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IGROK is a hardware/software system that provides the physician a means for comparison of medical imaging data from multiple DICOM conformant imaging modality sources. It allows the display, annotating, volume rendering, registration and fusing of medical images as an aid during use by diagnostic radiology, oncology, radiation therapy planning and other medical specialties. Certain registration functions and analyses are only applicable in pelvic, head and neck areas. IGROK is not intended for mammography diagnosis.

IGROK is a computer hardware and software system which is intended to facilitate Image-Guided Radiation Therapy (IGRT) by consolidating and organizing a wide array of data pertaining to a patient's course of external beam radiation therapy, and presenting this data, along with relevant analyses, so as to efficiently support typical IGRT review and decision-making tasks. The system functions as a radiation therapy-specific PACS, providing storage and visualization for DICOM diagnostic imaging, treatment plans, dose volumes, RT images, and structure set data. In addition, registration is provided between image volumes using both linear and non-linear techniques.

The provided 510(k) summary for the IGROK system does not contain the detailed study information needed to fill out all aspects of your request. This document is a premarket notification for substantial equivalence, which aims to demonstrate that a new device is as safe and effective as a legally marketed predicate device, rather than providing extensive de novo clinical performance studies with specific statistical acceptance criteria and detailed study designs.

Here's a breakdown of what can and cannot be extracted from the provided text:

Acceptance Criteria and Device Performance:

The document states: "The IGROK system will successfully complete verification testing prior to Beta validation. Software Beta testing/validation will be successfully completed prior to release." and "In summary, iGrok, LLC, is of the opinion that the IGROK system...performs as well as devices currently on the market, and concludes that the IGROK system is substantially equivalent to the predicate device."

This indicates that internal verification and validation were performed to confirm the system's functionality and that it performs comparably to existing devices. However, no specific numerical acceptance criteria or reported performance metrics (e.g., sensitivity, specificity, accuracy, dice score for registration, time savings) are provided.

Detailed Study Information:

1. Sample size used for the test set and the data provenance:

   • Not specified. The document mentions "Beta testing/validation" and "verification testing" but does not provide details on the number of cases or the nature of the test sets used.
   • Data Provenance: Not specified.

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

   • Not specified. The document does not describe a formal expert-adjudicated ground truth for testing.

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

   • Not specified. No mention of expert adjudication methods.

4. 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 evidence of an MRMC study. The document focuses on demonstrating substantial equivalence to a predicate device, not on quantifying human reader improvement with the system's assistance. The IGROK system is described as facilitating IGRT by consolidating and organizing data, and providing tools for display, annotation, volume rendering, registration, and fusing of images, implying it's an aid for physicians rather than a standalone AI diagnostic tool.

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

   • The primary function described is "providing the physician a means for comparison of medical imaging data" and acting "as an aid during use by diagnostic radiology, oncology, radiation therapy planning and other medical specialties." This strongly suggests a "human-in-the-loop" design. The document does not describe a standalone performance evaluation of the algorithms.

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

   • Not specified. Given the nature of a PACS-like system with registration capabilities, ground truth for image registration might involve known transformations or anatomical landmarks, but this is not detailed.

7. The sample size for the training set:

   • Not specified. The document mentions the software was "designed, developed, verified, and validated according to written procedures," but no details on training data are provided. This is typical for device types that are primarily tools for image processing and visualization, rather than AI models needing large training datasets.

8. How the ground truth for the training set was established:

   • Not applicable / Not specified. Without information on a training set or specific AI algorithms requiring ground truth for learning (beyond basic image processing algorithms), this cannot be addressed.

Summary of Limitations:

This 510(k) summary is a regulatory document focused on demonstrating substantial equivalence. It confirms internal verification and validation processes were followed and that the device is considered as safe and effective as a predicate device. However, it does not provide the kind of detailed performance study results (with specific metrics, sample sizes, ground truth establishment, and reader studies) that would typically be found in direct clinical performance studies for diagnostic or AI-powered devices aiming to establish novel performance claims.

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