(28 days)
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
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Primary: Accurate registration of MR images in stereotactic CT space. | Average Accuracy: Approximately 1.7 mm for landmark registration. |
| Primary: Maximum accuracy for individual landmarks during MR image registration in stereotactic CT space. | Maximum Accuracy: Approximately 2.9 mm for individual landmarks. |
| Secondary (System & Unit Testing): Bone segmentation accuracy | Verified to be accurate through system and unit testing. |
| Secondary (System & Unit Testing): Intensity match accuracy | Verified to be accurate through system and unit testing. |
| Secondary (System & Unit Testing): Landmark alignment accuracy | Verified to be accurate through system and unit testing. |
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.
§ 892.5050 Medical charged-particle radiation therapy system.
(a)
Identification. A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.(b)
Classification. Class II. When intended for use as a quality control system, the film dosimetry system (film scanning system) included as an accessory to the device described in paragraph (a) of this section, is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.