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INTDose is a software product intended to support the radiation therapy treatment planning process by providing independent dose verification through Monte Carlo simulation. INTDose is not a treatment planning system or a radiation delivery device and should only be used by trained radiation oncology personnel as a quality assurance tool.

INTDose is a software product used within a radiation therapy clinic for quality assurance and treatment plan verification. It allows clinicians to perform a second check of a radiotherapy dose generated by a treatment planning system by simulating the transport of ionizing radiation in patients using an independent Monte Carlo algorithm. INTDose is implemented such that one or more clients may communicate calculation requests to a central dose calculation server.

While INTDose operates in the field of radiation therapy, it is neither a treatment planning system nor radiation delivery device. INTDose never comes into contact with patients and cannot control treatment delivery devices or any other medical devices. It is an analysis tool to be used only by trained radiation oncology personnel for quality assurance purposes.

This FDA 510(k) clearance letter and summary describe a software product called INTDose, intended for independent dose verification in radiation therapy treatment planning. However, the provided document does not contain the specific details required to fully address your request regarding acceptance criteria and the study that proves the device meets those criteria.

Here's a breakdown of what can and cannot be answered based on the provided text:

Information NOT available in the provided text:

• A table of acceptance criteria and the reported device performance: The document states that performance was "evaluated and verified" and "established that the device meets its design requirements," but it does not provide specific quantitative acceptance criteria (e.g., dose accuracy within X%) or the results against those criteria.
• Sample sized used for the test set and the data provenance: There is no mention of the size of the test set used for verification and validation, nor the origin (country, retrospective/prospective) of the data.
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: This information is not provided.
• Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not mentioned.
• 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 device is described as "independent dose verification through Monte Carlo simulation," a quality assurance tool, and an "analysis tool," but it does not specify an AI component that would typically be evaluated with MRMC studies comparing human readers with and without AI assistance. It's a "secondary check QA software." Therefore, it's highly unlikely an MRMC study in this context would involve human readers in the typical sense of interpreting images for diagnosis.
• If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: While the nature of the device (a dose calculation software) implies independent performance, the study details of this standalone performance (what metrics were used, against what ground truth, sample size, etc.) are not provided.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The document refers to "independent Monte Carlo algorithm" and "second check of a radiotherapy dose generated by a treatment planning system," implying the "ground truth" or reference for comparison would be another established dose calculation method or physical measurement, but the exact nature or method of ground truth establishment is not detailed.
• The sample size for the training set: As a Monte Carlo simulation software, it's not explicitly stated that there's a "training set" in the machine learning sense. If there are underlying models that are trained, this information is not provided.
• How the ground truth for the training set was established: See above.

What is mentioned in the document:

• Device Type: INTDose is "software product intended to support the radiation therapy treatment planning process by providing independent dose verification through Monte Carlo simulation." It's a "Secondary Check QA Software."
• Purpose: Quality assurance tool to perform a "second check of a radiotherapy dose generated by a treatment planning system."
• Technology: Uses an "independent Monte Carlo algorithm" to simulate the transport of ionizing radiation.
• Performance Evaluation (General): "The safety and performance of INTDose has been evaluated and verified in accordance with software specifications and applicable performance standards through software verification and validation testing."
• Conclusion: "Non-clinical verification and validation test results, including simulation performance and software usability, established that the device meets its design requirements and intended use, that it is as safe and as effective as the predicate device, and that no new issues of safety and effectiveness were raised."
• Compatibility Testing: Successfully completed with specific treatment delivery machines (Accuray TomoTherapy HDA, Varian TrueBeam, Varian Clinac (21EX-Platinum), Varian Clinac (iX), Varian Halcyon). This indicates a form of performance testing, possibly verifying dose agreement or data transfer capabilities.
• Risk Management: "Potential hazards were controlled by a risk management plan including risk analysis, risk mitigation, verification and validation."

In summary, while the document confirms that performance testing was conducted for the INTDose device and asserts that it met design requirements and proved substantially equivalent to its predicate, it lacks the detailed quantitative data, sample sizes, ground truth establishment methods, and expert adjudication information typically found in a comprehensive study report. This level of detail is usually found in the full 510(k) submission, not the public summary or clearance letter.

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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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