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A pre-processing registration tool for use with other stereotactic surgical and neurosurgical treatment planning systems.

The above system is a pre-processing registration (fusion) software for CT, MR and PET scans. The software provides QA tools for the user to evaluate the fusion results. The results are used with other Integra Radionics applications. The software can be used on a HP UNIX or Linux workstation.

This submission does not contain information about acceptance criteria or a study that proves the device meets acceptance criteria. The provided documents are a 510(k) summary and the FDA's clearance letter for the Integra Radionics ImageFusion 3.

Here's a breakdown of what is and is not in the provided text:

• What's present:

  • Device Name: Integra Radionics ImageFusion 3
  • Predicate Device: Radionics ImageFusion 2, 510(k), K990071
  • Intended Use/Indications for Use: "A pre-processing registration tool for use with other stereotactic surgical and neurosurgical treatment planning systems." and "registering (fusing) stereotactic and non-stereotactic scans."
  • Technological Characteristics: Stated as "the same or similar to those found with the predicate devices."
  • Regulatory Information: 510(k) number (K063230), regulation number, product code, regulatory class.

• What's missing (information needed to answer the request):

  • A table of acceptance criteria and reported device performance.
  • Details of any study (sample size, data provenance, ground truth establishment, expert qualifications, adjudication methods).
  • Information on MRMC comparative effectiveness studies or standalone performance.
  • Training set details (sample size, ground truth establishment).

To answer your request thoroughly, a performance study report or verification and validation documentation would be needed, which is not included in this 510(k) summary. 510(k) summaries primarily focus on demonstrating substantial equivalence to a predicate device rather than detailing specific performance studies with acceptance criteria, especially for devices where clinical performance might be inferred from the predicate and technological similarity.

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A pre-processing registration tool for use with other stereotactic surgical and neurosurgical treatment planning systems.

ImageFusion 2.0, aids in identification of brain tumors prior to radiotherapy or stereotactic neurosurgical treatment planning. ImageFusion 2.0 has been enhanced to allow fusion of MR/MR images, in addition to CT/CT and CT/MR fusions that the previous version was capable of performing. The fusion process is based on the matching of bone or intensity and does not rely on matching of stereotactic rods or image slices. Therefore, a non-stereotactic MR or CT image can be re-sampled according to the stereotactic coordinates of the reference CT or MR image and further used in a stereotactic capacity.

ImageFusion 2.0 is image registration software for fusing a pair of 3D image sets. Both the reference and secondary image sets can be CT or MR images. The MR scans can be T1-weighted or non T1-weighted.

Here's an analysis of the provided text regarding the acceptance criteria and study for ImageFusion 2.0:

The provided 510(k) summary for ImageFusion 2.0 is quite succinct regarding the depth of performance testing. It focuses on verifying the accuracy of image registration and the functionality of key features.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state the sample size used for the test set or the data provenance (e.g., number of patient images, type of images, country of origin, retrospective/prospective). It only refers to "system testing" and "unit testing" without providing these details.

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

The document does not provide information on the number of experts used or their qualifications for establishing ground truth. The accuracy metrics (1.7 mm on average, 2.9 mm maximum) imply a quantitative measurement, but the method of establishing the "true" registration for comparison is not detailed.

4. Adjudication method for the test set

The document does not describe any adjudication method (e.g., 2+1, 3+1).

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

No, a multi-reader multi-case comparative effectiveness study was not conducted or reported. The device is a "pre-processing registration tool" and its performance is measured in terms of registration accuracy, not its impact on human reader performance.

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

Yes, the reported performance appears to be a standalone (algorithm only) assessment of the ImageFusion 2.0 system's ability to register images. The mentioned accuracy figures (1.7 mm on average, 2.9 mm maximum) are for the device's output itself.

7. The type of ground truth used

The document does not explicitly state the type of ground truth used. The accuracy metrics (mm) suggest that the ground truth for registration was likely established through a method that allows for quantitative comparison of spatial alignment, perhaps manual landmark identification by experts or a gold-standard registration method. It's not stated if it's based on pathology or outcomes data.

8. The sample size for the training set

The document does not provide any information about the sample size used for the training set.

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

The document does not provide any information on how the ground truth for the training set was established. Given the age of the submission (1999) and the nature of the device, it's possible that the "training" (development and refinement) of the algorithms for bone segmentation, intensity match, and landmark alignment was done using internal development data or established image processing techniques rather than a formal, large-scale supervised learning approach with explicitly defined ground truth for a training set.

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Image processing and comparing an MR and a CT image set or two different CT image sets.

The ImageFusion system, addressed in this premarket notification, has the same intended use and technological characteristics as the commercially available StereoPlan system. Like StereoPlan, the ImageFusion system includes an image processing work station used to evaluate, manipulate, and compare MR and CT image data. In addition, ImageFusion software can reconstruct (fuse) nonstereotactic MR images into the image space of a reference CT stereotactic image set for subsequent stereotactic use, eliminating the need for the localizing hardware required in StereoPlan to define stereotactic locations in MR images. Subsequently, fused images can be used in the treatment planning for stereotactic neurosurgery, radiosurgery and radiotherapy procedures in the same way that supplementary stereotactic MR or CT images are utilized in StereoPlan.

This document is a Summary of Safety and Effectiveness for a medical device called ImageFusion. It's a premarket notification (K960071) from 1996, which is quite old. As such, the level of detail regarding study design, ground truth establishment, and contemporary AI/ML evaluation metrics (like specific sensitivities, specificities, AUC) is not present. The language reflects the regulatory expectations of that era.

Here's an attempt to extract and infer the requested information based on the provided text, noting where information is explicitly stated, implied, or absent.

Acceptance Criteria and Device Performance

• Maximum: 2.5 mm for individual landmarks |
  | Bone Segmentation Accuracy | - Verified as "accurate" during system and unit testing. (No specific numerical metric provided) |
  | Landmark Alignment Accuracy | - Verified as "accurate" during system and unit testing. (No specific numerical metric provided) |

Note: The document specifies "registration of MR images in stereotactic CT space is accurate" and provides numerical values for this accuracy. For "bone segmentation and landmark alignment," it only states that these features are "accurate" based on system and unit testing, without providing quantitative metrics or specific acceptance criteria.

Study Information

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

• Test Set Sample Size: Not explicitly stated. The text mentions "system testing" and "unit testing" but does not specify the number of cases or landmarks used in these tests.
• Data Provenance: Not specified. It's an older submission, and such details (country of origin, retrospective/prospective nature) were often not explicitly required or documented in this section.

3. 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 the involvement of human experts in establishing ground truth for the "system testing" mentioned. It's possible the accuracy was determined by comparing the device's output to a known phantom or a previously established manual registration, but this is not detailed.

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

• Not specified. The description of testing is very high-level and does not include details on adjudication methods.

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, an MRMC comparative effectiveness study was not done. The device (ImageFusion) is described as an "image processing and comparing" system for MR and CT images used in treatment planning for stereotactic neurosurgery, radiosurgery, and radiotherapy. The focus here is on the accuracy of the image fusion itself, not on aiding human readers in diagnosis or interpretation compared to a baseline. The device's role is to merge images for subsequent use, not to make or assist in making a diagnostic interpretation typically associated with MRMC studies in AI.

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

• Yes, a standalone performance assessment was done. The described "system testing" verifies the registration accuracy of MR images into CT space (1.5 ± 0.6 mm on average, 2.5 mm maximum) and the accuracy of features like bone segmentation and landmark alignment. This assessment focuses purely on the algorithm's output (the fused accuracy) without explicitly describing a human-in-the-loop component for this specific accuracy verification. The device is used by humans, but the performance metrics provided are for the algorithmic output.

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

• Not explicitly stated, but likely based on precise measurements or phantoms. Given the nature of "registration accuracy" for "individual landmarks," the ground truth was most likely established through highly precise physical phantoms (where landmark positions are known) or possibly through meticulous manual registration performed by an expert against a known reference, followed by quantitative measurement of the difference. Pathology or outcomes data are not relevant for assessing image registration accuracy.

8. The sample size for the training set:

• Not applicable / Not specified. The document describes a traditional software system, not a machine learning or AI algorithm in the modern sense that typically requires a distinct "training set." While software development involves testing and iterative refinement, the concept of a separate "training set" for model learning is absent from this type of regulatory submission from 1996. The device's functionality is based on deterministic algorithms for image processing and registration.

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

• Not applicable. As a traditional software system, there isn't a "training set" in the machine learning context. Therefore, the establishment of ground truth for such a set is not relevant.

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