Breast, Abdomen, and Pelvis)
• b. Generates RT-DICOM structure of contoured organs
• c. Rule-based auto pre-processing
  Receive/Send/Export medical images and DICOM data
  Note that the Breast (Both right and left lung, Heart) were validated with non-contrast CT. Head & Neck (Both right and left Eyes, Brain and Mandible), Abdomen (Both right and Liver), and Pelvis (Both right and left Femur and Bladder) were validated with Contrast CT only.

The AVIEW RT ACS provides deep-learning-based auto-segmented organs and generates contours in RT-DICOM format from CT images. This software could be used by the radiation oncology department planning, or other professions where a segmented mask of organs is needed.

• Deep learning contouring: it can automatically contour the organ-at-risk (OARs) from four body parts (Head ● & Neck, Breast, Abdomen, and Pelvis)
• . Generates RT-DICOM structure of contoured organs
• . Rule-based auto pre-processing
  Receive/Send/Export medical images and DICOM data

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

Acceptance Criteria and Device Performance

The general acceptance criterion for the AVIEW RT ACS device appears to be comparable performance to a predicate device (MIM-MRT Dosimetry) in terms of segmentation accuracy, as measured by Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (HD). While explicit numerical acceptance thresholds are not stated in the provided text (e.g., "DSC must be greater than X"), the study is structured as a comparative effectiveness study. The expectation is that the AVIEW RT ACS performance should be at least equivalent to, if not better than, the predicate device.

The study's tables (Tables 1-30) consistently show the AVIEW RT ACS achieving higher average DSC values (closer to 1, indicating better overlap) and generally lower average 95% HD values (closer to 0, indicating less maximum distance between contours), across various organs, demographic groups, and scanner parameters, compared to the predicate device.

Table of Acceptance Criteria and Reported Device Performance:

Note: The tables throughout the document provide specific performance metrics for individual organs and sub-groups (race, vendors, slice thickness, kernel types). The general conclusion from these tables is that the AVIEW RT ACS consistently performs as well as or better than the predicate device across most metrics and categories.

Study Details for Acceptance Criteria Proof:

1. Sample Size Used for the Test Set: 120 cases.

   • Data Provenance: The dataset included cases from both South Korea and the USA. It was constructed with various ethnicities (White, Black, Asian, Hispanic, Latino, African, American, etc.), and from four major vendors (GE, Siemens, Toshiba, and Philips).
   • Retrospective/Prospective: Not explicitly stated, but the mention of a data set constructed for validation suggests a retrospective collection.

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

   • Number of Experts: 3 radiation oncology physicians.
   • Qualifications: All were trained by "The Korean Society for Radiation Oncology," board-certified by the "Ministry of Health and Welfare," with a range of 9-21 years of experience in radiotherapy. The experts included attending assistant professors (n=2) and professors (n=1) from three institutions.

3. Adjudication Method for the Test Set:

   • The method was a sequential editing process:
     1. One expert manually delineated the organs.
     2. The segmentation results from the first expert were then sequentially edited by the other two experts.
     3. The first expert made corrections.
     4. The result was then received by another expert who finalized the gold standard.
   • This can be considered a form of sequential consensus or collaborative review rather than a strict N+1 or M+N+1 method.

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

   • Yes, a comparative effectiveness study was done. The study directly compares the AVIEW RT ACS against a predicate device (MIM-MRT Dosimetry).
   • Effect Size of Human Readers Improvement with AI vs. Without AI Assistance:
     • The study does not measure the improvement of human readers with AI assistance. Instead, it evaluates the standalone performance of the AI device against the standalone performance of a predicate AI device, both compared to expert-generated ground truth. The "human readers" (the three experts) were used solely to create the ground truth, not to evaluate their performance with or without AI assistance.

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

   • Yes, a standalone performance study was done. The study compares the auto-segmentation results of the AVIEW RT ACS directly to the expert-derived "gold standard" and also compares it to the auto-segmentation of the predicate device. This is purely an algorithm-only evaluation.

6. The Type of Ground Truth Used:

   • Expert Consensus. The ground truth was established by three radiation oncology physicians through a sequential delineation and editing process to create a "robust gold standard."

7. The Sample Size for the Training Set:

   • Not specified within the provided text. The document refers only to the validation/test set.

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

   • Not specified within the provided text. Since the training set size and characteristics are not mentioned, neither is the method for establishing its ground truth.

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INTContour provides a machine learning-based approach for the automatic segmentation of structures including treatment targets and organs at risk to support the radiation therapy treatment planning process. INTContour is intended as an initial method to segment and contour study series; therefore, this software must be used in conjunction with an appropriate software to edit the segmentation results if necessary. It is not intended to replace a thorough review by qualified medical professionals. INTContour is developed for use by dosimetrists, medical physicists, and radiation oncologists. The currently supported anatomical regions for automatic segmentation are head and neck, thorax, abdomen, and male pelvis.

INTContour is a software-only product that uses a machine learning-based approach to perform automatic segmentation of structures in medical images, coupled with tools for visualizing the segmentation results. A library of previously contoured expert cases serves as inputs to train the machine learning algorithms, specifically, convolutional networks (CNNs), to perform automatic segmentation. The results of the automatic segmentation will be stored in the DICOM Radiotherapy Structure Set (RTSTRUCT) format, which can be sent to desired destinations via the DICOM protocol. INTContour is intended to be used by dosimetrists, medical physicists, and radiation oncologists, and serves as an initial method to segment and contour study series. It must be used in conjunction with appropriate software to edit the segmentation results if necessary. The currently supported anatomical regions for automatic segmentation are head and neck, thorax, abdomen, and male pelvis. INTContour software is intended to be deployed within a hospital's private network on a workstation with an advanced graphics processing unit (GPU) and runs as a service. A web-based interface is used to access the service and manage the transfer of data, automatic segmentation, and visualization.

The acceptance criteria for Carina Medical LLC's INTContour device, along with its reported performance and details of the study proving it meets these criteria, are outlined below based on the provided document.

1. Acceptance Criteria and Reported Device Performance

The study used two primary metrics for evaluating the segmentation performance:

• Dice Similarity Coefficient (DSC): Used for larger organs.
• 95% Hausdorff Distance (HD95): Used for smaller organs.

The acceptance criteria were defined by comparing the performance of INTContour against a predicate/reference device (Smart Segmentation – Knowledge Based Contouring and AccuContour, respectively). The criteria were that INTContour's performance should be non-inferior to the predicate/reference device.

Table 1: Acceptance Criteria and Reported Device Performance

Note: Specific numerical threshold values for Dice and HD95 were not provided in the document, only the statement that the INTContour met the non-inferiority criteria.

2. Sample Size and Data Provenance

• Test Set Sample Size: Not explicitly stated in terms of a numerical count of cases, however, the document notes: "Testing data was acquired from multiple sources than the training data that covers head and neck, thorax, abdomen, and male pelvis regions."
• Data Provenance:
  • Country of Origin: Not specified.
  • Retrospective or Prospective: Not specified, but the data was taken from "patients who went through radiation treatment," suggesting it was historical (retrospective) data.
  • Patient Characteristics: Patients with ages 18-76, both male and female (implied by "various types of cancers"), and various types of cancers were included.

3. Experts for Ground Truth (Test Set)

• Number of Experts: "At least two trained personnel."
• Qualifications of Experts: Included "dosimetrist, medical physicist and/or radiation oncologist." Specific years of experience are not mentioned.

4. Adjudication Method (Test Set)

• The ground truth was performed by "at least two trained personnel... to minimize human bias in segmentation." This implies a consensus approach. However, the exact adjudication method (e.g., 2+1, 3+1, simple average, majority vote) is not explicitly detailed beyond "at least two trained personnel." It is not 'none' as there was a process involving multiple experts.

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

• No Multi-Reader Multi-Case (MRMC) comparative effectiveness study was performed or described. The study focused on the performance of the algorithm itself (standalone) and its non-inferiority compared to predicate devices, rather than measuring how human readers improve with AI assistance.

6. Standalone Performance Study

• Yes, a standalone (algorithm only without human-in-the-loop performance) study was conducted. The performance data section directly compares the "calculated metrics of INTContour against the predicate/reference device" using Dice and HD95, which are metrics for automated segmentation, not human-AI team performance.

7. Type of Ground Truth Used

• The ground truth for the test set was established through expert consensus based on manual segmentation by qualified medical professionals. Specifically, "Ground truth was performed by at least two trained personnel including dosimetrist, medical physicist and/or radiation oncologist."

8. Training Set Sample Size

• The exact sample size for the training set is not explicitly stated. The document mentions, "A library of previously contoured expert cases serves as inputs to train the machine learning algorithms, specifically, convolutional networks (CNNs)."

9. How Ground Truth for Training Set Was Established

• The ground truth for the training set was established from a "library of previously contoured expert cases." This implies manual contouring performed by experts, similar to the test set, though specific details about the number of experts or adjudication for the training set ground truth are not provided.

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